2025-10-31 16:30:29.092098: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:30:29.103733: 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 00:00:1761924629.117690 1760217 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761924629.122018 1760217 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761924629.132525 1760217 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761924629.132552 1760217 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761924629.132555 1760217 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761924629.132557 1760217 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:30:29.135826: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-10-31 16:30:33,375	INFO worker.py:1927 -- Started a local Ray instance.
2025-10-31 16:30:34,088	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-10-31 16:30:34,158	INFO trial.py:182 -- Creating a new dirname dir_8adde_7f6d because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,162	INFO trial.py:182 -- Creating a new dirname dir_8adde_dcae because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,164	INFO trial.py:182 -- Creating a new dirname dir_8adde_943c because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,167	INFO trial.py:182 -- Creating a new dirname dir_8adde_c2d7 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,169	INFO trial.py:182 -- Creating a new dirname dir_8adde_833e because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,171	INFO trial.py:182 -- Creating a new dirname dir_8adde_3e9e because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,173	INFO trial.py:182 -- Creating a new dirname dir_8adde_c41b because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,175	INFO trial.py:182 -- Creating a new dirname dir_8adde_0770 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,177	INFO trial.py:182 -- Creating a new dirname dir_8adde_8f8f because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,182	INFO trial.py:182 -- Creating a new dirname dir_8adde_e9de because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,185	INFO trial.py:182 -- Creating a new dirname dir_8adde_3ee5 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,188	INFO trial.py:182 -- Creating a new dirname dir_8adde_9b8d because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,191	INFO trial.py:182 -- Creating a new dirname dir_8adde_dad0 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,194	INFO trial.py:182 -- Creating a new dirname dir_8adde_9ee1 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,198	INFO trial.py:182 -- Creating a new dirname dir_8adde_d184 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,201	INFO trial.py:182 -- Creating a new dirname dir_8adde_26b9 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,205	INFO trial.py:182 -- Creating a new dirname dir_8adde_9425 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,210	INFO trial.py:182 -- Creating a new dirname dir_8adde_b105 because trial dirname 'dir_8adde' already exists.
2025-10-31 16:30:34,216	INFO trial.py:182 -- Creating a new dirname dir_8adde_ca9c because trial dirname 'dir_8adde' 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_M/case_M_CAPTURE24_acc_gyr_17_classes/CAPTURE24_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-10-31_16-30-31_646831_1760217/artifacts/2025-10-31_16-30-34/CAPTURE24_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-10-31 16:30:34. 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_8adde    PENDING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    PENDING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    PENDING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    PENDING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    PENDING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    PENDING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    PENDING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    PENDING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    PENDING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    PENDING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    PENDING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    PENDING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    PENDING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    PENDING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    PENDING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    PENDING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    PENDING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    PENDING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    PENDING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    PENDING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            21 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00012 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            15 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            25 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            27 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00018 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            27 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00019 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            16 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            26 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            26 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            19 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            20 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            28 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00017 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            23 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            29 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            24 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            26 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            26 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            20 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_8adde started with configuration:
[36m(train_cnn_ray_tune pid=1761830)[0m 2025-10-31 16:30:37.406596: 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=1761830)[0m 2025-10-31 16:30:37.433328: 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=1761856)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=1761856)[0m E0000 00:00:1761924637.556883 1763014 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=1761856)[0m E0000 00:00:1761924637.565013 1763014 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=1761830)[0m W0000 00:00:1761924637.511766 1763000 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=1761830)[0m W0000 00:00:1761924637.511824 1763000 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=1761830)[0m W0000 00:00:1761924637.511827 1763000 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=1761830)[0m W0000 00:00:1761924637.511830 1763000 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=1761830)[0m 2025-10-31 16:30:37.519720: 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=1761830)[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=1761830)[0m 2025-10-31 16:30:40.769669: 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=1761830)[0m 2025-10-31 16:30:40.769719: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=1761830)[0m 2025-10-31 16:30:40.769728: 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=1761830)[0m 2025-10-31 16:30:40.769734: 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=1761830)[0m 2025-10-31 16:30:40.769740: 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=1761830)[0m 2025-10-31 16:30:40.769744: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=1761830)[0m 2025-10-31 16:30:40.769956: 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=1761830)[0m 2025-10-31 16:30:40.769987: 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=1761830)[0m 2025-10-31 16:30:40.769992: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_8adde config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            26 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761812)[0m Epoch 1/18
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 1/26[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=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m Epoch 2/20
[36m(train_cnn_ray_tune pid=1761852)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-31 16:31:04. 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m Epoch 2/18[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m Epoch 2/28[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 2/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 2/26[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m Epoch 2/27[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m Epoch 3/19
Trial status: 20 RUNNING
Current time: 2025-10-31 16:31:34. 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m Epoch 3/28
[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 85ms/step - accuracy: 0.1250 - loss: 2.5953
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m Epoch 4/20
[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 2/15
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:09[0m 112ms/step - accuracy: 0.0625 - loss: 3.0204
[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m Epoch 3/21[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m Epoch 3/20[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m32s[0m 55ms/step - accuracy: 0.0833 - loss: 3.2676 - val_accuracy: 0.1074 - val_loss: 2.8149
[36m(train_cnn_ray_tune pid=1761852)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 28ms/step - accuracy: 0.0688 - loss: 3.2400 - val_accuracy: 0.0649 - val_loss: 2.8069
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
[1m245/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.0826 - loss: 3.1945[32m [repeated 145x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m Epoch 5/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 3/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-31 16:32:04. 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 3/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m Epoch 4/18[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m Epoch 4/21[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 6/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m Epoch 5/28[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m353/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 44ms/step - accuracy: 0.1907 - loss: 2.5194 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m Epoch 7/20
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[1m 823/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.1367 - loss: 2.5441
[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m35s[0m 60ms/step - accuracy: 0.1357 - loss: 2.8213 - val_accuracy: 0.1638 - val_loss: 2.5162
[36m(train_cnn_ray_tune pid=1761870)[0m Epoch 4/18
[36m(train_cnn_ray_tune pid=1761852)[0m 
[1m 77/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 26ms/step - accuracy: 0.0893 - loss: 3.1172
[1m 79/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 26ms/step - accuracy: 0.0892 - loss: 3.1167
[1m 81/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 26ms/step - accuracy: 0.0892 - loss: 3.1163
[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:15[0m 131ms/step - accuracy: 0.1875 - loss: 2.7072
[1m  2/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34s[0m 59ms/step - accuracy: 0.1641 - loss: 2.7017  
[36m(train_cnn_ray_tune pid=1761812)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 94ms/step - accuracy: 0.1562 - loss: 3.0114
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1098/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 30ms/step - accuracy: 0.1147 - loss: 2.7696
[1m1100/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m1s[0m 30ms/step - accuracy: 0.1147 - loss: 2.7695[32m [repeated 102x across cluster][0m
[36m(train_cnn_ray_tune pid=1761818)[0m 
[1m302/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 36ms/step - accuracy: 0.1185 - loss: 2.9726 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m494/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 27ms/step - accuracy: 0.1032 - loss: 2.8152
[1m496/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 27ms/step - accuracy: 0.1032 - loss: 2.8151
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1089/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 44ms/step - accuracy: 0.0910 - loss: 3.2160[32m [repeated 128x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m53s[0m 46ms/step - accuracy: 0.1123 - loss: 2.9507 - val_accuracy: 0.1648 - val_loss: 2.5498
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m   3/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 42ms/step - accuracy: 0.0799 - loss: 2.9390  
[1m   5/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m45s[0m 39ms/step - accuracy: 0.0923 - loss: 2.9403
[36m(train_cnn_ray_tune pid=1761833)[0m 
[1m 538/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 24ms/step - accuracy: 0.0667 - loss: 3.2634
[1m 539/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 25ms/step - accuracy: 0.0667 - loss: 3.2634
[1m 541/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 25ms/step - accuracy: 0.0667 - loss: 3.2633
[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m 54/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 49ms/step - accuracy: 0.1505 - loss: 2.7531
[1m 55/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 49ms/step - accuracy: 0.1504 - loss: 2.7534
[1m 56/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 49ms/step - accuracy: 0.1503 - loss: 2.7537
Trial status: 20 RUNNING
Current time: 2025-10-31 16:32:34. 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 3/15[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m Epoch 5/21[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 4/23[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m Epoch 5/25[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m Epoch 4/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 3/26
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m Epoch 9/20
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 87ms/step - accuracy: 0.0625 - loss: 2.9719
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 945/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 28ms/step - accuracy: 0.1238 - loss: 2.6867
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 950/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 28ms/step - accuracy: 0.1239 - loss: 2.6867
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 960/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 27ms/step - accuracy: 0.1239 - loss: 2.6865
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 964/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 27ms/step - accuracy: 0.1239 - loss: 2.6865
[1m 966/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 27ms/step - accuracy: 0.1239 - loss: 2.6864
Trial status: 20 RUNNING
Current time: 2025-10-31 16:33:04. 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m Epoch 7/19[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 9/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m Epoch 6/25[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 5/23[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 4/15[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m54s[0m 47ms/step - accuracy: 0.1306 - loss: 2.7695 - val_accuracy: 0.1890 - val_loss: 2.4719
[36m(train_cnn_ray_tune pid=1761851)[0m Epoch 4/16[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-31 16:33:34. 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m Epoch 8/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 4/29[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 11/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 7/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m Epoch 9/28[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 4/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-31 16:34:04. 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m Epoch 8/21[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m Epoch 8/26
[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m Epoch 10/19[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 13/26[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:24[0m 125ms/step - accuracy: 0.1875 - loss: 3.2822
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m Epoch 5/24[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-31 16:34:34. 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 7/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 7/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m Epoch 11/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m Epoch 9/25[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 9/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m Epoch 9/20[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-31 16:35:04. Total running time: 4min 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:45[0m 92ms/step - accuracy: 0.3125 - loss: 2.1532
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[36m(train_cnn_ray_tune pid=1761854)[0m Epoch 7/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m Epoch 12/28[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m Epoch 10/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 8/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m Epoch 17/20[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m Epoch 13/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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Trial status: 20 RUNNING
Current time: 2025-10-31 16:35:34. Total running time: 5min 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_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16 │
│ trial_8adde    RUNNING            2   adam            relu                                   16                 32                  3                 1          0.000191302         27 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26 │
│ trial_8adde    RUNNING            3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24 │
│ trial_8adde    RUNNING            3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18 │
│ trial_8adde    RUNNING            3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28 │
│ trial_8adde    RUNNING            3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27 │
│ trial_8adde    RUNNING            2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26 │
│ trial_8adde    RUNNING            2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20 │
│ trial_8adde    RUNNING            2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 6/29[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 17/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 9/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 9/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 18/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 332ms/step
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m10/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m19/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m27/86[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m31/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m37/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m42/86[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m46/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1761833)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 78ms/step - accuracy: 0.0625 - loss: 2.8650
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m48/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 14ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 501/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m19s[0m 29ms/step - accuracy: 0.1488 - loss: 2.5033
[1m 503/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m19s[0m 29ms/step - accuracy: 0.1488 - loss: 2.5034
[1m 505/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m19s[0m 29ms/step - accuracy: 0.1488 - loss: 2.5034
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m62/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 14ms/step
[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m70/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m78/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m81/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1761852)[0m Epoch 19/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 42ms/step
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m  7/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step
[1m  9/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1009/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m7s[0m 51ms/step - accuracy: 0.1479 - loss: 2.8422[32m [repeated 49x across cluster][0m
[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m 11/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step
[1m 14/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1013/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m7s[0m 51ms/step - accuracy: 0.1479 - loss: 2.8421
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m 18/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m 27/158[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[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=1761869)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761833)[0m 2025-10-31 16:30:37.936363: 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=1761833)[0m 2025-10-31 16:30:37.959144: 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=1761833)[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=1761833)[0m E0000 00:00:1761924637.988289 1763134 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=1761833)[0m E0000 00:00:1761924637.996571 1763134 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=1761831)[0m W0000 00:00:1761924638.020916 1763136 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=1761831)[0m 2025-10-31 16:30:38.031904: 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=1761831)[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=1761833)[0m 2025-10-31 16:30:41.459193: 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=1761833)[0m 2025-10-31 16:30:41.459240: 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=1761833)[0m 2025-10-31 16:30:41.459249: 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=1761833)[0m 2025-10-31 16:30:41.459255: 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=1761833)[0m 2025-10-31 16:30:41.459259: 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=1761833)[0m 2025-10-31 16:30:41.459263: 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=1761833)[0m 2025-10-31 16:30:41.459582: 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=1761833)[0m 2025-10-31 16:30:41.459626: 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=1761833)[0m 2025-10-31 16:30:41.459632: 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=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
[1m129/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[1m143/158[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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[36m(train_cnn_ray_tune pid=1761869)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:36:03. Total running time: 5min 29s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             326.401 │
│ time_total_s                 326.401 │
│ training_iteration                 1 │
│ val_accuracy                  0.3373 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:36:03. Total running time: 5min 29s

Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-10-31 16:36:04. Total running time: 5min 30s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m Epoch 12/21[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 19/26
[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m Epoch 7/16[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 12/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m1086/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 27ms/step - accuracy: 0.1145 - loss: 2.8599
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[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 10/27
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:24[0m 73ms/step - accuracy: 0.1875 - loss: 2.1900
[36m(train_cnn_ray_tune pid=1761833)[0m Epoch 12/20
[36m(train_cnn_ray_tune pid=1761833)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:49[0m 95ms/step - accuracy: 0.0625 - loss: 3.1436
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 27ms/step - accuracy: 0.1465 - loss: 2.5697 - val_accuracy: 0.2210 - val_loss: 2.3954
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:52[0m 98ms/step - accuracy: 0.1250 - loss: 2.3543
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 274/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m33s[0m 37ms/step - accuracy: 0.1348 - loss: 2.8553[32m [repeated 143x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 300/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m21s[0m 25ms/step - accuracy: 0.1504 - loss: 2.5437
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m53s[0m 46ms/step - accuracy: 0.1305 - loss: 2.7585 - val_accuracy: 0.1596 - val_loss: 2.4916[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m  3/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 35ms/step - accuracy: 0.4236 - loss: 1.8459 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step - accuracy: 0.1145 - loss: 2.8593[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m1151/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 27ms/step - accuracy: 0.1145 - loss: 2.8593
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[36m(train_cnn_ray_tune pid=1761830)[0m Epoch 13/21[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-10-31 16:36:34. Total running time: 6min 0s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761852)[0m 
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[36m(train_cnn_ray_tune pid=1761852)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:36:35. Total running time: 6min 1s
[36m(train_cnn_ray_tune pid=1761852)[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=1761852)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             358.007 │
│ time_total_s                 358.007 │
│ training_iteration                 1 │
│ val_accuracy                 0.14155 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:36:35. Total running time: 6min 1s
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m Epoch 16/19[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m Epoch 13/25[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 91ms/step - accuracy: 0.0625 - loss: 2.5060
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 21/26
[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m27s[0m 47ms/step - accuracy: 0.1111 - loss: 2.9212 - val_accuracy: 0.1120 - val_loss: 2.7083
[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 13/26
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m 834/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m12s[0m 38ms/step - accuracy: 0.1623 - loss: 2.5266[32m [repeated 197x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 407/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m27s[0m 37ms/step - accuracy: 0.1512 - loss: 2.6109
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 100ms/step - accuracy: 0.0938 - loss: 2.9006
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 69/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 27ms/step - accuracy: 0.1414 - loss: 2.5519
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m 960/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m5s[0m 26ms/step - accuracy: 0.2323 - loss: 2.2845[32m [repeated 41x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
[1m1054/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 21ms/step - accuracy: 0.0838 - loss: 3.0369
[1m1057/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m2s[0m 21ms/step - accuracy: 0.0838 - loss: 3.0369[32m [repeated 99x across cluster][0m
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m202/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 26ms/step - accuracy: 0.1491 - loss: 2.5466 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m 895/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 38ms/step - accuracy: 0.1625 - loss: 2.5264 
[1m 896/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m9s[0m 38ms/step - accuracy: 0.1625 - loss: 2.5264
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 540/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m24s[0m 39ms/step - accuracy: 0.1253 - loss: 2.7508
[1m 542/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m24s[0m 39ms/step - accuracy: 0.1254 - loss: 2.7508
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[1m278/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m12s[0m 42ms/step - accuracy: 0.1398 - loss: 2.6929[32m [repeated 82x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m340/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 42ms/step - accuracy: 0.1905 - loss: 2.4832 
[1m342/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 42ms/step - accuracy: 0.1905 - loss: 2.4831
[36m(train_cnn_ray_tune pid=1761812)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 35ms/step - accuracy: 0.1276 - loss: 2.8282 - val_accuracy: 0.1684 - val_loss: 2.5071
[36m(train_cnn_ray_tune pid=1761812)[0m Epoch 17/18
[36m(train_cnn_ray_tune pid=1761812)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 82ms/step - accuracy: 0.1875 - loss: 2.8305
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m Epoch 17/19[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m Epoch 22/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-31 16:37:05. Total running time: 6min 30s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 11/23[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m Epoch 18/18[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 8/29[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m Epoch 15/21[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:03[0m 107ms/step - accuracy: 0.2500 - loss: 2.5023
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 12/26
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
[1m 396/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m16s[0m 22ms/step - accuracy: 0.0821 - loss: 2.9815
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[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m 844/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.1193 - loss: 2.7978
[1m 846/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.1193 - loss: 2.7978
[1m 848/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.1193 - loss: 2.7978
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 45ms/step - accuracy: 0.1978 - loss: 2.4664 - val_accuracy: 0.1755 - val_loss: 2.5108
[36m(train_cnn_ray_tune pid=1761853)[0m Epoch 15/26
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:00[0m 105ms/step - accuracy: 0.0938 - loss: 2.6591
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m  3/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 32ms/step - accuracy: 0.1458 - loss: 2.5368  
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m33s[0m 29ms/step - accuracy: 0.2454 - loss: 2.2542 - val_accuracy: 0.3073 - val_loss: 2.2090
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 79ms/step - accuracy: 0.3750 - loss: 2.3745
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-10-31 16:37:35. Total running time: 7min 1s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761812)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 18ms/step
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
[1m 41/158[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m Epoch 13/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[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=1761812)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761812)[0m 
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[36m(train_cnn_ray_tune pid=1761812)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:37:37. Total running time: 7min 3s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             420.599 │
│ time_total_s                 420.599 │
│ training_iteration                 1 │
│ val_accuracy                 0.17332 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:37:37. Total running time: 7min 3s
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 15/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m Epoch 11/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m Epoch 15/20[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m Epoch 16/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[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=1761818)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 13/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761818)[0m 
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[36m(train_cnn_ray_tune pid=1761818)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:38:02. Total running time: 7min 27s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              444.87 │
│ time_total_s                  444.87 │
│ training_iteration                 1 │
│ val_accuracy                 0.17232 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761818)[0m 
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:38:02. Total running time: 7min 27s
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 92/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 20ms/step - accuracy: 0.1497 - loss: 2.5127
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[1m 244/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m18s[0m 21ms/step - accuracy: 0.1647 - loss: 2.4419[32m [repeated 145x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:47[0m 93ms/step - accuracy: 0.0625 - loss: 3.2634
[1m   3/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 35ms/step - accuracy: 0.1354 - loss: 2.9668 

Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-10-31 16:38:05. Total running time: 7min 31s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m142/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m8s[0m 20ms/step - accuracy: 0.1498 - loss: 2.5124
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 9/15[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:32[0m 80ms/step - accuracy: 0.1250 - loss: 2.5632
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 100ms/step - accuracy: 0.2812 - loss: 2.0535
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m Epoch 17/21
[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 13/23
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:31[0m 80ms/step - accuracy: 0.0625 - loss: 2.7788
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[36m(train_cnn_ray_tune pid=1761854)[0m 
[1m1147/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 26ms/step - accuracy: 0.3717 - loss: 1.9187
[1m1149/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 26ms/step - accuracy: 0.3717 - loss: 1.9187
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 718/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.1687 - loss: 2.4371[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 721/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.1687 - loss: 2.4371
[1m 724/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.1687 - loss: 2.4371[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 25ms/step - accuracy: 0.1595 - loss: 2.5033 - val_accuracy: 0.2500 - val_loss: 2.3508
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 296ms/step
[1m 7/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step   
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m13/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[1m19/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m25/86[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m31/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m43/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m55/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 9ms/step
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m68/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step
[1m74/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m81/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761833)[0m Epoch 16/20
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 16/26
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 36ms/step
[1m  9/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step 
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 15/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step
[1m 23/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m55s[0m 96ms/step - accuracy: 0.2812 - loss: 2.2659
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 27/158[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 39/158[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[1m 46/158[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 52/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m257/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3498 - loss: 1.9667[32m [repeated 29x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m  3/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 43ms/step - accuracy: 0.2431 - loss: 2.3170
[1m  5/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 38ms/step - accuracy: 0.2265 - loss: 2.3470[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m 101/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 24ms/step - accuracy: 0.1320 - loss: 2.7029
[1m 104/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 24ms/step - accuracy: 0.1315 - loss: 2.7042
[1m 106/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 24ms/step - accuracy: 0.1313 - loss: 2.7051[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761854)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m34s[0m 29ms/step - accuracy: 0.3717 - loss: 1.9188 - val_accuracy: 0.3224 - val_loss: 2.1851
[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 69/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 81/158[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761843)[0m 
[1m 94/158[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[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=1761843)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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[36m(train_cnn_ray_tune pid=1761843)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:38:20. Total running time: 7min 46s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             463.011 │
│ time_total_s                 463.011 │
│ training_iteration                 1 │
│ val_accuracy                 0.24995 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:38:20. Total running time: 7min 46s
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m Epoch 17/25[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m286/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 34ms/step - accuracy: 0.1154 - loss: 2.8753 
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[36m(train_cnn_ray_tune pid=1761870)[0m Epoch 15/18
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 14/26
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[1m 975/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m3s[0m 18ms/step - accuracy: 0.0926 - loss: 2.9333[32m [repeated 117x across cluster][0m
[36m(train_cnn_ray_tune pid=1761854)[0m 
[1m 537/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m15s[0m 25ms/step - accuracy: 0.3850 - loss: 1.8897
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[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 413ms/step
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m 6/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step  
[1m10/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:18[0m 68ms/step - accuracy: 0.1250 - loss: 2.4358
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[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m14/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m18/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:29[0m 78ms/step - accuracy: 0.2500 - loss: 2.2728
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m36/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m44/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m30s[0m 26ms/step - accuracy: 0.2708 - loss: 2.1828 - val_accuracy: 0.3059 - val_loss: 2.1704
[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 14/27
[36m(train_cnn_ray_tune pid=1761830)[0m 
[1m51/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-10-31 16:38:35. Total running time: 8min 1s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[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=1761830)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m Epoch 10/16
[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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[36m(train_cnn_ray_tune pid=1761830)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:38:37. Total running time: 8min 3s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             480.473 │
│ time_total_s                 480.473 │
│ training_iteration                 1 │
│ val_accuracy                 0.29978 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:38:37. Total running time: 8min 3s
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m Epoch 18/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 10/15[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m Epoch 16/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 15/26
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m Epoch 18/20
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m Epoch 19/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m404/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m6s[0m 35ms/step - accuracy: 0.2440 - loss: 2.3138
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 473/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m13s[0m 20ms/step - accuracy: 0.1776 - loss: 2.4260
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Trial status: 14 RUNNING | 6 TERMINATED
Current time: 2025-10-31 16:39:05. 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_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 35ms/step - accuracy: 0.1662 - loss: 2.6074 - val_accuracy: 0.1568 - val_loss: 2.5885[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 76ms/step - accuracy: 0.2188 - loss: 2.6101[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 997/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m4s[0m 29ms/step - accuracy: 0.1420 - loss: 2.7505
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[36m(train_cnn_ray_tune pid=1761854)[0m 
[1m 723/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.4067 - loss: 1.8315 
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[36m(train_cnn_ray_tune pid=1761824)[0m Epoch 19/25
[36m(train_cnn_ray_tune pid=1761833)[0m 
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[1m 626/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m8s[0m 17ms/step - accuracy: 0.1005 - loss: 2.9122
[1m 629/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m8s[0m 17ms/step - accuracy: 0.1005 - loss: 2.9122
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 637/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1774 - loss: 2.4254
[1m 640/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1774 - loss: 2.4254
[1m 643/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m10s[0m 20ms/step - accuracy: 0.1774 - loss: 2.4254
[36m(train_cnn_ray_tune pid=1761850)[0m 
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m259/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.1308 - loss: 2.8113
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m269/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.1307 - loss: 2.8114 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m249/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.2214 - loss: 2.3631[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m189/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m12s[0m 33ms/step - accuracy: 0.1698 - loss: 2.5832
[1m191/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m12s[0m 33ms/step - accuracy: 0.1698 - loss: 2.5831[32m [repeated 75x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m287/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.1306 - loss: 2.8114[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 15/23
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.1775 - loss: 2.4244
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m Epoch 11/16[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m Epoch 19/20[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
[1m49/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 10ms/step
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
[1m82/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761854)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:39:23. Total running time: 8min 49s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             526.373 │
│ time_total_s                 526.373 │
│ training_iteration                 1 │
│ val_accuracy                 0.34544 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:39:23. Total running time: 8min 49s
[36m(train_cnn_ray_tune pid=1761854)[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=1761854)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 16/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:23[0m 72ms/step - accuracy: 0.3125 - loss: 2.6224
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m44s[0m 78ms/step - accuracy: 0.2500 - loss: 2.5956
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[1m458/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.1700 - loss: 2.5726[32m [repeated 80x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[1m 392/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m18s[0m 25ms/step - accuracy: 0.1455 - loss: 2.6283[32m [repeated 190x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 16/23[32m [repeated 3x across cluster][0m

Trial status: 13 RUNNING | 7 TERMINATED
Current time: 2025-10-31 16:39:35. 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_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 20/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 17/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 17/27[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m 5/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step  
[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m 9/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m12/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m20/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m24/86[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m24s[0m 21ms/step - accuracy: 0.3045 - loss: 2.0830 - val_accuracy: 0.3220 - val_loss: 2.1591
[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m28/86[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m36/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[1m40/86[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m32s[0m 28ms/step - accuracy: 0.1680 - loss: 2.5044 - val_accuracy: 0.2156 - val_loss: 2.3702
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:21[0m 70ms/step - accuracy: 0.1875 - loss: 2.2997
[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m44/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 15ms/step
[1m71/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m 283/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m22s[0m 26ms/step - accuracy: 0.2170 - loss: 2.4038[32m [repeated 59x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m524/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 28ms/step - accuracy: 0.1747 - loss: 2.5560
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 590/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m10s[0m 18ms/step - accuracy: 0.1843 - loss: 2.4003
[1m 593/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m10s[0m 18ms/step - accuracy: 0.1843 - loss: 2.4003[32m [repeated 176x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 12/15
[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m75/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 15ms/step
[1m79/86[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 597/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 18ms/step - accuracy: 0.1843 - loss: 2.4003 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m83/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m 247/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m16s[0m 18ms/step - accuracy: 0.3095 - loss: 2.0786
[1m 250/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m16s[0m 18ms/step - accuracy: 0.3095 - loss: 2.0784
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 19ms/step
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m  5/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m 14/158[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
[1m 31/158[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 21/26
[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[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=1761870)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761870)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
[1m32/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:39:58. Total running time: 9min 24s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             561.017 │
│ time_total_s                 561.017 │
│ training_iteration                 1 │
│ val_accuracy                 0.27417 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:39:58. Total running time: 9min 24s
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[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=1761833)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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[36m(train_cnn_ray_tune pid=1761833)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:39:59. Total running time: 9min 25s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             562.561 │
│ time_total_s                 562.561 │
│ training_iteration                 1 │
│ val_accuracy                 0.14949 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:39:59. Total running time: 9min 25s
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m34s[0m 29ms/step - accuracy: 0.1472 - loss: 2.6101 - val_accuracy: 0.2009 - val_loss: 2.4342[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:06[0m 58ms/step - accuracy: 0.1250 - loss: 2.5757[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m Epoch 21/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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Trial status: 11 RUNNING | 9 TERMINATED
Current time: 2025-10-31 16:40:05. Total running time: 9min 31s
Logical resource usage: 11.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m250/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2400 - loss: 2.3433[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 516/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.1743 - loss: 2.4734[32m [repeated 62x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m330/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m5s[0m 20ms/step - accuracy: 0.1709 - loss: 2.5439
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 527/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m12s[0m 20ms/step - accuracy: 0.1742 - loss: 2.4734
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 18ms/step - accuracy: 0.1858 - loss: 2.4012 - val_accuracy: 0.2454 - val_loss: 2.3144
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m57s[0m 50ms/step - accuracy: 0.1250 - loss: 2.4101
[1m   6/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 12ms/step - accuracy: 0.2139 - loss: 2.3313
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:21:43[0m 7s/step - accuracy: 0.0000e+00 - loss: 2.6302
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   4/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 24ms/step - accuracy: 0.0768 - loss: 2.6514      
[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 11/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 775/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m8s[0m 22ms/step - accuracy: 0.1619 - loss: 2.6969
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 706/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 22ms/step - accuracy: 0.1618 - loss: 2.6980 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m556/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step - accuracy: 0.1740 - loss: 2.5394
[1m559/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step - accuracy: 0.1740 - loss: 2.5394[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 322/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m12s[0m 15ms/step - accuracy: 0.1954 - loss: 2.3820
[1m 326/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m12s[0m 15ms/step - accuracy: 0.1954 - loss: 2.3821[32m [repeated 82x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m35s[0m 30ms/step - accuracy: 0.1831 - loss: 2.6302 - val_accuracy: 0.2422 - val_loss: 2.3530
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 62ms/step - accuracy: 0.1250 - loss: 2.7634
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m21s[0m 18ms/step - accuracy: 0.3150 - loss: 2.0569 - val_accuracy: 0.3286 - val_loss: 2.1422
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m   5/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 16ms/step - accuracy: 0.3313 - loss: 2.2826 
[1m   9/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 15ms/step - accuracy: 0.3493 - loss: 2.1733
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 25ms/step - accuracy: 0.1281 - loss: 2.7889 - val_accuracy: 0.1219 - val_loss: 2.6511
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 73ms/step - accuracy: 0.1562 - loss: 2.5355[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1002/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 22ms/step - accuracy: 0.1621 - loss: 2.6943[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 22/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1017/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m3s[0m 22ms/step - accuracy: 0.1621 - loss: 2.6942
[1m1020/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 22ms/step - accuracy: 0.1621 - loss: 2.6942[32m [repeated 132x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m 34/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 23ms/step - accuracy: 0.1581 - loss: 2.7245
[1m 37/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 23ms/step - accuracy: 0.1570 - loss: 2.7272[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m 45/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 23ms/step - accuracy: 0.1554 - loss: 2.7306[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m137/578[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.1480 - loss: 2.7354 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 69ms/step - accuracy: 0.2812 - loss: 2.3049
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 410/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m16s[0m 22ms/step - accuracy: 0.1486 - loss: 2.5857[32m [repeated 49x across cluster][0m
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m Epoch 13/16[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 13/15[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[1m568/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 23ms/step - accuracy: 0.1800 - loss: 2.5319[32m [repeated 75x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 198/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m20s[0m 21ms/step - accuracy: 0.1815 - loss: 2.4612
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 26ms/step - accuracy: 0.1387 - loss: 2.7525 - val_accuracy: 0.1177 - val_loss: 2.6477
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 66ms/step - accuracy: 0.0625 - loss: 2.8326
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 51ms/step - accuracy: 0.1875 - loss: 2.1022
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 17ms/step - accuracy: 0.1943 - loss: 2.3837 - val_accuracy: 0.2509 - val_loss: 2.2975
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1051/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 15ms/step - accuracy: 0.3239 - loss: 2.0489[32m [repeated 64x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 23/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[1m 945/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m4s[0m 22ms/step - accuracy: 0.1493 - loss: 2.5867[32m [repeated 86x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m 68/578[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 23ms/step - accuracy: 0.1295 - loss: 2.7739
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36s[0m 425ms/step
[1m 6/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step  
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m11/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m16/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m21/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m26/86[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m31/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m36/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m41/86[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m46/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m189/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m8s[0m 22ms/step - accuracy: 0.1328 - loss: 2.7674[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m198/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m8s[0m 22ms/step - accuracy: 0.1329 - loss: 2.7667
[1m201/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m8s[0m 22ms/step - accuracy: 0.1330 - loss: 2.7665[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 381/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m11s[0m 15ms/step - accuracy: 0.1954 - loss: 2.3704
[1m 385/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m11s[0m 15ms/step - accuracy: 0.1954 - loss: 2.3704[32m [repeated 128x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m51/86[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 11ms/step
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[36m(train_cnn_ray_tune pid=1761853)[0m 
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 11ms/step
[1m66/86[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 19/27[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761853)[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=1761853)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761853)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:40:33. Total running time: 9min 59s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              596.17 │
│ time_total_s                  596.17 │
│ training_iteration                 1 │
│ val_accuracy                 0.20131 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:40:33. Total running time: 9min 59s
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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Trial status: 10 RUNNING | 10 TERMINATED
Current time: 2025-10-31 16:40:35. Total running time: 10min 1s
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_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26        1            596.17          0.20131  │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 12/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[1m555/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 20ms/step - accuracy: 0.1892 - loss: 2.5053[32m [repeated 40x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1047/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 14ms/step - accuracy: 0.1979 - loss: 2.3697
[1m1051/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 14ms/step - accuracy: 0.1979 - loss: 2.3697
[1m1055/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 14ms/step - accuracy: 0.1979 - loss: 2.3697
[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m20s[0m 17ms/step - accuracy: 0.1425 - loss: 2.6560 - val_accuracy: 0.1944 - val_loss: 2.4632
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m Epoch 25/25[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 23ms/step - accuracy: 0.2221 - loss: 2.3450 - val_accuracy: 0.2791 - val_loss: 2.2775
[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:08[0m 59ms/step - accuracy: 0.2500 - loss: 2.7259
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   5/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 13ms/step - accuracy: 0.2540 - loss: 2.2761 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:19[0m 68ms/step - accuracy: 0.0000e+00 - loss: 2.6956[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 14/15[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m475/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.1917 - loss: 2.5009
[1m478/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.1917 - loss: 2.5008[32m [repeated 57x across cluster][0m
[36m(train_cnn_ray_tune pid=1761856)[0m 
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[1m 234/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m17s[0m 19ms/step - accuracy: 0.1895 - loss: 2.4621[32m [repeated 144x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 686/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 21ms/step - accuracy: 0.1950 - loss: 2.5747 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 62ms/step - accuracy: 0.4375 - loss: 1.8869[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 713/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 21ms/step - accuracy: 0.1951 - loss: 2.5739[32m [repeated 36x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 20/27
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[1m 683/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m6s[0m 14ms/step - accuracy: 0.2040 - loss: 2.3591[32m [repeated 107x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 920/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m4s[0m 19ms/step - accuracy: 0.1514 - loss: 2.5739
[1m 923/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m4s[0m 19ms/step - accuracy: 0.1514 - loss: 2.5739
[1m 926/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m4s[0m 19ms/step - accuracy: 0.1514 - loss: 2.5739
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 15ms/step - accuracy: 0.3310 - loss: 2.0096 - val_accuracy: 0.3192 - val_loss: 2.1594
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 23ms/step - accuracy: 0.1341 - loss: 2.7340 - val_accuracy: 0.1217 - val_loss: 2.6375
[36m(train_cnn_ray_tune pid=1761850)[0m Epoch 25/26
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m37s[0m 65ms/step - accuracy: 0.1250 - loss: 2.7726
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m  7/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 21ms/step - accuracy: 0.1214 - loss: 2.7838
[1m 10/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 20ms/step - accuracy: 0.1283 - loss: 2.7583
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[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=1761824)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m 53/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1070/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 13ms/step - accuracy: 0.2030 - loss: 2.3584
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[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m 65/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m 77/158[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m 89/158[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m101/158[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m107/158[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 9ms/step
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[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m119/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step
[1m124/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m130/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 9ms/step
[1m136/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m142/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step
[1m148/158[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m154/158[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step

Trial trial_8adde finished iteration 1 at 2025-10-31 16:40:58. Total running time: 10min 24s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             621.281 │
│ time_total_s                 621.281 │
│ training_iteration                 1 │
│ val_accuracy                 0.17292 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:40:58. Total running time: 10min 24s
[36m(train_cnn_ray_tune pid=1761824)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 23ms/step - accuracy: 0.1915 - loss: 2.5002 - val_accuracy: 0.1729 - val_loss: 2.5616
[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 20/23
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 55ms/step - accuracy: 0.1875 - loss: 2.6475
[1m   5/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 13ms/step - accuracy: 0.1762 - loss: 2.5148 
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 14/29
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 63ms/step - accuracy: 0.3750 - loss: 2.4339
[1m   4/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 17ms/step - accuracy: 0.2995 - loss: 2.5052 
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:02[0m 54ms/step - accuracy: 0.0000e+00 - loss: 2.4167
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[1m  11/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 11ms/step - accuracy: 0.1265 - loss: 2.3931
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m 365/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 13ms/step - accuracy: 0.1407 - loss: 2.6283 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[1m  62/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 11ms/step - accuracy: 0.1724 - loss: 2.3686
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m 350/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 13ms/step - accuracy: 0.1405 - loss: 2.6283[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m398/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.1359 - loss: 2.7289
[1m402/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.1358 - loss: 2.7290[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m  67/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 11ms/step - accuracy: 0.1740 - loss: 2.3682
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 15ms/step - accuracy: 0.2027 - loss: 2.3583 - val_accuracy: 0.2617 - val_loss: 2.2804[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761851)[0m 
[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m1030/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 18ms/step - accuracy: 0.2332 - loss: 2.3166
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 129/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 11ms/step - accuracy: 0.1873 - loss: 2.3571
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 889/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m4s[0m 18ms/step - accuracy: 0.1838 - loss: 2.4674[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 13/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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Trial status: 11 TERMINATED | 9 RUNNING
Current time: 2025-10-31 16:41:05. Total running time: 10min 31s
Logical resource usage: 9.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25        1            621.281         0.17292  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26        1            596.17          0.20131  │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m Epoch 15/15[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m22s[0m 19ms/step - accuracy: 0.1834 - loss: 2.4655 - val_accuracy: 0.2299 - val_loss: 2.3480[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m380/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 18ms/step - accuracy: 0.1378 - loss: 2.7266
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:04[0m 56ms/step - accuracy: 0.2500 - loss: 2.4084
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 729/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m7s[0m 16ms/step - accuracy: 0.1665 - loss: 2.5430[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m   6/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 12ms/step - accuracy: 0.1708 - loss: 2.6091 
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m 569/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m6s[0m 12ms/step - accuracy: 0.3432 - loss: 1.9525
[1m 573/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m6s[0m 12ms/step - accuracy: 0.3432 - loss: 1.9527[32m [repeated 121x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 245/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m16s[0m 18ms/step - accuracy: 0.1758 - loss: 2.6327
[1m 248/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m16s[0m 18ms/step - accuracy: 0.1757 - loss: 2.6329
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m480/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 18ms/step - accuracy: 0.1376 - loss: 2.7264
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 732/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m6s[0m 16ms/step - accuracy: 0.1665 - loss: 2.5430
[1m 735/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m6s[0m 16ms/step - accuracy: 0.1664 - loss: 2.5429
[1m 738/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m6s[0m 16ms/step - accuracy: 0.1664 - loss: 2.5429[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 21/23
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 618/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 19ms/step - accuracy: 0.1882 - loss: 2.5640 
[1m 621/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m9s[0m 19ms/step - accuracy: 0.1882 - loss: 2.5640
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m556/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step - accuracy: 0.1376 - loss: 2.7262
[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 22/26
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   6/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 12ms/step - accuracy: 0.1811 - loss: 2.4623 
[1m  11/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 12ms/step - accuracy: 0.1787 - loss: 2.4609
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m571/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step - accuracy: 0.1376 - loss: 2.7262
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 458/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m12s[0m 18ms/step - accuracy: 0.1721 - loss: 2.6358[32m [repeated 60x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m574/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step - accuracy: 0.1376 - loss: 2.7262
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step - accuracy: 0.1376 - loss: 2.7262[32m [repeated 39x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m  58/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 12ms/step - accuracy: 0.1918 - loss: 2.3931
[1m  63/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 12ms/step - accuracy: 0.1914 - loss: 2.3912[32m [repeated 168x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m15s[0m 13ms/step - accuracy: 0.2032 - loss: 2.3439 - val_accuracy: 0.2617 - val_loss: 2.2604[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 55ms/step - accuracy: 0.1875 - loss: 2.5686
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m 407/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 13ms/step - accuracy: 0.1492 - loss: 2.6176[32m [repeated 48x across cluster][0m
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 21ms/step - accuracy: 0.1376 - loss: 2.7262 - val_accuracy: 0.1215 - val_loss: 2.6330
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m 987/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m2s[0m 12ms/step - accuracy: 0.3423 - loss: 1.9565
[1m 992/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 12ms/step - accuracy: 0.3423 - loss: 1.9565[32m [repeated 114x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m 101/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 13ms/step - accuracy: 0.1568 - loss: 2.5968
[1m 105/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 13ms/step - accuracy: 0.1565 - loss: 2.5967
[1m 109/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 13ms/step - accuracy: 0.1562 - loss: 2.5968
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 386ms/step
[1m 7/86[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step   
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m13/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[1m19/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m25/86[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[1m32/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m39/86[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[1m46/86[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m52/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 8ms/step
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[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=1761850)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
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[36m(train_cnn_ray_tune pid=1761850)[0m 
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step

Trial trial_8adde finished iteration 1 at 2025-10-31 16:41:20. Total running time: 10min 46s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             643.157 │
│ time_total_s                 643.157 │
│ training_iteration                 1 │
│ val_accuracy                  0.1215 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:41:20. Total running time: 10min 46s
[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 22/27
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 55ms/step - accuracy: 0.5625 - loss: 1.5172
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 14ms/step - accuracy: 0.3422 - loss: 1.9568 - val_accuracy: 0.3304 - val_loss: 2.1607
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m22s[0m 19ms/step - accuracy: 0.1653 - loss: 2.5420 - val_accuracy: 0.2202 - val_loss: 2.4063
[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 734/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m4s[0m 11ms/step - accuracy: 0.2103 - loss: 2.3473
[1m 739/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m4s[0m 11ms/step - accuracy: 0.2104 - loss: 2.3473
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[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 15/29
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:12[0m 62ms/step - accuracy: 0.1250 - loss: 2.4741
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   4/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 17ms/step - accuracy: 0.1628 - loss: 2.4715 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m Epoch 16/16
[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 48ms/step - accuracy: 0.2500 - loss: 1.9471
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m23s[0m 20ms/step - accuracy: 0.1903 - loss: 2.5590 - val_accuracy: 0.2545 - val_loss: 2.3108[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:01[0m 53ms/step - accuracy: 0.1875 - loss: 2.4076
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1151/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 10ms/step - accuracy: 0.2103 - loss: 2.3449[32m [repeated 53x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 430/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 14ms/step - accuracy: 0.1561 - loss: 2.5199 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 9/86[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step   
[1m16/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m23/86[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[1m31/86[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m38/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m54/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m69/86[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m85/86[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 16/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 32/158[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 40/158[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 54/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 70/158[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
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[36m(train_cnn_ray_tune pid=1761856)[0m 
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m 99/158[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 7ms/step
[1m107/158[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m114/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m122/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 7ms/step
[1m129/158[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m137/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 7ms/step
[1m144/158[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[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=1761856)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m 389/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m7s[0m 10ms/step - accuracy: 0.1508 - loss: 2.6174
[1m 394/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m7s[0m 10ms/step - accuracy: 0.1508 - loss: 2.6174
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[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m152/158[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=1761856)[0m 
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step

Trial trial_8adde finished iteration 1 at 2025-10-31 16:41:31. Total running time: 10min 57s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             654.328 │
│ time_total_s                 654.328 │
│ training_iteration                 1 │
│ val_accuracy                 0.23585 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:41:31. Total running time: 10min 57s
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 299/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m13s[0m 15ms/step - accuracy: 0.2080 - loss: 2.5178[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 190/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m14s[0m 15ms/step - accuracy: 0.1763 - loss: 2.6257
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 23/26[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:06[0m 58ms/step - accuracy: 0.0625 - loss: 2.6868
[1m   5/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 14ms/step - accuracy: 0.1181 - loss: 2.7242 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 12ms/step - accuracy: 0.2103 - loss: 2.3449 - val_accuracy: 0.2676 - val_loss: 2.2533[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 52ms/step - accuracy: 0.1250 - loss: 2.1319
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 751/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m5s[0m 14ms/step - accuracy: 0.1560 - loss: 2.5300[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1128/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 10ms/step - accuracy: 0.3527 - loss: 1.9300
[1m1133/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 10ms/step - accuracy: 0.3527 - loss: 1.9300[32m [repeated 122x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m 148/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 10ms/step - accuracy: 0.1514 - loss: 2.6236 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m59s[0m 52ms/step - accuracy: 0.3750 - loss: 1.8845
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m  18/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 9ms/step - accuracy: 0.3712 - loss: 1.8214 
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Trial status: 13 TERMINATED | 7 RUNNING
Current time: 2025-10-31 16:41:35. Total running time: 11min 1s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16                                              │
│ trial_8adde    RUNNING              2   adam            relu                                   16                 32                  3                 1          0.000191302         27                                              │
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25        1            621.281         0.17292  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15        1            654.328         0.235855 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26        1            596.17          0.20131  │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26        1            643.157         0.121501 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 364/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m7s[0m 10ms/step - accuracy: 0.2064 - loss: 2.3231
[1m 369/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m7s[0m 10ms/step - accuracy: 0.2065 - loss: 2.3230
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m 124/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 10ms/step - accuracy: 0.3679 - loss: 1.8863
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[36m(train_cnn_ray_tune pid=1761855)[0m Epoch 23/27
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 12ms/step - accuracy: 0.3527 - loss: 1.9302 - val_accuracy: 0.3228 - val_loss: 2.1908
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 680/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m7s[0m 15ms/step - accuracy: 0.2090 - loss: 2.5168[32m [repeated 41x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m 
[1m 307/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m8s[0m 10ms/step - accuracy: 0.3652 - loss: 1.9017
[1m 313/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m8s[0m 10ms/step - accuracy: 0.3651 - loss: 1.9020[32m [repeated 231x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1049/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 9ms/step - accuracy: 0.2133 - loss: 2.3188 
[1m1056/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step - accuracy: 0.2134 - loss: 2.3188
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1063/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step - accuracy: 0.2134 - loss: 2.3188
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1076/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step - accuracy: 0.2134 - loss: 2.3188
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m Epoch 23/23
[36m(train_cnn_ray_tune pid=1761849)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m54s[0m 47ms/step - accuracy: 0.0625 - loss: 2.4692
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 11ms/step - accuracy: 0.2137 - loss: 2.3187 - val_accuracy: 0.2696 - val_loss: 2.2389
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03[0m 55ms/step - accuracy: 0.1250 - loss: 2.6514
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 335ms/step
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[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=1761851)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 24/26[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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[36m(train_cnn_ray_tune pid=1761851)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:41:46. Total running time: 11min 12s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             669.148 │
│ time_total_s                 669.148 │
│ training_iteration                 1 │
│ val_accuracy                 0.25392 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:41:46. Total running time: 11min 12s
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[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=1761855)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761842)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:41:48. Total running time: 11min 14s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             671.324 │
│ time_total_s                 671.324 │
│ training_iteration                 1 │
│ val_accuracy                 0.33095 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:41:48. Total running time: 11min 14s
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 15ms/step - accuracy: 0.1760 - loss: 2.6106 - val_accuracy: 0.2394 - val_loss: 2.3543[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m Epoch 17/24[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761855)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[1m 16/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step 

Trial trial_8adde finished iteration 1 at 2025-10-31 16:41:52. Total running time: 11min 18s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             675.393 │
│ time_total_s                 675.393 │
│ training_iteration                 1 │
│ val_accuracy                 0.21104 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:41:52. Total running time: 11min 18s
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 17/29[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761849)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m Epoch 26/26[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761831)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 45ms/step - accuracy: 0.1250 - loss: 2.5010
[1m   7/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 9ms/step - accuracy: 0.1346 - loss: 2.4483 
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 18/29
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 959/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2313 - loss: 2.2890
[1m 967/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2313 - loss: 2.2889
[1m 975/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.2312 - loss: 2.2889

Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-10-31 16:42:05. 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_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    RUNNING              2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26                                              │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25        1            621.281         0.17292  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16        1            669.148         0.253921 │
│ trial_8adde    TERMINATED           2   adam            relu                                   16                 32                  3                 1          0.000191302         27        1            671.324         0.330951 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15        1            654.328         0.235855 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26        1            596.17          0.20131  │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26        1            643.157         0.121501 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23        1            675.393         0.211038 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 283ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m18/86[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step   
[1m37/86[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m  25/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 9ms/step - accuracy: 0.1456 - loss: 2.5124
[1m  31/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 9ms/step - accuracy: 0.1482 - loss: 2.5165 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[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=1761831)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step
[1m75/86[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
[36m(train_cnn_ray_tune pid=1761858)[0m Epoch 19/24
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 36ms/step
[1m 19/158[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step 
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 37/158[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step
[1m 56/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m 73/158[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 3ms/step
[1m 91/158[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m110/158[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step
[1m136/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=1761831)[0m 
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step

Trial trial_8adde finished iteration 1 at 2025-10-31 16:42:09. Total running time: 11min 35s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             691.892 │
│ time_total_s                 691.892 │
│ training_iteration                 1 │
│ val_accuracy                 0.28152 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:42:09. Total running time: 11min 35s
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 10ms/step - accuracy: 0.2184 - loss: 2.4596 - val_accuracy: 0.2571 - val_loss: 2.2827[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 37ms/step - accuracy: 0.1875 - loss: 2.3249
[1m   7/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 8ms/step - accuracy: 0.1946 - loss: 2.4875  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 17/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 454/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.2320 - loss: 2.4392
[1m 460/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.2319 - loss: 2.4393
[1m 466/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.2318 - loss: 2.4393
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 513/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 8ms/step - accuracy: 0.2308 - loss: 2.4398
[1m 520/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m5s[0m 8ms/step - accuracy: 0.2306 - loss: 2.4399[32m [repeated 112x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 860/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.1826 - loss: 2.5637[32m [repeated 36x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 9ms/step - accuracy: 0.1726 - loss: 2.4771 - val_accuracy: 0.2398 - val_loss: 2.3666
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 19/29
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 42ms/step - accuracy: 0.1875 - loss: 2.3647
[1m   8/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 8ms/step - accuracy: 0.2051 - loss: 2.4039  
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1131/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 8ms/step - accuracy: 0.2255 - loss: 2.4421
[1m1137/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 8ms/step - accuracy: 0.2255 - loss: 2.4421[32m [repeated 107x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step - accuracy: 0.2254 - loss: 2.4422[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 9ms/step - accuracy: 0.1831 - loss: 2.5606 - val_accuracy: 0.2416 - val_loss: 2.3322
[36m(train_cnn_ray_tune pid=1761858)[0m Epoch 20/24
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 44ms/step - accuracy: 0.1875 - loss: 2.4383
[1m   8/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 8ms/step - accuracy: 0.1591 - loss: 2.6148  
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 9ms/step - accuracy: 0.2254 - loss: 2.4422 - val_accuracy: 0.2631 - val_loss: 2.2754
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 18/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m44s[0m 39ms/step - accuracy: 0.1875 - loss: 2.2286
[1m   7/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 9ms/step - accuracy: 0.2337 - loss: 2.2529  
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 213/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m8s[0m 9ms/step - accuracy: 0.2193 - loss: 2.4312
[1m 219/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m8s[0m 9ms/step - accuracy: 0.2192 - loss: 2.4322
[1m 225/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m7s[0m 9ms/step - accuracy: 0.2191 - loss: 2.4331
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 432/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m6s[0m 9ms/step - accuracy: 0.2190 - loss: 2.4448
[1m 439/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m6s[0m 8ms/step - accuracy: 0.2191 - loss: 2.4449[32m [repeated 111x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 694/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 7ms/step - accuracy: 0.1952 - loss: 2.5296[32m [repeated 31x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 9ms/step - accuracy: 0.1863 - loss: 2.4612 - val_accuracy: 0.2366 - val_loss: 2.3619
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 20/29
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m50s[0m 44ms/step - accuracy: 0.1250 - loss: 2.5736
[1m   8/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 8ms/step - accuracy: 0.1220 - loss: 2.5905  
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 8ms/step - accuracy: 0.1933 - loss: 2.5320 - val_accuracy: 0.2430 - val_loss: 2.3248
[36m(train_cnn_ray_tune pid=1761858)[0m Epoch 21/24
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 45ms/step - accuracy: 0.0000e+00 - loss: 2.9853
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m   8/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 8ms/step - accuracy: 0.1482 - loss: 2.7476      
[1m  15/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 8ms/step - accuracy: 0.1622 - loss: 2.6784
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1055/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 8ms/step - accuracy: 0.2215 - loss: 2.4436
[1m1062/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 8ms/step - accuracy: 0.2215 - loss: 2.4435[32m [repeated 101x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 497/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 8ms/step - accuracy: 0.1673 - loss: 2.4891[32m [repeated 29x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m43s[0m 38ms/step - accuracy: 0.1875 - loss: 2.7159
[1m   7/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 9ms/step - accuracy: 0.1922 - loss: 2.4397  
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 9ms/step - accuracy: 0.2217 - loss: 2.4429 - val_accuracy: 0.2658 - val_loss: 2.2596
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 19/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 362/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m6s[0m 8ms/step - accuracy: 0.2302 - loss: 2.4028
[1m 369/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m6s[0m 8ms/step - accuracy: 0.2302 - loss: 2.4027[32m [repeated 109x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 905/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.1915 - loss: 2.5178[32m [repeated 43x across cluster][0m

Trial status: 17 TERMINATED | 3 RUNNING
Current time: 2025-10-31 16:42:35. Total running time: 12min 1s
Logical resource usage: 3.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25        1            621.281         0.17292  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16        1            669.148         0.253921 │
│ trial_8adde    TERMINATED           2   adam            relu                                   16                 32                  3                 1          0.000191302         27        1            671.324         0.330951 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15        1            654.328         0.235855 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26        1            596.17          0.20131  │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26        1            643.157         0.121501 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23        1            675.393         0.211038 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26        1            691.892         0.281517 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 9ms/step - accuracy: 0.1747 - loss: 2.4748 - val_accuracy: 0.2341 - val_loss: 2.3575
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 21/29
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m49s[0m 43ms/step - accuracy: 0.3125 - loss: 2.3589
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 301/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m6s[0m 8ms/step - accuracy: 0.1903 - loss: 2.5247[32m [repeated 36x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 8ms/step - accuracy: 0.1914 - loss: 2.5188 - val_accuracy: 0.2440 - val_loss: 2.3169
[36m(train_cnn_ray_tune pid=1761858)[0m Epoch 22/24
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 45ms/step - accuracy: 0.1875 - loss: 2.8720
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 9ms/step - accuracy: 0.2296 - loss: 2.4029 - val_accuracy: 0.2636 - val_loss: 2.2511
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 20/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 37ms/step - accuracy: 0.1250 - loss: 2.9012
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   7/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 9ms/step - accuracy: 0.2555 - loss: 2.4529  
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 290/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m7s[0m 8ms/step - accuracy: 0.2265 - loss: 2.4163[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 42ms/step - accuracy: 0.0625 - loss: 2.7828
[1m   8/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 8ms/step - accuracy: 0.1369 - loss: 2.5394  
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 9ms/step - accuracy: 0.1823 - loss: 2.4586 - val_accuracy: 0.2426 - val_loss: 2.3467
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 22/29
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 8ms/step - accuracy: 0.1938 - loss: 2.5118 - val_accuracy: 0.2412 - val_loss: 2.3178
[36m(train_cnn_ray_tune pid=1761858)[0m Epoch 23/24
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 784/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 8ms/step - accuracy: 0.2289 - loss: 2.4054
[1m 790/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.2290 - loss: 2.4053
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 615/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 8ms/step - accuracy: 0.1826 - loss: 2.4225[32m [repeated 43x across cluster][0m
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 45ms/step - accuracy: 0.0625 - loss: 2.6800
[1m   8/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 8ms/step - accuracy: 0.2097 - loss: 2.5407  
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 9ms/step - accuracy: 0.2302 - loss: 2.4011 - val_accuracy: 0.2635 - val_loss: 2.2470
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 21/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 36ms/step - accuracy: 0.0625 - loss: 2.8957
[1m   7/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 8ms/step - accuracy: 0.1093 - loss: 2.8560  
[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m Epoch 24/24[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 9ms/step - accuracy: 0.2301 - loss: 2.4118 - val_accuracy: 0.2644 - val_loss: 2.2441
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 22/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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Trial status: 17 TERMINATED | 3 RUNNING
Current time: 2025-10-31 16:43:05. Total running time: 12min 31s
Logical resource usage: 3.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25        1            621.281         0.17292  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16        1            669.148         0.253921 │
│ trial_8adde    TERMINATED           2   adam            relu                                   16                 32                  3                 1          0.000191302         27        1            671.324         0.330951 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15        1            654.328         0.235855 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26        1            596.17          0.20131  │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26        1            643.157         0.121501 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23        1            675.393         0.211038 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26        1            691.892         0.281517 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 135/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 8ms/step - accuracy: 0.2153 - loss: 2.3956[32m [repeated 20x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 42ms/step - accuracy: 0.2500 - loss: 2.3311
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 357ms/step
[1m16/86[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step   
[36m(train_cnn_ray_tune pid=1761858)[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=1761858)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m61/86[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 8ms/step - accuracy: 0.2015 - loss: 2.4901 - val_accuracy: 0.2440 - val_loss: 2.3022[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 24/29
[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
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[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m136/158[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step
[1m151/158[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=1761858)[0m 
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 4ms/step

Trial trial_8adde finished iteration 1 at 2025-10-31 16:43:09. Total running time: 12min 35s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             752.297 │
│ time_total_s                 752.297 │
│ training_iteration                 1 │
│ val_accuracy                 0.24399 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:43:09. Total running time: 12min 35s
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 801/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.2302 - loss: 2.3727
[1m 808/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.2302 - loss: 2.3726[32m [repeated 94x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 838/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.2305 - loss: 2.3722[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 23/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36s[0m 32ms/step - accuracy: 0.2500 - loss: 2.2241
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   9/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 7ms/step - accuracy: 0.2406 - loss: 2.3196  
[1m  17/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 7ms/step - accuracy: 0.2259 - loss: 2.3427
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 8ms/step - accuracy: 0.2332 - loss: 2.3679 - val_accuracy: 0.2670 - val_loss: 2.2403
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 8ms/step - accuracy: 0.2063 - loss: 2.3886 - val_accuracy: 0.2480 - val_loss: 2.3193
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 37ms/step - accuracy: 0.1875 - loss: 2.2190
[1m   9/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 7ms/step - accuracy: 0.2121 - loss: 2.3540  
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 236/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 7ms/step - accuracy: 0.2280 - loss: 2.3860
[1m 243/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 7ms/step - accuracy: 0.2282 - loss: 2.3853[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 154/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 6ms/step - accuracy: 0.2247 - loss: 2.3919[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 25/29
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 939/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.2357 - loss: 2.3658
[1m 946/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.2357 - loss: 2.3657[32m [repeated 88x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 977/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.2359 - loss: 2.3655[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 8ms/step - accuracy: 0.2370 - loss: 2.3635 - val_accuracy: 0.2726 - val_loss: 2.2369
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 24/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 29ms/step - accuracy: 0.1875 - loss: 2.2708
[1m   9/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 6ms/step - accuracy: 0.2169 - loss: 2.3858  
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m41s[0m 36ms/step - accuracy: 0.1250 - loss: 2.5896
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   9/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 7ms/step - accuracy: 0.1529 - loss: 2.5443  
[1m  17/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 7ms/step - accuracy: 0.1552 - loss: 2.4994
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 367/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 7ms/step - accuracy: 0.2422 - loss: 2.3414
[1m 374/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 7ms/step - accuracy: 0.2422 - loss: 2.3411[32m [repeated 73x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 361/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 7ms/step - accuracy: 0.2032 - loss: 2.3877[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 7ms/step - accuracy: 0.1959 - loss: 2.3968 - val_accuracy: 0.2521 - val_loss: 2.3154
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 26/29
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1076/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 7ms/step - accuracy: 0.2463 - loss: 2.3365
[1m1083/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 7ms/step - accuracy: 0.2464 - loss: 2.3365[32m [repeated 70x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1149/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step - accuracy: 0.2466 - loss: 2.3364[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 8ms/step - accuracy: 0.2467 - loss: 2.3364 - val_accuracy: 0.2694 - val_loss: 2.2305
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 25/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 29ms/step - accuracy: 0.2500 - loss: 2.4718
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m   9/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 6ms/step - accuracy: 0.2691 - loss: 2.4315  
[1m  17/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 6ms/step - accuracy: 0.2549 - loss: 2.4016
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m44s[0m 38ms/step - accuracy: 0.1250 - loss: 2.4845
[1m   9/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 7ms/step - accuracy: 0.1874 - loss: 2.3793  
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 503/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 7ms/step - accuracy: 0.2505 - loss: 2.3264
[1m 510/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 7ms/step - accuracy: 0.2505 - loss: 2.3264[32m [repeated 72x across cluster][0m

Trial status: 18 TERMINATED | 2 RUNNING
Current time: 2025-10-31 16:43:35. Total running time: 13min 1s
Logical resource usage: 2.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_8adde    RUNNING              3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26                                              │
│ trial_8adde    RUNNING              3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29                                              │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25        1            621.281         0.17292  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16        1            669.148         0.253921 │
│ trial_8adde    TERMINATED           2   adam            relu                                   16                 32                  3                 1          0.000191302         27        1            671.324         0.330951 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15        1            654.328         0.235855 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26        1            596.17          0.20131  │
│ trial_8adde    TERMINATED           3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24        1            752.297         0.243994 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26        1            643.157         0.121501 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23        1            675.393         0.211038 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26        1            691.892         0.281517 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m 567/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m4s[0m 7ms/step - accuracy: 0.2104 - loss: 2.3598[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 7ms/step - accuracy: 0.2050 - loss: 2.3819 - val_accuracy: 0.2513 - val_loss: 2.3096
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 27/29
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 28/29
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 8ms/step - accuracy: 0.2523 - loss: 2.3228 - val_accuracy: 0.2736 - val_loss: 2.2283
[36m(train_cnn_ray_tune pid=1761857)[0m Epoch 26/26
[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 8ms/step - accuracy: 0.2211 - loss: 2.3453 - val_accuracy: 0.2452 - val_loss: 2.2946
[36m(train_cnn_ray_tune pid=1761842)[0m Epoch 29/29
[36m(train_cnn_ray_tune pid=1761842)[0m 
[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 31ms/step - accuracy: 0.2500 - loss: 2.2327
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m 39/158[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step
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[36m(train_cnn_ray_tune pid=1761857)[0m 
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.2554 - loss: 2.3041[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m117/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 3ms/step
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[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m155/158[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 3ms/step
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:43:51. Total running time: 13min 17s
[36m(train_cnn_ray_tune pid=1761857)[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=1761857)[0m   _log_deprecation_warning(
2025-10-31 16:43:56,283	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_CAPTURE24_acc_gyr_17_classes/CAPTURE24_hyperparameters_tuning' in 0.0066s.
I0000 00:00:1761925436.411465 1760217 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             794.527 │
│ time_total_s                 794.527 │
│ training_iteration                 1 │
│ val_accuracy                 0.27596 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:43:51. Total running time: 13min 17s
[36m(train_cnn_ray_tune pid=1761857)[0m 
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 8ms/step - accuracy: 0.2554 - loss: 2.3041 - val_accuracy: 0.2760 - val_loss: 2.2235
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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Trial trial_8adde finished iteration 1 at 2025-10-31 16:43:56. Total running time: 13min 22s
╭──────────────────────────────────────╮
│ Trial trial_8adde result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             798.993 │
│ time_total_s                 798.993 │
│ training_iteration                 1 │
│ val_accuracy                  0.2696 │
╰──────────────────────────────────────╯

Trial trial_8adde completed after 1 iterations at 2025-10-31 16:43:56. Total running time: 13min 22s

Trial status: 20 TERMINATED
Current time: 2025-10-31 16:43:56. Total running time: 13min 22s
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_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 0          2.01724e-05         25        1            621.281         0.17292  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 1          8.45939e-06         19        1            444.87          0.172325 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          0.000116912         21        1            480.473         0.299782 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  3                 0          6.46272e-06         18        1            420.599         0.173317 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  5                 0          5.04719e-05         16        1            669.148         0.253921 │
│ trial_8adde    TERMINATED           2   adam            relu                                   16                 32                  3                 1          0.000191302         27        1            671.324         0.330951 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   16                 32                  3                 0          3.60718e-05         15        1            654.328         0.235855 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   16                 64                  3                 0          9.50117e-06         26        1            794.527         0.275958 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          4.76818e-05         26        1            596.17          0.20131  │
│ trial_8adde    TERMINATED           3   adam            tanh                                   16                 32                  3                 1          1.25126e-05         24        1            752.297         0.243994 │
│ trial_8adde    TERMINATED           3   rmsprop         relu                                   32                 64                  3                 1          2.90392e-05         18        1            561.017         0.274171 │
│ trial_8adde    TERMINATED           3   rmsprop         tanh                                   32                 32                  5                 1          8.99448e-06         26        1            643.157         0.121501 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 32                  3                 1          1.29046e-05         23        1            675.393         0.211038 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          0.000168011         28        1            326.401         0.337304 │
│ trial_8adde    TERMINATED           3   adam            relu                                   16                 32                  5                 1          2.12002e-05         29        1            798.993         0.269605 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 64                  3                 1          0.000181453         27        1            526.373         0.345444 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   32                 16                  3                 1          4.41563e-05         26        1            463.011         0.24995  │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   16                 32                  3                 1          6.53064e-05         26        1            691.892         0.281517 │
│ trial_8adde    TERMINATED           2   adam            tanh                                   16                 16                  3                 0          6.68878e-06         20        1            562.561         0.149494 │
│ trial_8adde    TERMINATED           2   rmsprop         relu                                   32                 16                  3                 0          6.21016e-06         20        1            358.007         0.141553 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'rmsprop', 'funcion_activacion': 'relu', 'tamanho_minilote': 16, 'numero_filtros': 64, 'tamanho_filtro': 3, 'num_resblocks': 1, 'tasa_aprendizaje': 0.00018145252732369767, 'epochs': 27}
Epoch 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925438.538026 1810492 service.cc:152] XLA service 0x78bbe00101f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925438.538084 1810492 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:43:58.577368: 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 00:00:1761925438.807892 1810492 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925440.730033 1810492 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 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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[1m 842/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2165 - loss: 2.3438
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[1m1109/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2159 - loss: 2.3443
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Epoch 6/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 22ms/step - accuracy: 0.3125 - loss: 2.2692
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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

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[1m1079/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3337 - loss: 2.0182
[1m1114/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3337 - loss: 2.0181
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Epoch 11/27

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[1m1017/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3494 - loss: 1.9660
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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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Epoch 15/27

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Saved model to disk.
[36m(train_cnn_ray_tune pid=1761842)[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=1761842)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=1761842)[0m 
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[36m(train_cnn_ray_tune pid=1761842)[0m 
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2025-10-31 16:44:54.130001: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:44:54.141472: 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 00:00:1761925494.154910 1813094 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925494.159196 1813094 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925494.169524 1813094 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925494.169548 1813094 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925494.169550 1813094 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925494.169552 1813094 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:44:54.172787: 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 00:00:1761925496.501900 1813094 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925498.393736 1813228 service.cc:152] XLA service 0x787798006840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925498.393814 1813228 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:44:58.435697: 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 00:00:1761925498.675646 1813228 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925500.579102 1813228 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 2/27

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Epoch 3/27

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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1736 - loss: 2.4880
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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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[1m 911/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 2.0608
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[1m1007/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 2.0594
[1m1038/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 2.0590
[1m1072/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 2.0586
[1m1107/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3250 - loss: 2.0582
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[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.3249 - loss: 2.0578 - val_accuracy: 0.3464 - val_loss: 2.1179
Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.2500 - loss: 2.2157
[1m  34/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3305 - loss: 1.9761  
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[1m 105/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3250 - loss: 2.0113
[1m 138/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3263 - loss: 2.0103
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[1m 236/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3282 - loss: 2.0075
[1m 271/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3285 - loss: 2.0077
[1m 306/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3288 - loss: 2.0079
[1m 341/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3288 - loss: 2.0081
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[1m 517/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0077
[1m 553/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0079
[1m 587/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0081
[1m 620/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3293 - loss: 2.0083
[1m 655/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3293 - loss: 2.0086
[1m 691/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3293 - loss: 2.0090
[1m 727/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0094
[1m 763/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0096
[1m 798/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0096
[1m 828/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0096
[1m 863/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0097
[1m 896/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3292 - loss: 2.0099
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[1m 995/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3293 - loss: 2.0099
[1m1028/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3294 - loss: 2.0099
[1m1063/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3294 - loss: 2.0099
[1m1094/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3295 - loss: 2.0098
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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 26ms/step - accuracy: 0.5625 - loss: 1.6127
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Epoch 12/27

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Epoch 13/27

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[1m 757/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3976 - loss: 1.8228
[1m 790/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3973 - loss: 1.8236
[1m 825/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3971 - loss: 1.8243
[1m 857/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3969 - loss: 1.8248
[1m 892/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3967 - loss: 1.8253
[1m 927/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3965 - loss: 1.8258
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[1m 997/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3961 - loss: 1.8269
[1m1030/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3959 - loss: 1.8274
[1m1063/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3957 - loss: 1.8279
[1m1095/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3956 - loss: 1.8283
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:28[0m 1s/step
[1m53/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 974us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m113/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 908us/step
[1m177/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 866us/step
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[1m483/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 840us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 947us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 49/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m108/158[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 939us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 34.01 [%]
Global F1 score (validation) = 34.4 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[4.4594463e-03 4.3821307e-03 6.0974737e-03 ... 1.3854770e-01
  1.2181493e-02 5.5975448e-03]
 [4.4953423e-03 4.4924282e-03 5.7498938e-03 ... 1.5556073e-01
  1.1757091e-02 7.0994091e-03]
 [3.2719009e-02 2.9427676e-02 3.2545123e-02 ... 1.5857436e-01
  3.0020015e-02 1.8664086e-02]
 ...
 [1.0569614e-02 4.8745383e-02 7.1371786e-02 ... 8.5074262e-04
  5.4477911e-02 1.7483713e-02]
 [2.1022128e-02 5.3974879e-01 3.3710338e-02 ... 4.1287372e-04
  9.0310045e-02 4.9331523e-03]
 [1.2047574e-02 5.0517634e-02 2.4334100e-01 ... 1.1988377e-02
  2.9410353e-02 2.4556242e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 52.26 [%]
Global accuracy score (test) = 31.75 [%]
Global F1 score (train) = 53.16 [%]
Global F1 score (test) = 31.53 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.41      0.52      0.46       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.41      0.36       184
       CAMINAR USUAL SPEED       0.28      0.33      0.30       184
            CAMINAR ZIGZAG       0.16      0.20      0.18       184
          DE PIE BARRIENDO       0.31      0.22      0.25       184
   DE PIE DOBLANDO TOALLAS       0.20      0.18      0.19       184
    DE PIE MOVIENDO LIBROS       0.20      0.17      0.19       184
          DE PIE USANDO PC       0.19      0.37      0.25       184
        FASE REPOSO CON K5       0.59      0.60      0.60       184
INCREMENTAL CICLOERGOMETRO       0.71      0.58      0.64       184
           SENTADO LEYENDO       0.42      0.42      0.42       184
         SENTADO USANDO PC       0.14      0.06      0.08       184
      SENTADO VIENDO LA TV       0.18      0.24      0.20       184
   SUBIR Y BAJAR ESCALERAS       0.25      0.10      0.15       184
                    TROTAR       0.60      0.38      0.47       161

                  accuracy                           0.32      2737
                 macro avg       0.33      0.32      0.32      2737
              weighted avg       0.33      0.32      0.31      2737


Accuracy capturado en la ejecución 1: 31.75 [%]
F1-score capturado en la ejecución 1: 31.53 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
2025-10-31 16:45:47.385624: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:45:47.396940: 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 00:00:1761925547.410224 1815549 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925547.414350 1815549 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925547.424607 1815549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925547.424630 1815549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925547.424632 1815549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925547.424634 1815549 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:45:47.427962: 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 00:00:1761925549.761564 1815549 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925551.642104 1815675 service.cc:152] XLA service 0x735c0400fe70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925551.642174 1815675 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:45:51.688879: 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 00:00:1761925551.918324 1815675 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925553.817419 1815675 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03:56[0m 3s/step - accuracy: 0.0000e+00 - loss: 3.6100
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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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[1m 603/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1868 - loss: 2.4133
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Epoch 5/27

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Epoch 6/27

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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2424 - loss: 2.2774
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Epoch 7/27

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Epoch 8/27

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[1m1107/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2947 - loss: 2.1532
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Epoch 9/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.2500 - loss: 2.4395
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[1m 102/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3022 - loss: 2.0836
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[1m 557/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3161 - loss: 2.0713
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[1m 615/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3165 - loss: 2.0713
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[1m1007/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3184 - loss: 2.0692
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[1m1103/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3188 - loss: 2.0685
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Epoch 10/27

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Epoch 11/27

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Epoch 12/27

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Epoch 13/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 54/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 949us/step
[1m116/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 876us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 35.4 [%]
Global F1 score (validation) = 34.48 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00310935 0.01087297 0.00413735 ... 0.04798298 0.00916468 0.00648595]
 [0.00461032 0.01021248 0.00680015 ... 0.11431877 0.01320935 0.00860017]
 [0.0133989  0.00794953 0.01722947 ... 0.15132777 0.00920511 0.0129696 ]
 ...
 [0.02077089 0.01621441 0.39323786 ... 0.00247024 0.16395015 0.03889215]
 [0.06640055 0.05983948 0.1718343  ... 0.00346186 0.12364066 0.08162232]
 [0.06128103 0.08268873 0.19612828 ... 0.00908797 0.17020303 0.0334282 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.57 [%]
Global accuracy score (test) = 33.69 [%]
Global F1 score (train) = 47.95 [%]
Global F1 score (test) = 32.53 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.28      0.63      0.39       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.29      0.30       184
       CAMINAR USUAL SPEED       0.27      0.40      0.32       184
            CAMINAR ZIGZAG       0.29      0.13      0.18       184
          DE PIE BARRIENDO       0.25      0.27      0.26       184
   DE PIE DOBLANDO TOALLAS       0.30      0.25      0.27       184
    DE PIE MOVIENDO LIBROS       0.29      0.22      0.25       184
          DE PIE USANDO PC       0.21      0.38      0.27       184
        FASE REPOSO CON K5       0.67      0.61      0.64       184
INCREMENTAL CICLOERGOMETRO       0.76      0.57      0.65       184
           SENTADO LEYENDO       0.34      0.55      0.42       184
         SENTADO USANDO PC       0.06      0.02      0.03       184
      SENTADO VIENDO LA TV       0.30      0.24      0.27       184
   SUBIR Y BAJAR ESCALERAS       0.24      0.08      0.12       184
                    TROTAR       0.65      0.44      0.52       161

                  accuracy                           0.34      2737
                 macro avg       0.35      0.34      0.33      2737
              weighted avg       0.34      0.34      0.32      2737


Accuracy capturado en la ejecución 2: 33.69 [%]
F1-score capturado en la ejecución 2: 32.53 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
2025-10-31 16:46:40.857519: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:46:40.868879: 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 00:00:1761925600.882107 1818005 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925600.886369 1818005 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925600.896518 1818005 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925600.896540 1818005 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925600.896542 1818005 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925600.896544 1818005 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:46:40.899787: 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 00:00:1761925603.248376 1818005 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925605.141033 1818102 service.cc:152] XLA service 0x786d740103a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925605.141069 1818102 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:46:45.180309: 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 00:00:1761925605.419582 1818102 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925607.317339 1818102 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  61/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0739 - loss: 3.3591
[1m  96/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0772 - loss: 3.3301
[1m 127/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0786 - loss: 3.3077
[1m 160/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0795 - loss: 3.2880
[1m 195/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0804 - loss: 3.2692
[1m 229/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0816 - loss: 3.2517
[1m 263/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0832 - loss: 3.2361
[1m 297/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0848 - loss: 3.2222
[1m 333/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0865 - loss: 3.2090
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 20ms/step - accuracy: 0.1875 - loss: 2.3728
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[1m 570/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2895 - loss: 2.1362
[1m 602/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2894 - loss: 2.1364
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[1m 800/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2894 - loss: 2.1370
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Epoch 9/27

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Epoch 10/27

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Epoch 11/27

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Epoch 12/27

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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3808 - loss: 1.9126
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Epoch 13/27

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Epoch 14/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 60/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 858us/step
[1m116/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 879us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 32.94 [%]
Global F1 score (validation) = 32.84 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[4.3620616e-03 3.6444964e-03 1.1269689e-02 ... 1.6492322e-01
  9.0600718e-03 3.3212376e-03]
 [4.3735686e-03 3.6172105e-03 1.0045236e-02 ... 1.7465460e-01
  8.6912001e-03 4.4381842e-03]
 [3.5671338e-02 3.8309664e-02 3.5332810e-02 ... 1.0094804e-01
  3.3541631e-02 1.0386948e-02]
 ...
 [1.2462933e-02 3.0037645e-02 1.8952176e-01 ... 7.4026693e-04
  2.7841592e-01 2.1145763e-02]
 [6.8653591e-02 6.7715579e-01 4.7012731e-03 ... 2.3101592e-03
  8.6829945e-02 2.3631694e-02]
 [6.6889077e-02 3.9120883e-02 1.4910620e-01 ... 2.1888938e-04
  3.0539563e-01 3.4711964e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.94 [%]
Global accuracy score (test) = 33.18 [%]
Global F1 score (train) = 48.83 [%]
Global F1 score (test) = 33.48 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.35      0.60      0.45       184
 CAMINAR CON MÓVIL O LIBRO       0.38      0.37      0.38       184
       CAMINAR USUAL SPEED       0.30      0.40      0.34       184
            CAMINAR ZIGZAG       0.17      0.18      0.17       184
          DE PIE BARRIENDO       0.29      0.20      0.24       184
   DE PIE DOBLANDO TOALLAS       0.23      0.21      0.22       184
    DE PIE MOVIENDO LIBROS       0.27      0.21      0.24       184
          DE PIE USANDO PC       0.20      0.48      0.28       184
        FASE REPOSO CON K5       0.84      0.47      0.60       184
INCREMENTAL CICLOERGOMETRO       0.84      0.53      0.65       184
           SENTADO LEYENDO       0.40      0.45      0.42       184
         SENTADO USANDO PC       0.21      0.18      0.19       184
      SENTADO VIENDO LA TV       0.18      0.10      0.13       184
   SUBIR Y BAJAR ESCALERAS       0.33      0.19      0.24       184
                    TROTAR       0.55      0.40      0.46       161

                  accuracy                           0.33      2737
                 macro avg       0.37      0.33      0.33      2737
              weighted avg       0.37      0.33      0.33      2737


Accuracy capturado en la ejecución 3: 33.18 [%]
F1-score capturado en la ejecución 3: 33.48 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-10-31 16:47:36.554417: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:47:36.565652: 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 00:00:1761925656.578631 1820568 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925656.582871 1820568 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925656.592939 1820568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925656.592960 1820568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925656.592972 1820568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925656.592973 1820568 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:47:36.595972: 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 00:00:1761925658.961169 1820568 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925660.869149 1820671 service.cc:152] XLA service 0x70ce64013410 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925660.869212 1820671 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:47:40.912265: 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 00:00:1761925661.132422 1820671 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925663.027025 1820671 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 2/27

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[1m 603/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1434 - loss: 2.6166
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[1m 803/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1437 - loss: 2.6137
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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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[1m1109/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3008 - loss: 2.1091
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Epoch 9/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.3125 - loss: 2.3977
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[1m 291/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3415 - loss: 2.0597
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Epoch 10/27

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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.4375 - loss: 1.9446
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[1m 127/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4069 - loss: 1.8755
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[1m 602/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3880 - loss: 1.8969
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[1m 809/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3859 - loss: 1.8992
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[1m1052/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3846 - loss: 1.9018
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Epoch 12/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 53/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 965us/step
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[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
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Global accuracy score (validation) = 33.49 [%]
Global F1 score (validation) = 31.05 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00427124 0.00614271 0.00536304 ... 0.08422797 0.00646851 0.00207103]
 [0.00403599 0.00330217 0.00496683 ... 0.11494736 0.00585162 0.0021532 ]
 [0.02337542 0.01213135 0.02309039 ... 0.15636896 0.01818856 0.00576373]
 ...
 [0.01060191 0.05863153 0.20177872 ... 0.00049379 0.22591439 0.31471914]
 [0.01840387 0.25457814 0.02024976 ... 0.000627   0.36838058 0.127038  ]
 [0.06049912 0.07016487 0.21875241 ... 0.00416974 0.23440899 0.02754405]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.94 [%]
Global accuracy score (test) = 31.53 [%]
Global F1 score (train) = 50.9 [%]
Global F1 score (test) = 29.2 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.36      0.65      0.46       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.42      0.33       184
       CAMINAR USUAL SPEED       0.25      0.27      0.26       184
            CAMINAR ZIGZAG       0.23      0.15      0.18       184
          DE PIE BARRIENDO       0.24      0.27      0.25       184
   DE PIE DOBLANDO TOALLAS       0.22      0.11      0.15       184
    DE PIE MOVIENDO LIBROS       0.22      0.12      0.16       184
          DE PIE USANDO PC       0.20      0.48      0.29       184
        FASE REPOSO CON K5       0.58      0.64      0.61       184
INCREMENTAL CICLOERGOMETRO       0.84      0.55      0.67       184
           SENTADO LEYENDO       0.29      0.50      0.37       184
         SENTADO USANDO PC       0.05      0.02      0.03       184
      SENTADO VIENDO LA TV       0.14      0.02      0.03       184
   SUBIR Y BAJAR ESCALERAS       0.18      0.10      0.13       184
                    TROTAR       0.53      0.45      0.49       161

                  accuracy                           0.32      2737
                 macro avg       0.31      0.32      0.29      2737
              weighted avg       0.30      0.32      0.29      2737


Accuracy capturado en la ejecución 4: 31.53 [%]
F1-score capturado en la ejecución 4: 29.2 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
2025-10-31 16:48:28.012397: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:48:28.023689: 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 00:00:1761925708.036887 1822912 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925708.041117 1822912 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925708.051012 1822912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925708.051034 1822912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925708.051037 1822912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925708.051038 1822912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:48:28.054280: 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 00:00:1761925710.422364 1822912 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925712.321071 1823012 service.cc:152] XLA service 0x7d5aa8017170 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925712.321109 1823012 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:48:32.367964: 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 00:00:1761925712.596773 1823012 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925714.501007 1823012 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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[1m1007/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0963 - loss: 3.0540
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Epoch 2/27

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[1m 609/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1360 - loss: 2.6499
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[1m1050/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1397 - loss: 2.6337
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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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[1m1122/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2680 - loss: 2.2041
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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.5000 - loss: 1.6766
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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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[1m 888/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4125 - loss: 1.8056
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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 58/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 881us/step
[1m116/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 873us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 34.52 [%]
Global F1 score (validation) = 34.04 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00136072 0.00246737 0.00361638 ... 0.07898753 0.00602502 0.002119  ]
 [0.00263708 0.00488737 0.00745083 ... 0.145132   0.00976327 0.00417471]
 [0.04203864 0.07202889 0.03734861 ... 0.09093175 0.051794   0.02594392]
 ...
 [0.01504837 0.11656723 0.37069544 ... 0.00364429 0.158472   0.02248317]
 [0.115666   0.08758846 0.04398062 ... 0.00153184 0.1714955  0.07171679]
 [0.01754029 0.00653316 0.11556964 ... 0.00113525 0.11946552 0.02336111]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 49.27 [%]
Global accuracy score (test) = 34.97 [%]
Global F1 score (train) = 50.61 [%]
Global F1 score (test) = 34.31 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.34      0.67      0.45       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.36      0.31       184
       CAMINAR USUAL SPEED       0.32      0.35      0.34       184
            CAMINAR ZIGZAG       0.25      0.29      0.27       184
          DE PIE BARRIENDO       0.38      0.23      0.28       184
   DE PIE DOBLANDO TOALLAS       0.27      0.26      0.27       184
    DE PIE MOVIENDO LIBROS       0.22      0.10      0.14       184
          DE PIE USANDO PC       0.14      0.17      0.15       184
        FASE REPOSO CON K5       0.71      0.59      0.65       184
INCREMENTAL CICLOERGOMETRO       0.59      0.59      0.59       184
           SENTADO LEYENDO       0.50      0.38      0.43       184
         SENTADO USANDO PC       0.16      0.11      0.13       184
      SENTADO VIENDO LA TV       0.32      0.56      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.24      0.10      0.14       184
                    TROTAR       0.75      0.49      0.59       161

                  accuracy                           0.35      2737
                 macro avg       0.36      0.35      0.34      2737
              weighted avg       0.36      0.35      0.34      2737


Accuracy capturado en la ejecución 5: 34.97 [%]
F1-score capturado en la ejecución 5: 34.31 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
2025-10-31 16:49:23.577086: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:49:23.588718: 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 00:00:1761925763.602111 1825478 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925763.606494 1825478 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925763.616613 1825478 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925763.616636 1825478 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925763.616638 1825478 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925763.616640 1825478 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:49:23.619969: 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 00:00:1761925765.980374 1825478 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925767.896393 1825587 service.cc:152] XLA service 0x7edddc003000 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925767.896438 1825587 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:49:27.934350: 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 00:00:1761925768.163950 1825587 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925770.043654 1825587 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03:29[0m 3s/step - accuracy: 0.1250 - loss: 3.4482
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[1m  59/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0854 - loss: 3.2645
[1m  90/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0857 - loss: 3.2508
[1m 122/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0862 - loss: 3.2328
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
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Epoch 2/27

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Epoch 3/27

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[1m 904/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1599 - loss: 2.4997
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Epoch 4/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 20ms/step - accuracy: 0.1250 - loss: 2.7062
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[1m 579/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1768 - loss: 2.4392
[1m 613/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1771 - loss: 2.4388
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[1m1052/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1803 - loss: 2.4330
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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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[1m 842/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2602 - loss: 2.2013
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Epoch 8/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.3125 - loss: 2.2519
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Epoch 9/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.3125 - loss: 2.3507
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[1m1054/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3091 - loss: 2.0833
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.2500 - loss: 2.3272
[1m  32/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3041 - loss: 2.0766  
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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.4375 - loss: 2.1290
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[1m 168/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3515 - loss: 1.9827
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Epoch 12/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.5000 - loss: 1.7779
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Epoch 13/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 48/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m112/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 909us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
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Global accuracy score (validation) = 31.45 [%]
Global F1 score (validation) = 30.21 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[8.9584570e-03 5.8458843e-03 1.4625967e-02 ... 1.6472352e-01
  9.3923081e-03 6.8244082e-03]
 [9.0044150e-03 6.6230395e-03 1.3537172e-02 ... 1.6607510e-01
  1.0190233e-02 8.0609117e-03]
 [2.0534663e-02 1.3234527e-02 1.9069113e-02 ... 1.4874859e-01
  1.4992047e-02 1.1033179e-02]
 ...
 [2.6686017e-03 2.1779241e-02 1.6368805e-01 ... 8.2075386e-04
  3.4526836e-02 3.1538925e-03]
 [3.2570016e-02 2.4084911e-01 8.1690602e-02 ... 2.6434870e-03
  2.5920647e-01 1.6685240e-02]
 [2.0514692e-03 1.9051006e-02 6.2924288e-02 ... 9.4710493e-05
  2.5516365e-02 3.0876158e-04]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 49.8 [%]
Global accuracy score (test) = 32.37 [%]
Global F1 score (train) = 50.84 [%]
Global F1 score (test) = 32.29 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.62      0.41       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.36      0.32       184
       CAMINAR USUAL SPEED       0.30      0.25      0.27       184
            CAMINAR ZIGZAG       0.33      0.29      0.31       184
          DE PIE BARRIENDO       0.21      0.20      0.20       184
   DE PIE DOBLANDO TOALLAS       0.25      0.20      0.22       184
    DE PIE MOVIENDO LIBROS       0.24      0.16      0.19       184
          DE PIE USANDO PC       0.21      0.53      0.30       184
        FASE REPOSO CON K5       1.00      0.37      0.54       184
INCREMENTAL CICLOERGOMETRO       0.78      0.54      0.64       184
           SENTADO LEYENDO       0.32      0.42      0.36       184
         SENTADO USANDO PC       0.09      0.04      0.06       184
      SENTADO VIENDO LA TV       0.30      0.23      0.26       184
   SUBIR Y BAJAR ESCALERAS       0.29      0.20      0.23       184
                    TROTAR       0.57      0.48      0.52       161

                  accuracy                           0.32      2737
                 macro avg       0.37      0.33      0.32      2737
              weighted avg       0.36      0.32      0.32      2737


Accuracy capturado en la ejecución 6: 32.37 [%]
F1-score capturado en la ejecución 6: 32.29 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-10-31 16:50:16.786032: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:50:16.797256: 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 00:00:1761925816.810290 1827912 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925816.814522 1827912 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925816.824232 1827912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925816.824253 1827912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925816.824256 1827912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925816.824270 1827912 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:50:16.827430: 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 00:00:1761925819.175082 1827912 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925821.059369 1828045 service.cc:152] XLA service 0x7391bc013b50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925821.059403 1828045 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:50:21.098125: 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 00:00:1761925821.336885 1828045 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925823.269928 1828045 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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[1m1038/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1002 - loss: 3.0341
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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[1m 703/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1946 - loss: 2.4249
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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1945 - loss: 2.4227
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[1m1038/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1949 - loss: 2.4208
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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

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[1m 929/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3400 - loss: 2.0198
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[1m1025/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3402 - loss: 2.0188
[1m1055/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3403 - loss: 2.0184
[1m1088/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3403 - loss: 2.0181
[1m1120/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3404 - loss: 2.0178
[1m1153/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3404 - loss: 2.0175
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.3404 - loss: 2.0175 - val_accuracy: 0.3482 - val_loss: 2.1025
Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.1875 - loss: 2.0466
[1m  34/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3357 - loss: 2.0052  
[1m  67/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3424 - loss: 1.9924
[1m 101/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3396 - loss: 1.9969
[1m 137/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3398 - loss: 1.9929
[1m 171/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3406 - loss: 1.9896
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[1m 240/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3443 - loss: 1.9797
[1m 275/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3453 - loss: 1.9766
[1m 310/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3460 - loss: 1.9750
[1m 342/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3466 - loss: 1.9743
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[1m 477/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3481 - loss: 1.9735
[1m 510/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3482 - loss: 1.9737
[1m 543/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.9738
[1m 577/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.9740
[1m 610/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.9742
[1m 643/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3486 - loss: 1.9744
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[1m 782/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.9755
[1m 815/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3486 - loss: 1.9757
[1m 851/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3487 - loss: 1.9757
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[1m 984/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3495 - loss: 1.9754
[1m1017/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.9753
[1m1052/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.9752
[1m1084/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.9752
[1m1114/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3500 - loss: 1.9752
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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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Epoch 15/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 56/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 914us/step
[1m118/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 861us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 31.39 [%]
Global F1 score (validation) = 29.83 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.01073081 0.00908792 0.01046153 ... 0.13921668 0.00924216 0.00446822]
 [0.01078994 0.00966199 0.01185565 ... 0.16206579 0.01077099 0.00666241]
 [0.04525537 0.03720783 0.03297711 ... 0.12982887 0.02383744 0.01390206]
 ...
 [0.03472746 0.10289216 0.15913555 ... 0.00321463 0.09589907 0.03554793]
 [0.02766951 0.16740613 0.14816096 ... 0.00280873 0.05476623 0.03789978]
 [0.18745229 0.03233143 0.18286233 ... 0.01488713 0.21880616 0.01038519]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 46.64 [%]
Global accuracy score (test) = 30.4 [%]
Global F1 score (train) = 47.01 [%]
Global F1 score (test) = 29.58 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.65      0.38       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.36      0.30       184
       CAMINAR USUAL SPEED       0.39      0.19      0.26       184
            CAMINAR ZIGZAG       0.23      0.22      0.23       184
          DE PIE BARRIENDO       0.26      0.22      0.24       184
   DE PIE DOBLANDO TOALLAS       0.27      0.16      0.20       184
    DE PIE MOVIENDO LIBROS       0.22      0.20      0.21       184
          DE PIE USANDO PC       0.19      0.43      0.26       184
        FASE REPOSO CON K5       0.78      0.32      0.45       184
INCREMENTAL CICLOERGOMETRO       0.85      0.53      0.66       184
           SENTADO LEYENDO       0.25      0.56      0.34       184
         SENTADO USANDO PC       0.05      0.01      0.01       184
      SENTADO VIENDO LA TV       0.58      0.12      0.20       184
   SUBIR Y BAJAR ESCALERAS       0.15      0.11      0.13       184
                    TROTAR       0.66      0.52      0.58       161

                  accuracy                           0.30      2737
                 macro avg       0.36      0.31      0.30      2737
              weighted avg       0.36      0.30      0.29      2737


Accuracy capturado en la ejecución 7: 30.4 [%]
F1-score capturado en la ejecución 7: 29.58 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-10-31 16:51:14.784817: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:51:14.795867: 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 00:00:1761925874.809140 1830612 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925874.813210 1830612 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925874.823233 1830612 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925874.823253 1830612 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925874.823255 1830612 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925874.823256 1830612 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:51:14.826306: 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 00:00:1761925877.188551 1830612 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925879.102754 1830716 service.cc:152] XLA service 0x76dd200101f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925879.102826 1830716 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:51:19.151531: 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 00:00:1761925879.374372 1830716 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925881.272371 1830716 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  93/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0599 - loss: 3.3677
[1m 127/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0632 - loss: 3.3522
[1m 161/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0658 - loss: 3.3396
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[1m 269/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0709 - loss: 3.2987
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
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Epoch 2/27

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[1m 603/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1309 - loss: 2.6741
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Epoch 3/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.1250 - loss: 2.1607
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[1m 514/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1546 - loss: 2.5371
[1m 548/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1548 - loss: 2.5366
[1m 581/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1549 - loss: 2.5361
[1m 614/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1551 - loss: 2.5355
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[1m 754/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1553 - loss: 2.5326
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[1m 822/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1554 - loss: 2.5310
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[1m1023/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.1562 - loss: 2.5262
[1m1059/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.1564 - loss: 2.5253
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[1m1127/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.1567 - loss: 2.5238
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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

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Epoch 11/27

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[1m 964/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3497 - loss: 1.9768
[1m 996/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.9769
[1m1031/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3498 - loss: 1.9769
[1m1066/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.9769
[1m1102/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.9767
[1m1135/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.9766
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.3502 - loss: 1.9765 - val_accuracy: 0.3450 - val_loss: 2.1485
Epoch 12/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.4375 - loss: 1.5768
[1m  30/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3542 - loss: 1.8919  
[1m  64/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3563 - loss: 1.8976
[1m  96/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3611 - loss: 1.8966
[1m 132/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3648 - loss: 1.8933
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[1m 197/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3687 - loss: 1.8909
[1m 233/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3705 - loss: 1.8909
[1m 264/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3716 - loss: 1.8912
[1m 299/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3726 - loss: 1.8918
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[1m 510/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3766 - loss: 1.8936
[1m 545/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3771 - loss: 1.8932
[1m 580/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3775 - loss: 1.8929
[1m 613/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3778 - loss: 1.8926
[1m 647/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3782 - loss: 1.8923
[1m 681/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3784 - loss: 1.8923
[1m 715/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3786 - loss: 1.8924
[1m 749/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3787 - loss: 1.8927
[1m 783/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3788 - loss: 1.8928
[1m 817/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3789 - loss: 1.8931
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[1m 887/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3790 - loss: 1.8935
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[1m 955/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3792 - loss: 1.8937
[1m 990/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3793 - loss: 1.8938
[1m1026/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3795 - loss: 1.8939
[1m1060/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3796 - loss: 1.8941
[1m1093/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3797 - loss: 1.8942
[1m1127/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3797 - loss: 1.8944
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Epoch 13/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.3125 - loss: 1.9638
[1m  29/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 2ms/step - accuracy: 0.3668 - loss: 2.0032  
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[1m1103/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3966 - loss: 1.8668
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Epoch 14/27

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Epoch 15/27

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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:32[0m 1s/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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:24[0m 1s/step
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[1m163/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 932us/step
[1m218/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 928us/step
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[1m329/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 922us/step
[1m385/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 918us/step
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 917us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 58/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 882us/step
[1m117/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 869us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 34.94 [%]
Global F1 score (validation) = 34.92 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[2.7650222e-03 2.4815737e-03 1.4338763e-03 ... 1.9535735e-02
  3.8485490e-03 6.2025827e-03]
 [2.9716536e-03 2.4854585e-03 1.5342154e-03 ... 2.8677694e-02
  4.7577773e-03 4.7623347e-03]
 [9.5222639e-03 5.7481308e-03 1.3102391e-02 ... 1.8330072e-01
  9.7158765e-03 3.5707739e-03]
 ...
 [5.8313147e-03 1.8114291e-02 1.4983706e-02 ... 1.3683531e-03
  1.0664431e-02 5.2182790e-02]
 [1.6642025e-02 3.2055551e-01 8.5150518e-02 ... 2.3762383e-04
  2.2778863e-01 5.9187185e-02]
 [5.2710164e-02 1.2857464e-01 1.6186781e-01 ... 1.2819185e-02
  8.2832187e-02 3.3751301e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 51.51 [%]
Global accuracy score (test) = 31.46 [%]
Global F1 score (train) = 52.28 [%]
Global F1 score (test) = 30.97 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.60      0.42       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.32      0.32       184
       CAMINAR USUAL SPEED       0.27      0.20      0.23       184
            CAMINAR ZIGZAG       0.20      0.19      0.20       184
          DE PIE BARRIENDO       0.24      0.28      0.26       184
   DE PIE DOBLANDO TOALLAS       0.26      0.14      0.18       184
    DE PIE MOVIENDO LIBROS       0.17      0.15      0.16       184
          DE PIE USANDO PC       0.17      0.27      0.21       184
        FASE REPOSO CON K5       0.53      0.70      0.61       184
INCREMENTAL CICLOERGOMETRO       0.82      0.51      0.62       184
           SENTADO LEYENDO       0.31      0.40      0.35       184
         SENTADO USANDO PC       0.14      0.15      0.14       184
      SENTADO VIENDO LA TV       0.29      0.13      0.18       184
   SUBIR Y BAJAR ESCALERAS       0.34      0.17      0.23       184
                    TROTAR       0.57      0.53      0.55       161

                  accuracy                           0.31      2737
                 macro avg       0.33      0.32      0.31      2737
              weighted avg       0.33      0.31      0.31      2737


Accuracy capturado en la ejecución 8: 31.46 [%]
F1-score capturado en la ejecución 8: 30.97 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-10-31 16:52:12.560748: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:52:12.572307: 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 00:00:1761925932.585899 1833277 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925932.590245 1833277 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925932.600365 1833277 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925932.600390 1833277 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925932.600392 1833277 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925932.600394 1833277 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:52:12.603742: 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 00:00:1761925934.974618 1833277 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925936.859624 1833400 service.cc:152] XLA service 0x7c7cb0019a40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925936.859663 1833400 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:52:16.895875: 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 00:00:1761925937.126110 1833400 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925939.038877 1833400 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03:52[0m 3s/step - accuracy: 0.0625 - loss: 2.6559
[1m  29/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 2ms/step - accuracy: 0.0791 - loss: 3.1255    
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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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[1m1026/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3027 - loss: 2.1273
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[1m1096/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3028 - loss: 2.1270
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[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.3028 - loss: 2.1268 - val_accuracy: 0.3047 - val_loss: 2.1393
Epoch 9/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0078
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[1m 139/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3114 - loss: 2.0655
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[1m 276/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3196 - loss: 2.0641
[1m 311/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3201 - loss: 2.0642
[1m 345/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3205 - loss: 2.0642
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[1m 510/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 2.0650
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[1m 575/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3209 - loss: 2.0657
[1m 609/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3209 - loss: 2.0661
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[1m 777/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3214 - loss: 2.0661
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[1m1012/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3220 - loss: 2.0648
[1m1046/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3221 - loss: 2.0647
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[1m1115/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3223 - loss: 2.0643
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.3750 - loss: 1.7244
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Epoch 11/27

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Epoch 12/27

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Epoch 13/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 56/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 923us/step
[1m121/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 841us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
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Global accuracy score (validation) = 34.05 [%]
Global F1 score (validation) = 31.92 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00161465 0.00127094 0.00159115 ... 0.05578218 0.00146455 0.00090665]
 [0.00237952 0.00229529 0.0029944  ... 0.09354158 0.00314223 0.00219372]
 [0.01613846 0.01010996 0.01604515 ... 0.14663006 0.01426664 0.01497741]
 ...
 [0.12632123 0.16963872 0.02461201 ... 0.00186066 0.35175973 0.02871384]
 [0.01630298 0.03339047 0.05541229 ... 0.00088238 0.12749168 0.02292002]
 [0.03145923 0.01048196 0.09183239 ... 0.00104457 0.16018075 0.01342827]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 53.78 [%]
Global accuracy score (test) = 32.88 [%]
Global F1 score (train) = 54.28 [%]
Global F1 score (test) = 31.72 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.37      0.57      0.45       184
 CAMINAR CON MÓVIL O LIBRO       0.33      0.35      0.34       184
       CAMINAR USUAL SPEED       0.26      0.15      0.19       184
            CAMINAR ZIGZAG       0.25      0.27      0.26       184
          DE PIE BARRIENDO       0.24      0.35      0.28       184
   DE PIE DOBLANDO TOALLAS       0.32      0.22      0.26       184
    DE PIE MOVIENDO LIBROS       0.23      0.23      0.23       184
          DE PIE USANDO PC       0.20      0.40      0.26       184
        FASE REPOSO CON K5       0.51      0.72      0.60       184
INCREMENTAL CICLOERGOMETRO       0.77      0.54      0.63       184
           SENTADO LEYENDO       0.37      0.40      0.39       184
         SENTADO USANDO PC       0.17      0.09      0.12       184
      SENTADO VIENDO LA TV       0.22      0.06      0.09       184
   SUBIR Y BAJAR ESCALERAS       0.28      0.16      0.20       184
                    TROTAR       0.47      0.43      0.45       161

                  accuracy                           0.33      2737
                 macro avg       0.33      0.33      0.32      2737
              weighted avg       0.33      0.33      0.32      2737


Accuracy capturado en la ejecución 9: 32.88 [%]
F1-score capturado en la ejecución 9: 31.72 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-10-31 16:53:05.984071: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:53:05.995549: 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 00:00:1761925986.009467 1835739 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761925986.013907 1835739 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761925986.024467 1835739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925986.024497 1835739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925986.024500 1835739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761925986.024502 1835739 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:53:06.027846: 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 00:00:1761925988.418069 1835739 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761925990.341082 1835850 service.cc:152] XLA service 0x7f46cc022210 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761925990.341132 1835850 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:53:10.384906: 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 00:00:1761925990.605131 1835850 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761925992.491694 1835850 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 132/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0733 - loss: 3.2547
[1m 163/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0748 - loss: 3.2413
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

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Epoch 11/27

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[1m1026/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3502 - loss: 1.9763
[1m1059/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3502 - loss: 1.9763
[1m1095/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3503 - loss: 1.9763
[1m1130/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3504 - loss: 1.9762
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.3505 - loss: 1.9762 - val_accuracy: 0.3278 - val_loss: 2.1546
Epoch 12/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 20ms/step - accuracy: 0.2500 - loss: 2.4002
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[1m 106/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3875 - loss: 1.9036
[1m 141/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3886 - loss: 1.9037
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[1m 210/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3870 - loss: 1.9046
[1m 244/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3847 - loss: 1.9072
[1m 274/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3831 - loss: 1.9088
[1m 306/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3815 - loss: 1.9103
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[1m 478/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3783 - loss: 1.9131
[1m 511/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3780 - loss: 1.9132
[1m 547/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3778 - loss: 1.9132
[1m 580/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3775 - loss: 1.9131
[1m 615/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3772 - loss: 1.9132
[1m 649/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3769 - loss: 1.9133
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[1m 718/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3764 - loss: 1.9134
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[1m 789/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3759 - loss: 1.9135
[1m 820/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3757 - loss: 1.9136
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[1m 955/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3751 - loss: 1.9131
[1m 989/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3750 - loss: 1.9130
[1m1020/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3749 - loss: 1.9129
[1m1053/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3748 - loss: 1.9129
[1m1086/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3748 - loss: 1.9127
[1m1122/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3748 - loss: 1.9126
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Epoch 13/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.4375 - loss: 1.9419
[1m  32/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3999 - loss: 1.8423  
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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step
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Global accuracy score (validation) = 34.58 [%]
Global F1 score (validation) = 33.44 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00073823 0.00070431 0.00265415 ... 0.07433794 0.00319698 0.00124687]
 [0.00075964 0.00080212 0.00321817 ... 0.09301259 0.00340507 0.00153282]
 [0.00793198 0.01114783 0.0191651  ... 0.1549003  0.01582077 0.01327846]
 ...
 [0.03921537 0.04500818 0.17241639 ... 0.00064603 0.17977828 0.02872217]
 [0.03786151 0.25747722 0.01726069 ... 0.00119471 0.34734955 0.07042987]
 [0.04323594 0.3436406  0.06558424 ... 0.00079622 0.11076253 0.00255913]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 52.39 [%]
Global accuracy score (test) = 32.01 [%]
Global F1 score (train) = 53.03 [%]
Global F1 score (test) = 30.89 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.42      0.52      0.46       184
 CAMINAR CON MÓVIL O LIBRO       0.25      0.38      0.30       184
       CAMINAR USUAL SPEED       0.23      0.17      0.19       184
            CAMINAR ZIGZAG       0.22      0.22      0.22       184
          DE PIE BARRIENDO       0.21      0.20      0.21       184
   DE PIE DOBLANDO TOALLAS       0.21      0.14      0.17       184
    DE PIE MOVIENDO LIBROS       0.23      0.15      0.18       184
          DE PIE USANDO PC       0.19      0.36      0.25       184
        FASE REPOSO CON K5       0.52      0.78      0.62       184
INCREMENTAL CICLOERGOMETRO       0.76      0.57      0.65       184
           SENTADO LEYENDO       0.38      0.44      0.41       184
         SENTADO USANDO PC       0.13      0.07      0.09       184
      SENTADO VIENDO LA TV       0.24      0.12      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.25      0.22      0.23       184
                    TROTAR       0.50      0.48      0.49       161

                  accuracy                           0.32      2737
                 macro avg       0.32      0.32      0.31      2737
              weighted avg       0.31      0.32      0.31      2737


Accuracy capturado en la ejecución 10: 32.01 [%]
F1-score capturado en la ejecución 10: 30.89 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-10-31 16:53:59.101313: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:53:59.112794: 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 00:00:1761926039.125932 1838178 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926039.130038 1838178 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926039.139776 1838178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926039.139808 1838178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926039.139810 1838178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926039.139811 1838178 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:53:59.143060: 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 00:00:1761926041.483660 1838178 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926043.360296 1838310 service.cc:152] XLA service 0x788e30010120 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926043.360332 1838310 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:54:03.396768: 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 00:00:1761926043.617127 1838310 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926045.516708 1838310 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 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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[1m 904/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2004 - loss: 2.3914
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[1m1001/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2005 - loss: 2.3908
[1m1036/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2005 - loss: 2.3906
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[1m1100/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2006 - loss: 2.3902
[1m1130/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2007 - loss: 2.3900
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Epoch 6/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.1875 - loss: 2.5567
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[1m 302/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2247 - loss: 2.3369
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[1m 598/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2270 - loss: 2.3329
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[1m 759/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2277 - loss: 2.3311
[1m 792/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2279 - loss: 2.3307
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[1m1027/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2287 - loss: 2.3286
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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

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Epoch 11/27

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[1m1101/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3335 - loss: 2.0111
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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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Epoch 15/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.1875 - loss: 2.1763
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[1m1027/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4206 - loss: 1.8014
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Epoch 16/27

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Epoch 17/27

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[1m 428/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4527 - loss: 1.6940
[1m 460/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4526 - loss: 1.6944
[1m 491/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4524 - loss: 1.6950
[1m 524/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4522 - loss: 1.6953
[1m 557/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4520 - loss: 1.6957
[1m 590/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4517 - loss: 1.6962
[1m 621/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4515 - loss: 1.6966
[1m 652/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4513 - loss: 1.6973
[1m 684/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4511 - loss: 1.6980
[1m 717/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4510 - loss: 1.6986
[1m 750/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4508 - loss: 1.6992
[1m 782/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4507 - loss: 1.6997
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[1m1008/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4496 - loss: 1.7023
[1m1038/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4495 - loss: 1.7026
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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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:14[0m 1s/step
[1m 49/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step  
[1m108/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 941us/step
[1m167/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 912us/step
[1m227/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 895us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m54/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 955us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 58/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 890us/step
[1m112/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 910us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 35.46 [%]
Global F1 score (validation) = 33.93 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00171533 0.00207055 0.00160676 ... 0.01394252 0.00179204 0.00270401]
 [0.00194339 0.0021739  0.0014872  ... 0.01564231 0.0018837  0.00304635]
 [0.02163585 0.01516746 0.02629275 ... 0.14152426 0.01715574 0.01312131]
 ...
 [0.06597506 0.00362128 0.13035268 ... 0.00118522 0.11057658 0.01495866]
 [0.25538456 0.06174473 0.02652873 ... 0.00087197 0.3741971  0.04437818]
 [0.09275035 0.0139898  0.14200926 ... 0.00378117 0.08148907 0.0049459 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 49.74 [%]
Global accuracy score (test) = 33.94 [%]
Global F1 score (train) = 50.29 [%]
Global F1 score (test) = 32.89 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.64      0.43       184
 CAMINAR CON MÓVIL O LIBRO       0.33      0.36      0.35       184
       CAMINAR USUAL SPEED       0.31      0.26      0.28       184
            CAMINAR ZIGZAG       0.24      0.29      0.26       184
          DE PIE BARRIENDO       0.29      0.27      0.28       184
   DE PIE DOBLANDO TOALLAS       0.30      0.15      0.20       184
    DE PIE MOVIENDO LIBROS       0.28      0.25      0.26       184
          DE PIE USANDO PC       0.21      0.33      0.25       184
        FASE REPOSO CON K5       0.55      0.65      0.59       184
INCREMENTAL CICLOERGOMETRO       0.81      0.55      0.66       184
           SENTADO LEYENDO       0.29      0.51      0.37       184
         SENTADO USANDO PC       0.21      0.05      0.09       184
      SENTADO VIENDO LA TV       0.20      0.17      0.18       184
   SUBIR Y BAJAR ESCALERAS       0.32      0.12      0.17       184
                    TROTAR       0.60      0.52      0.56       161

                  accuracy                           0.34      2737
                 macro avg       0.35      0.34      0.33      2737
              weighted avg       0.35      0.34      0.33      2737


Accuracy capturado en la ejecución 11: 33.94 [%]
F1-score capturado en la ejecución 11: 32.89 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-10-31 16:55:01.525316: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:55:01.536553: 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 00:00:1761926101.549572 1841109 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926101.553747 1841109 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926101.563476 1841109 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926101.563500 1841109 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926101.563503 1841109 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926101.563504 1841109 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:55:01.566743: 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 00:00:1761926103.904454 1841109 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926105.812177 1841219 service.cc:152] XLA service 0x73db1800ff20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926105.812240 1841219 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:55:05.853091: 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 00:00:1761926106.073907 1841219 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926107.977249 1841219 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 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2313 - loss: 2.2932
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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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[1m1038/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3094 - loss: 2.0854
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 23ms/step - accuracy: 0.3125 - loss: 2.5187
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[1m 365/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3376 - loss: 2.0240
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Epoch 11/27

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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 55/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 940us/step
[1m117/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 875us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 31.11 [%]
Global F1 score (validation) = 31.27 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00507329 0.00427619 0.00937749 ... 0.200822   0.00529154 0.00220171]
 [0.00654046 0.00419817 0.00859049 ... 0.22065037 0.00542014 0.00279769]
 [0.03355843 0.01366137 0.01445126 ... 0.12897232 0.01769541 0.00853541]
 ...
 [0.00793801 0.00804178 0.3059588  ... 0.00794881 0.11522336 0.07821303]
 [0.4822095  0.11255912 0.00938494 ... 0.00752176 0.20213625 0.01712395]
 [0.06918273 0.07407075 0.08119342 ... 0.00070319 0.08428804 0.00254592]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 52.18 [%]
Global accuracy score (test) = 31.31 [%]
Global F1 score (train) = 54.12 [%]
Global F1 score (test) = 31.83 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.38      0.52      0.44       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.34      0.28       184
       CAMINAR USUAL SPEED       0.23      0.23      0.23       184
            CAMINAR ZIGZAG       0.27      0.28      0.28       184
          DE PIE BARRIENDO       0.23      0.14      0.18       184
   DE PIE DOBLANDO TOALLAS       0.26      0.16      0.20       184
    DE PIE MOVIENDO LIBROS       0.17      0.14      0.15       184
          DE PIE USANDO PC       0.21      0.22      0.21       184
        FASE REPOSO CON K5       0.81      0.40      0.53       184
INCREMENTAL CICLOERGOMETRO       0.76      0.59      0.66       184
           SENTADO LEYENDO       0.39      0.42      0.40       184
         SENTADO USANDO PC       0.18      0.35      0.24       184
      SENTADO VIENDO LA TV       0.29      0.29      0.29       184
   SUBIR Y BAJAR ESCALERAS       0.20      0.18      0.19       184
                    TROTAR       0.54      0.46      0.49       161

                  accuracy                           0.31      2737
                 macro avg       0.34      0.31      0.32      2737
              weighted avg       0.34      0.31      0.32      2737


Accuracy capturado en la ejecución 12: 31.31 [%]
F1-score capturado en la ejecución 12: 31.83 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-10-31 16:55:57.453411: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:55:57.464680: 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 00:00:1761926157.477896 1843680 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926157.482131 1843680 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926157.491940 1843680 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926157.491961 1843680 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926157.491972 1843680 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926157.491974 1843680 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:55:57.495200: 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 00:00:1761926159.836384 1843680 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926161.715052 1843779 service.cc:152] XLA service 0x7b78240112a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926161.715093 1843779 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:56:01.752967: 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 00:00:1761926161.991402 1843779 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926163.887630 1843779 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  98/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0703 - loss: 3.3813
[1m 132/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0730 - loss: 3.3471
[1m 166/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0750 - loss: 3.3203
[1m 199/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0761 - loss: 3.2993
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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.1875 - loss: 2.4470
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Epoch 7/27

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[1m1120/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2573 - loss: 2.2266
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Epoch 8/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.2500 - loss: 2.0168
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[1m 567/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2871 - loss: 2.1498
[1m 600/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2873 - loss: 2.1499
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[1m1007/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2879 - loss: 2.1527
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Epoch 9/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.3125 - loss: 2.2640
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[1m1106/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3081 - loss: 2.0767
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Epoch 10/27

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[1m 803/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3261 - loss: 2.0390
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Epoch 11/27

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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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Epoch 15/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 53/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 964us/step
[1m114/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 898us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 33.63 [%]
Global F1 score (validation) = 31.73 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00276099 0.00565022 0.00805079 ... 0.10115774 0.01096154 0.00435072]
 [0.00408245 0.00754375 0.00902812 ... 0.14454399 0.0146283  0.00816203]
 [0.12454183 0.05583708 0.03608666 ... 0.08878925 0.03989581 0.01498379]
 ...
 [0.18210822 0.06100613 0.10515638 ... 0.00392093 0.23638235 0.0728502 ]
 [0.12176511 0.06391393 0.21278152 ... 0.00618939 0.11114305 0.04559822]
 [0.13088135 0.03795215 0.23314361 ... 0.00424378 0.1368918  0.00835797]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.86 [%]
Global accuracy score (test) = 34.09 [%]
Global F1 score (train) = 50.44 [%]
Global F1 score (test) = 32.09 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.33      0.65      0.44       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.42      0.36       184
       CAMINAR USUAL SPEED       0.26      0.29      0.27       184
            CAMINAR ZIGZAG       0.28      0.14      0.18       184
          DE PIE BARRIENDO       0.27      0.42      0.33       184
   DE PIE DOBLANDO TOALLAS       0.16      0.13      0.14       184
    DE PIE MOVIENDO LIBROS       0.29      0.25      0.27       184
          DE PIE USANDO PC       0.23      0.43      0.30       184
        FASE REPOSO CON K5       0.62      0.65      0.64       184
INCREMENTAL CICLOERGOMETRO       0.72      0.59      0.65       184
           SENTADO LEYENDO       0.36      0.49      0.42       184
         SENTADO USANDO PC       0.15      0.08      0.10       184
      SENTADO VIENDO LA TV       0.52      0.09      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.31      0.05      0.08       184
                    TROTAR       0.50      0.45      0.47       161

                  accuracy                           0.34      2737
                 macro avg       0.35      0.34      0.32      2737
              weighted avg       0.35      0.34      0.32      2737


Accuracy capturado en la ejecución 13: 34.09 [%]
F1-score capturado en la ejecución 13: 32.09 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
2025-10-31 16:56:55.177042: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:56:55.188401: 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 00:00:1761926215.201890 1846337 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926215.206175 1846337 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926215.216059 1846337 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926215.216080 1846337 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926215.216082 1846337 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926215.216085 1846337 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:56:55.219365: 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 00:00:1761926217.590617 1846337 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926219.503600 1846468 service.cc:152] XLA service 0x749658017320 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926219.503636 1846468 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:56:59.540565: 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 00:00:1761926219.770699 1846468 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926221.668820 1846468 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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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2411 - loss: 2.2568
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Epoch 8/27

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[1m1116/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2649 - loss: 2.1875
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Epoch 9/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.4375 - loss: 1.7944
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[1m  65/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2887 - loss: 2.1350
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[1m 134/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2852 - loss: 2.1369
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[1m 605/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2903 - loss: 2.1272
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[1m 777/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2907 - loss: 2.1261
[1m 810/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2908 - loss: 2.1259
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[1m1038/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2919 - loss: 2.1240
[1m1070/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2921 - loss: 2.1237
[1m1102/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2923 - loss: 2.1233
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.3125 - loss: 2.0660
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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3178 - loss: 2.0654
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[1m1103/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3181 - loss: 2.0636
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Epoch 11/27

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Epoch 12/27

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[1m 834/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3595 - loss: 1.9377
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[1m1007/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3600 - loss: 1.9377
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[1m1112/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3602 - loss: 1.9376
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Epoch 13/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 22ms/step - accuracy: 0.4375 - loss: 1.7978
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[1m  65/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4004 - loss: 1.9012
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[1m 295/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4005 - loss: 1.8750
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[1m 802/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3894 - loss: 1.8802
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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3884 - loss: 1.8816
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[1m1067/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3871 - loss: 1.8831
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Epoch 14/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m126/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 806us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
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Global accuracy score (validation) = 31.98 [%]
Global F1 score (validation) = 31.86 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0055417  0.00200974 0.00908511 ... 0.1668223  0.00547371 0.00435555]
 [0.0050031  0.00202638 0.00706629 ... 0.1962698  0.00554393 0.00567867]
 [0.01167079 0.00636853 0.01171637 ... 0.15944934 0.00813988 0.00955645]
 ...
 [0.00123122 0.02205543 0.07808064 ... 0.00334378 0.03593367 0.24719289]
 [0.02817681 0.49759194 0.02292188 ... 0.00093326 0.1412362  0.01605736]
 [0.01772197 0.12810197 0.12448205 ... 0.00548731 0.11218484 0.0466353 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 51.15 [%]
Global accuracy score (test) = 32.55 [%]
Global F1 score (train) = 53.09 [%]
Global F1 score (test) = 32.52 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.52      0.39       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.41      0.30       184
       CAMINAR USUAL SPEED       0.30      0.19      0.23       184
            CAMINAR ZIGZAG       0.28      0.23      0.25       184
          DE PIE BARRIENDO       0.22      0.23      0.23       184
   DE PIE DOBLANDO TOALLAS       0.22      0.13      0.16       184
    DE PIE MOVIENDO LIBROS       0.26      0.14      0.18       184
          DE PIE USANDO PC       0.18      0.45      0.26       184
        FASE REPOSO CON K5       0.62      0.48      0.54       184
INCREMENTAL CICLOERGOMETRO       0.70      0.57      0.63       184
           SENTADO LEYENDO       0.48      0.33      0.39       184
         SENTADO USANDO PC       0.27      0.23      0.25       184
      SENTADO VIENDO LA TV       0.46      0.35      0.40       184
   SUBIR Y BAJAR ESCALERAS       0.22      0.13      0.16       184
                    TROTAR       0.47      0.50      0.48       161

                  accuracy                           0.33      2737
                 macro avg       0.35      0.33      0.33      2737
              weighted avg       0.35      0.33      0.32      2737


Accuracy capturado en la ejecución 14: 32.55 [%]
F1-score capturado en la ejecución 14: 32.52 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
2025-10-31 16:57:50.645244: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:57:50.656524: 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 00:00:1761926270.669673 1848898 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926270.673894 1848898 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926270.683786 1848898 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926270.683806 1848898 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926270.683808 1848898 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926270.683810 1848898 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:57:50.687037: 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 00:00:1761926273.020567 1848898 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926274.879035 1849017 service.cc:152] XLA service 0x7e9c6c0109f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926274.879068 1849017 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:57:54.915188: 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 00:00:1761926275.135221 1849017 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926277.028886 1849017 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 2/27

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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1421 - loss: 2.6286
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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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[1m 850/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2199 - loss: 2.3456
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Epoch 6/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.1875 - loss: 2.4015
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[1m1019/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2374 - loss: 2.2856
[1m1051/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2375 - loss: 2.2852
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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

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Epoch 11/27

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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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[1m1016/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4095 - loss: 1.8035
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:32[0m 1s/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.
(2737, 6, 250)
(18469, 6, 250)

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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 52/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 990us/step
[1m115/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 884us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 33.39 [%]
Global F1 score (validation) = 31.65 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[6.8043489e-03 6.7902608e-03 1.4691815e-02 ... 1.4574467e-01
  1.3434745e-02 6.6515598e-03]
 [6.4908750e-03 7.9746908e-03 1.4368960e-02 ... 1.5734901e-01
  1.2299624e-02 8.7007005e-03]
 [1.4513753e-02 1.0274602e-02 1.8058525e-02 ... 1.4853306e-01
  1.8255448e-02 8.5951993e-03]
 ...
 [7.9530096e-03 5.7565467e-03 1.4887941e-01 ... 3.2659215e-03
  7.8731760e-02 2.0859075e-01]
 [1.5159888e-02 5.7119664e-02 2.8799343e-01 ... 1.9409292e-04
  3.4730449e-01 2.6428346e-03]
 [7.7800222e-02 1.8421309e-02 5.9247047e-02 ... 3.2241151e-03
  8.6649053e-02 1.4242588e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 51.38 [%]
Global accuracy score (test) = 32.66 [%]
Global F1 score (train) = 51.04 [%]
Global F1 score (test) = 31.19 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.41      0.59      0.48       184
 CAMINAR CON MÓVIL O LIBRO       0.23      0.40      0.29       184
       CAMINAR USUAL SPEED       0.30      0.34      0.32       184
            CAMINAR ZIGZAG       0.29      0.27      0.28       184
          DE PIE BARRIENDO       0.24      0.33      0.28       184
   DE PIE DOBLANDO TOALLAS       0.28      0.20      0.23       184
    DE PIE MOVIENDO LIBROS       0.31      0.17      0.22       184
          DE PIE USANDO PC       0.20      0.45      0.27       184
        FASE REPOSO CON K5       0.48      0.70      0.57       184
INCREMENTAL CICLOERGOMETRO       0.81      0.48      0.60       184
           SENTADO LEYENDO       0.33      0.38      0.35       184
         SENTADO USANDO PC       0.18      0.03      0.06       184
      SENTADO VIENDO LA TV       0.24      0.07      0.11       184
   SUBIR Y BAJAR ESCALERAS       0.21      0.10      0.13       184
                    TROTAR       0.59      0.40      0.48       161

                  accuracy                           0.33      2737
                 macro avg       0.34      0.33      0.31      2737
              weighted avg       0.34      0.33      0.31      2737


Accuracy capturado en la ejecución 15: 32.66 [%]
F1-score capturado en la ejecución 15: 31.19 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-10-31 16:58:45.925446: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:58:45.936775: 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 00:00:1761926325.949828 1851451 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926325.954118 1851451 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926325.963794 1851451 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926325.963814 1851451 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926325.963817 1851451 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926325.963818 1851451 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:58:45.966952: 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 00:00:1761926328.316439 1851451 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926330.196205 1851581 service.cc:152] XLA service 0x7b1228012430 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926330.196276 1851581 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:58:50.238902: 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 00:00:1761926330.458541 1851581 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926332.360619 1851581 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  65/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0670 - loss: 3.5524
[1m  96/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0703 - loss: 3.4993
[1m 131/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0722 - loss: 3.4540
[1m 168/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0735 - loss: 3.4137
[1m 204/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0746 - loss: 3.3819
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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.0947 - loss: 3.1124
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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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[1m 803/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2600 - loss: 2.2424
[1m 839/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2604 - loss: 2.2416
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Epoch 8/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.5000 - loss: 1.9794
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Epoch 9/27

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[1m1103/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 2.0823
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.4375 - loss: 1.6294
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Epoch 11/27

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[1m 365/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3509 - loss: 1.9788
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Epoch 12/27

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Epoch 13/27

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[1m 603/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3991 - loss: 1.8605
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[1m1034/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3999 - loss: 1.8565
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Epoch 14/27

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Epoch 15/27

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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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:20[0m 1s/step
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[1m 60/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 859us/step
[1m124/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 820us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 35.48 [%]
Global F1 score (validation) = 33.79 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00343027 0.00313076 0.00554593 ... 0.12031196 0.00460061 0.00120945]
 [0.00390642 0.00330086 0.0060678  ... 0.14141689 0.00537488 0.00209125]
 [0.02276148 0.00949551 0.01937473 ... 0.1593874  0.02024096 0.00516196]
 ...
 [0.02475525 0.04723517 0.315749   ... 0.00323954 0.09214411 0.01513429]
 [0.08161636 0.18729804 0.07640027 ... 0.00259983 0.18589261 0.01276232]
 [0.00985681 0.03841142 0.45174533 ... 0.00315835 0.07022993 0.00809004]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 51.48 [%]
Global accuracy score (test) = 32.81 [%]
Global F1 score (train) = 51.89 [%]
Global F1 score (test) = 31.88 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.34      0.62      0.44       184
 CAMINAR CON MÓVIL O LIBRO       0.24      0.35      0.28       184
       CAMINAR USUAL SPEED       0.25      0.27      0.26       184
            CAMINAR ZIGZAG       0.24      0.20      0.22       184
          DE PIE BARRIENDO       0.29      0.22      0.25       184
   DE PIE DOBLANDO TOALLAS       0.28      0.11      0.16       184
    DE PIE MOVIENDO LIBROS       0.22      0.18      0.20       184
          DE PIE USANDO PC       0.22      0.33      0.27       184
        FASE REPOSO CON K5       0.49      0.80      0.61       184
INCREMENTAL CICLOERGOMETRO       0.87      0.52      0.65       184
           SENTADO LEYENDO       0.36      0.38      0.37       184
         SENTADO USANDO PC       0.24      0.18      0.21       184
      SENTADO VIENDO LA TV       0.18      0.16      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.12      0.16       184
                    TROTAR       0.59      0.48      0.53       161

                  accuracy                           0.33      2737
                 macro avg       0.34      0.33      0.32      2737
              weighted avg       0.33      0.33      0.32      2737


Accuracy capturado en la ejecución 16: 32.81 [%]
F1-score capturado en la ejecución 16: 31.88 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
2025-10-31 16:59:43.615997: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 16:59:43.627433: 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 00:00:1761926383.640745 1854149 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926383.645018 1854149 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926383.655095 1854149 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926383.655120 1854149 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926383.655122 1854149 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926383.655124 1854149 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 16:59:43.658405: 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 00:00:1761926386.005157 1854149 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926387.904005 1854270 service.cc:152] XLA service 0x786524005420 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926387.904075 1854270 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 16:59:47.950329: 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 00:00:1761926388.181211 1854270 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926390.095619 1854270 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 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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[1m1087/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3204 - loss: 2.0614
[1m1120/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3204 - loss: 2.0612
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[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.3204 - loss: 2.0610 - val_accuracy: 0.2783 - val_loss: 2.1792
Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.4375 - loss: 1.9520
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[1m  65/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3342 - loss: 2.0831
[1m 100/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3390 - loss: 2.0698
[1m 136/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3406 - loss: 2.0604
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[1m 273/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3425 - loss: 2.0327
[1m 308/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3424 - loss: 2.0284
[1m 342/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3425 - loss: 2.0245
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[1m 547/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 2.0084
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[1m 612/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3437 - loss: 2.0058
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[1m 809/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3436 - loss: 2.0009
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[1m 950/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.9995
[1m 983/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.9994
[1m1015/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.9992
[1m1048/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3433 - loss: 1.9990
[1m1082/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.9988
[1m1115/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3434 - loss: 1.9985
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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.5000 - loss: 1.7685
[1m  31/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3769 - loss: 1.8990  
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[1m 802/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3628 - loss: 1.9386
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[1m1114/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3628 - loss: 1.9385
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Epoch 12/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.3125 - loss: 1.6665
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[1m 297/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3650 - loss: 1.9101
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[1m 802/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3725 - loss: 1.8911
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Epoch 13/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 58/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 884us/step
[1m115/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 884us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 32.5 [%]
Global F1 score (validation) = 31.67 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00455109 0.00991636 0.00293822 ... 0.03470963 0.01122083 0.00366074]
 [0.00460087 0.0111362  0.00362521 ... 0.04901465 0.01187314 0.00588605]
 [0.03531476 0.01017302 0.02307962 ... 0.12727454 0.01965007 0.0130632 ]
 ...
 [0.08727281 0.14235468 0.01271194 ... 0.00958236 0.34000435 0.0884469 ]
 [0.08047293 0.0202129  0.05560054 ... 0.00328242 0.24692817 0.03826324]
 [0.24538012 0.0357085  0.08872677 ... 0.01129301 0.18856378 0.02183834]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 52.52 [%]
Global accuracy score (test) = 31.75 [%]
Global F1 score (train) = 53.63 [%]
Global F1 score (test) = 30.58 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.62      0.41       184
 CAMINAR CON MÓVIL O LIBRO       0.34      0.35      0.34       184
       CAMINAR USUAL SPEED       0.22      0.16      0.19       184
            CAMINAR ZIGZAG       0.23      0.27      0.25       184
          DE PIE BARRIENDO       0.22      0.25      0.23       184
   DE PIE DOBLANDO TOALLAS       0.31      0.18      0.23       184
    DE PIE MOVIENDO LIBROS       0.13      0.05      0.08       184
          DE PIE USANDO PC       0.18      0.42      0.26       184
        FASE REPOSO CON K5       0.57      0.69      0.62       184
INCREMENTAL CICLOERGOMETRO       0.66      0.62      0.64       184
           SENTADO LEYENDO       0.48      0.24      0.32       184
         SENTADO USANDO PC       0.09      0.06      0.07       184
      SENTADO VIENDO LA TV       0.39      0.20      0.27       184
   SUBIR Y BAJAR ESCALERAS       0.28      0.21      0.24       184
                    TROTAR       0.43      0.44      0.44       161

                  accuracy                           0.32      2737
                 macro avg       0.32      0.32      0.31      2737
              weighted avg       0.32      0.32      0.30      2737


Accuracy capturado en la ejecución 17: 31.75 [%]
F1-score capturado en la ejecución 17: 30.58 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
2025-10-31 17:00:36.915333: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:00:36.926564: 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 00:00:1761926436.939559 1856622 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926436.943682 1856622 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926436.953492 1856622 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926436.953513 1856622 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926436.953515 1856622 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926436.953516 1856622 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:00:36.956652: 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 00:00:1761926439.284956 1856622 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926441.179270 1856724 service.cc:152] XLA service 0x716b5c010020 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926441.179324 1856724 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:00:41.217991: 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 00:00:1761926441.452896 1856724 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926443.341013 1856724 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  60/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0729 - loss: 3.4745
[1m  94/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0758 - loss: 3.4135
[1m 129/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0764 - loss: 3.3719
[1m 161/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0778 - loss: 3.3393
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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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[1m 768/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1790 - loss: 2.4326
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[1m1103/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1795 - loss: 2.4309
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Epoch 5/27

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Epoch 6/27

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[1m1027/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2312 - loss: 2.2954
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Epoch 7/27

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[1m1121/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2597 - loss: 2.2307
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Epoch 8/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.2500 - loss: 2.5171
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[1m 736/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2800 - loss: 2.1781
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[1m 803/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2804 - loss: 2.1762
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[1m 902/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2810 - loss: 2.1735
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[1m1005/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2818 - loss: 2.1706
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Epoch 9/27

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[1m1049/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3108 - loss: 2.0770
[1m1083/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3109 - loss: 2.0771
[1m1117/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3109 - loss: 2.0772
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.3125 - loss: 2.2129
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[1m 802/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3146 - loss: 2.0389
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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3156 - loss: 2.0382
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[1m1112/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3175 - loss: 2.0363
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Epoch 11/27

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[1m 307/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3442 - loss: 1.9764
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Epoch 12/27

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Epoch 13/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.1875 - loss: 1.9525
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[1m 604/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3829 - loss: 1.8817
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[1m1026/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3837 - loss: 1.8792
[1m1062/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3837 - loss: 1.8791
[1m1096/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3837 - loss: 1.8791
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Epoch 14/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.4375 - loss: 1.8065
[1m  31/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4041 - loss: 1.7876  
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Epoch 15/27

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[1m 608/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4207 - loss: 1.7565
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[1m1124/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.7613
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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 49/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step 
[1m101/158[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step
[1m154/158[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 990us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 34.19 [%]
Global F1 score (validation) = 32.93 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00401337 0.00294858 0.00863552 ... 0.15931676 0.00328206 0.00284047]
 [0.00434064 0.00390181 0.00906168 ... 0.17450444 0.00476095 0.00519905]
 [0.01660672 0.01644397 0.02376738 ... 0.17314029 0.01336911 0.01195786]
 ...
 [0.08296    0.03030686 0.09419481 ... 0.00526808 0.09029827 0.02551741]
 [0.11116434 0.03185011 0.05968912 ... 0.00024378 0.1704702  0.00384119]
 [0.05720032 0.02612362 0.13890599 ... 0.00892969 0.04450923 0.01154551]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.39 [%]
Global accuracy score (test) = 32.41 [%]
Global F1 score (train) = 50.37 [%]
Global F1 score (test) = 30.85 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.33      0.62      0.43       184
 CAMINAR CON MÓVIL O LIBRO       0.20      0.35      0.26       184
       CAMINAR USUAL SPEED       0.33      0.44      0.38       184
            CAMINAR ZIGZAG       0.26      0.26      0.26       184
          DE PIE BARRIENDO       0.19      0.19      0.19       184
   DE PIE DOBLANDO TOALLAS       0.22      0.11      0.15       184
    DE PIE MOVIENDO LIBROS       0.17      0.08      0.11       184
          DE PIE USANDO PC       0.22      0.26      0.23       184
        FASE REPOSO CON K5       0.49      0.79      0.61       184
INCREMENTAL CICLOERGOMETRO       0.72      0.59      0.64       184
           SENTADO LEYENDO       0.50      0.19      0.28       184
         SENTADO USANDO PC       0.23      0.14      0.17       184
      SENTADO VIENDO LA TV       0.29      0.36      0.32       184
   SUBIR Y BAJAR ESCALERAS       0.13      0.07      0.09       184
                    TROTAR       0.63      0.44      0.52       161

                  accuracy                           0.32      2737
                 macro avg       0.33      0.33      0.31      2737
              weighted avg       0.32      0.32      0.31      2737


Accuracy capturado en la ejecución 18: 32.41 [%]
F1-score capturado en la ejecución 18: 30.85 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-10-31 17:01:34.353546: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:01:34.365030: 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 00:00:1761926494.378391 1859305 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926494.382742 1859305 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926494.392666 1859305 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926494.392691 1859305 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926494.392694 1859305 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926494.392695 1859305 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:01:34.396014: 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 00:00:1761926496.736730 1859305 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926498.593454 1859434 service.cc:152] XLA service 0x74ede40104a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926498.593497 1859434 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:01:38.629563: 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 00:00:1761926498.850027 1859434 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926500.768309 1859434 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 2/27

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Epoch 3/27

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Epoch 4/27

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[1m1038/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1866 - loss: 2.4116
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Epoch 5/27

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[1m 595/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2115 - loss: 2.3675
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[1m1027/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2149 - loss: 2.3548
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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

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[1m1027/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3435 - loss: 2.0076
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Epoch 11/27

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Epoch 12/27

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[1m 647/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3864 - loss: 1.8756
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:33[0m 1s/step
[1m54/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 969us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:56[0m 1s/step
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[1m163/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 932us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 903us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 896us/step
[1m115/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 880us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 33.31 [%]
Global F1 score (validation) = 31.91 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[9.4400911e-04 4.3225757e-04 3.0004850e-03 ... 1.1228543e-01
  1.1313965e-03 7.5603445e-04]
 [1.5208786e-03 5.7001226e-04 3.6379697e-03 ... 1.4832887e-01
  1.5860989e-03 1.4908649e-03]
 [1.1456727e-02 6.8584601e-03 1.5923385e-02 ... 1.8791595e-01
  1.1224679e-02 1.3251625e-02]
 ...
 [9.4239144e-03 3.0690772e-02 1.9927460e-01 ... 3.0130739e-04
  7.2555572e-02 1.8364359e-02]
 [1.5011980e-02 6.5587270e-01 3.0756371e-02 ... 1.1457810e-03
  8.1243195e-02 3.2498002e-02]
 [6.0124885e-02 1.8694878e-02 2.6426142e-01 ... 5.7514547e-03
  1.2954448e-01 1.4475202e-02]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 51.74 [%]
Global accuracy score (test) = 32.92 [%]
Global F1 score (train) = 52.37 [%]
Global F1 score (test) = 31.78 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.32      0.63      0.42       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.34      0.32       184
       CAMINAR USUAL SPEED       0.27      0.28      0.28       184
            CAMINAR ZIGZAG       0.24      0.25      0.24       184
          DE PIE BARRIENDO       0.31      0.23      0.27       184
   DE PIE DOBLANDO TOALLAS       0.29      0.22      0.25       184
    DE PIE MOVIENDO LIBROS       0.20      0.08      0.11       184
          DE PIE USANDO PC       0.24      0.42      0.30       184
        FASE REPOSO CON K5       0.45      0.73      0.55       184
INCREMENTAL CICLOERGOMETRO       0.73      0.54      0.62       184
           SENTADO LEYENDO       0.41      0.38      0.39       184
         SENTADO USANDO PC       0.14      0.09      0.11       184
      SENTADO VIENDO LA TV       0.23      0.19      0.21       184
   SUBIR Y BAJAR ESCALERAS       0.22      0.15      0.18       184
                    TROTAR       0.65      0.42      0.51       161

                  accuracy                           0.33      2737
                 macro avg       0.33      0.33      0.32      2737
              weighted avg       0.33      0.33      0.32      2737


Accuracy capturado en la ejecución 19: 32.92 [%]
F1-score capturado en la ejecución 19: 31.78 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
2025-10-31 17:02:25.601395: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:02:25.612667: 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 00:00:1761926545.625739 1861633 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926545.629943 1861633 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926545.639749 1861633 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926545.639768 1861633 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926545.639771 1861633 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926545.639772 1861633 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:02:25.642994: 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 00:00:1761926548.030943 1861633 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926549.901346 1861757 service.cc:152] XLA service 0x795000010d00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926549.901379 1861757 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:02:29.938132: 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 00:00:1761926550.159696 1861757 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926552.060949 1861757 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 2/27

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Epoch 3/27

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Epoch 4/27

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[1m1101/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1794 - loss: 2.4298
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Epoch 5/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.2500 - loss: 2.1351
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[1m 617/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2067 - loss: 2.3828
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[1m1025/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.3768
[1m1061/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2078 - loss: 2.3764
[1m1096/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2079 - loss: 2.3760
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Epoch 6/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 23ms/step - accuracy: 0.2500 - loss: 2.3691
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Epoch 7/27

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Epoch 8/27

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[1m1101/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2930 - loss: 2.1423
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[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.2933 - loss: 2.1418 - val_accuracy: 0.3315 - val_loss: 2.1462
Epoch 9/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.3125 - loss: 2.1390
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[1m  67/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3257 - loss: 2.0631
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[1m 126/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3227 - loss: 2.0653
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[1m 291/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3223 - loss: 2.0671
[1m 324/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3223 - loss: 2.0673
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[1m 492/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3227 - loss: 2.0674
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[1m 560/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3226 - loss: 2.0677
[1m 596/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3227 - loss: 2.0675
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[1m 700/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3232 - loss: 2.0669
[1m 735/1155[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3233 - loss: 2.0666
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[1m 804/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 2.0661
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[1m1039/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3234 - loss: 2.0650
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[1m1109/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3235 - loss: 2.0648
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.3750 - loss: 1.9436
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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 20ms/step - accuracy: 0.4375 - loss: 1.8153
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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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Epoch 15/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
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Global accuracy score (validation) = 34.7 [%]
Global F1 score (validation) = 33.11 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.0028234  0.00176806 0.00723069 ... 0.13477366 0.00411924 0.00373067]
 [0.00308926 0.00290361 0.00930152 ... 0.13466206 0.00618741 0.00548541]
 [0.01212209 0.01362281 0.02390313 ... 0.17277859 0.01446677 0.0112578 ]
 ...
 [0.02228604 0.09815709 0.31164676 ... 0.0024361  0.23590522 0.08738691]
 [0.11663005 0.23211566 0.1046006  ... 0.00937368 0.18669418 0.11591969]
 [0.19694811 0.05801027 0.10089956 ... 0.00174292 0.34442475 0.02665241]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.99 [%]
Global accuracy score (test) = 30.84 [%]
Global F1 score (train) = 48.11 [%]
Global F1 score (test) = 28.71 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.56      0.39       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.37      0.32       184
       CAMINAR USUAL SPEED       0.37      0.34      0.36       184
            CAMINAR ZIGZAG       0.19      0.18      0.19       184
          DE PIE BARRIENDO       0.26      0.26      0.26       184
   DE PIE DOBLANDO TOALLAS       0.16      0.07      0.10       184
    DE PIE MOVIENDO LIBROS       0.20      0.17      0.18       184
          DE PIE USANDO PC       0.19      0.42      0.26       184
        FASE REPOSO CON K5       0.52      0.71      0.60       184
INCREMENTAL CICLOERGOMETRO       0.59      0.64      0.61       184
           SENTADO LEYENDO       0.32      0.34      0.33       184
         SENTADO USANDO PC       0.18      0.05      0.08       184
      SENTADO VIENDO LA TV       0.04      0.01      0.02       184
   SUBIR Y BAJAR ESCALERAS       0.16      0.09      0.12       184
                    TROTAR       0.59      0.43      0.50       161

                  accuracy                           0.31      2737
                 macro avg       0.29      0.31      0.29      2737
              weighted avg       0.29      0.31      0.29      2737


Accuracy capturado en la ejecución 20: 30.84 [%]
F1-score capturado en la ejecución 20: 28.71 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
2025-10-31 17:03:23.455165: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:03:23.466396: 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 00:00:1761926603.479695 1864319 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926603.483732 1864319 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926603.493649 1864319 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926603.493669 1864319 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926603.493671 1864319 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926603.493673 1864319 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:03:23.496940: 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 00:00:1761926605.858044 1864319 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926607.766679 1864430 service.cc:152] XLA service 0x7c2bf4012560 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926607.766742 1864430 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:03:27.804566: 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 00:00:1761926608.026137 1864430 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926609.926566 1864430 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 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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[1m1027/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2403 - loss: 2.2720
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Epoch 7/27

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[1m 603/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2828 - loss: 2.1926
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Epoch 8/27

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Epoch 9/27

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[1m 802/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3124 - loss: 2.0766
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Epoch 10/27

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[1m 802/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3487 - loss: 1.9829
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Epoch 11/27

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[1m1104/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3637 - loss: 1.9532
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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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[1m1113/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.4122 - loss: 1.8047
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Epoch 15/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.3750 - loss: 1.5307
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[1m1019/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4400 - loss: 1.7280
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Epoch 16/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 54/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 947us/step
[1m116/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 876us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
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Global accuracy score (validation) = 34.84 [%]
Global F1 score (validation) = 33.44 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00061309 0.00070428 0.00148445 ... 0.0096567  0.00277253 0.00250794]
 [0.0018769  0.00208205 0.00283096 ... 0.02656512 0.00551552 0.00588976]
 [0.02180872 0.01600037 0.02684272 ... 0.17082444 0.01984808 0.0052255 ]
 ...
 [0.03608015 0.44072056 0.02393931 ... 0.0013346  0.1383297  0.17440812]
 [0.01836021 0.21882473 0.02657041 ... 0.00044837 0.04084311 0.00553952]
 [0.02888857 0.09570783 0.19866702 ... 0.00198702 0.11934163 0.0054741 ]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 53.85 [%]
Global accuracy score (test) = 31.24 [%]
Global F1 score (train) = 53.78 [%]
Global F1 score (test) = 29.09 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.34      0.64      0.44       184
 CAMINAR CON MÓVIL O LIBRO       0.27      0.40      0.32       184
       CAMINAR USUAL SPEED       0.40      0.26      0.31       184
            CAMINAR ZIGZAG       0.18      0.21      0.19       184
          DE PIE BARRIENDO       0.15      0.15      0.15       184
   DE PIE DOBLANDO TOALLAS       0.25      0.22      0.23       184
    DE PIE MOVIENDO LIBROS       0.16      0.07      0.10       184
          DE PIE USANDO PC       0.22      0.48      0.30       184
        FASE REPOSO CON K5       0.57      0.67      0.62       184
INCREMENTAL CICLOERGOMETRO       0.61      0.60      0.60       184
           SENTADO LEYENDO       0.37      0.36      0.37       184
         SENTADO USANDO PC       0.15      0.06      0.09       184
      SENTADO VIENDO LA TV       0.07      0.01      0.02       184
   SUBIR Y BAJAR ESCALERAS       0.19      0.12      0.15       184
                    TROTAR       0.50      0.44      0.47       161

                  accuracy                           0.31      2737
                 macro avg       0.29      0.31      0.29      2737
              weighted avg       0.29      0.31      0.29      2737


Accuracy capturado en la ejecución 21: 31.24 [%]
F1-score capturado en la ejecución 21: 29.09 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-10-31 17:04:23.542435: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:04:23.553730: 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 00:00:1761926663.566886 1867131 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926663.571097 1867131 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926663.580988 1867131 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926663.581007 1867131 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926663.581009 1867131 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926663.581011 1867131 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:04:23.584264: 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 00:00:1761926665.925716 1867131 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926667.821567 1867231 service.cc:152] XLA service 0x7af808004de0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926667.821628 1867231 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:04:27.859238: 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 00:00:1761926668.083201 1867231 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926669.985913 1867231 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 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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[1m1109/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2353 - loss: 2.3117
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Epoch 7/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 24ms/step - accuracy: 0.3125 - loss: 2.1020
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[1m 294/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2779 - loss: 2.1975
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[1m 950/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2754 - loss: 2.2072
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[1m1017/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2753 - loss: 2.2076
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Epoch 8/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0033
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Epoch 9/27

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Epoch 10/27

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Epoch 11/27

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Epoch 12/27

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[1m1017/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3762 - loss: 1.9103
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[1m1121/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3764 - loss: 1.9109
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Epoch 13/27

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Epoch 14/27

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Epoch 15/27

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Epoch 16/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 58/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 879us/step
[1m118/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 861us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
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Global accuracy score (validation) = 33.77 [%]
Global F1 score (validation) = 33.25 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[3.25320684e-03 3.96571448e-03 2.76159775e-03 ... 6.44547343e-02
  7.29075167e-03 1.98212941e-03]
 [5.38027240e-03 6.51027076e-03 5.01380349e-03 ... 1.20647356e-01
  1.04900533e-02 3.70904384e-03]
 [2.85013076e-02 1.57768894e-02 3.51795070e-02 ... 1.55478761e-01
  2.74828598e-02 7.74809765e-03]
 ...
 [1.67207107e-01 2.68844754e-01 2.58431230e-02 ... 2.19517318e-03
  7.67860189e-02 1.76792238e-02]
 [8.69476981e-03 9.55380440e-01 1.15858635e-03 ... 1.96893161e-04
  6.79149292e-03 3.03648645e-03]
 [1.40078574e-01 3.65602598e-02 1.30580842e-01 ... 1.49585074e-02
  6.75576255e-02 7.14397151e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 55.16 [%]
Global accuracy score (test) = 34.71 [%]
Global F1 score (train) = 56.94 [%]
Global F1 score (test) = 34.89 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.33      0.57      0.41       184
 CAMINAR CON MÓVIL O LIBRO       0.32      0.38      0.35       184
       CAMINAR USUAL SPEED       0.31      0.27      0.29       184
            CAMINAR ZIGZAG       0.21      0.25      0.23       184
          DE PIE BARRIENDO       0.30      0.27      0.29       184
   DE PIE DOBLANDO TOALLAS       0.30      0.17      0.22       184
    DE PIE MOVIENDO LIBROS       0.24      0.21      0.22       184
          DE PIE USANDO PC       0.17      0.22      0.19       184
        FASE REPOSO CON K5       0.72      0.70      0.71       184
INCREMENTAL CICLOERGOMETRO       0.88      0.50      0.64       184
           SENTADO LEYENDO       0.34      0.51      0.40       184
         SENTADO USANDO PC       0.24      0.17      0.20       184
      SENTADO VIENDO LA TV       0.35      0.36      0.35       184
   SUBIR Y BAJAR ESCALERAS       0.21      0.17      0.19       184
                    TROTAR       0.64      0.47      0.54       161

                  accuracy                           0.35      2737
                 macro avg       0.37      0.35      0.35      2737
              weighted avg       0.37      0.35      0.35      2737


Accuracy capturado en la ejecución 22: 34.71 [%]
F1-score capturado en la ejecución 22: 34.89 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
2025-10-31 17:05:23.726016: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:05:23.737470: 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 00:00:1761926723.750654 1869917 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926723.754707 1869917 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926723.764729 1869917 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926723.764751 1869917 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926723.764753 1869917 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926723.764755 1869917 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:05:23.768010: 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 00:00:1761926726.141215 1869917 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926728.044937 1870039 service.cc:152] XLA service 0x7825e00241b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926728.044972 1870039 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:05:28.082771: 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 00:00:1761926728.315331 1870039 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926730.233033 1870039 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 100/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0799 - loss: 3.2117
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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[1m1115/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2484 - loss: 2.2530
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[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.2486 - loss: 2.2522 - val_accuracy: 0.2688 - val_loss: 2.1835
Epoch 8/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 25ms/step - accuracy: 0.5625 - loss: 1.9338
[1m  34/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3055 - loss: 2.1065  
[1m  71/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.2929 - loss: 2.1451
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[1m 273/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.2806 - loss: 2.1833
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[1m 613/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2785 - loss: 2.1851
[1m 647/1155[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2783 - loss: 2.1852
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[1m 998/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2786 - loss: 2.1827
[1m1032/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2787 - loss: 2.1823
[1m1067/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2789 - loss: 2.1819
[1m1102/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2790 - loss: 2.1815
[1m1137/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2792 - loss: 2.1811
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Epoch 9/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 22ms/step - accuracy: 0.4375 - loss: 1.8611
[1m  34/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3155 - loss: 2.1282  
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.2500 - loss: 1.9742
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Epoch 11/27

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[1m 305/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3393 - loss: 2.0132
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[1m 510/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3431 - loss: 2.0041
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[1m 609/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3438 - loss: 2.0037
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[1m 906/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3451 - loss: 2.0009
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[1m1075/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3460 - loss: 1.9984
[1m1105/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3462 - loss: 1.9980
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[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.3465 - loss: 1.9973 - val_accuracy: 0.3587 - val_loss: 2.1189
Epoch 12/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0138
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[1m  96/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3507 - loss: 1.9042
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[1m 269/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3647 - loss: 1.9068
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[1m1021/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3672 - loss: 1.9200
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Epoch 13/27

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Epoch 14/27

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Epoch 15/27

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[1m 827/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4163 - loss: 1.7825
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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m60/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 853us/step
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 908us/step
[1m117/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 875us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 34.11 [%]
Global F1 score (validation) = 34.05 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[1.8126325e-03 1.3827403e-03 6.7914650e-04 ... 2.5645301e-02
  3.4541583e-03 1.5837994e-03]
 [5.8656172e-03 3.4926499e-03 2.8056512e-03 ... 1.1212113e-01
  6.0602790e-03 4.3984852e-03]
 [2.4488453e-02 1.7535463e-02 2.6284570e-02 ... 1.4066689e-01
  1.6427994e-02 1.0213512e-02]
 ...
 [1.3832828e-01 2.5514422e-02 2.4403321e-02 ... 6.0669747e-05
  7.7266234e-01 1.7472354e-03]
 [1.4234757e-01 1.2631707e-01 3.8308337e-02 ... 3.4802748e-04
  6.1022359e-01 2.6644343e-03]
 [1.7622296e-02 3.2550748e-02 1.3716111e-01 ... 1.3507662e-02
  4.5228276e-02 3.7392757e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 54.58 [%]
Global accuracy score (test) = 31.53 [%]
Global F1 score (train) = 56.17 [%]
Global F1 score (test) = 31.07 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.36      0.55      0.44       184
 CAMINAR CON MÓVIL O LIBRO       0.26      0.39      0.31       184
       CAMINAR USUAL SPEED       0.27      0.20      0.23       184
            CAMINAR ZIGZAG       0.25      0.24      0.25       184
          DE PIE BARRIENDO       0.31      0.29      0.30       184
   DE PIE DOBLANDO TOALLAS       0.20      0.13      0.16       184
    DE PIE MOVIENDO LIBROS       0.24      0.18      0.21       184
          DE PIE USANDO PC       0.20      0.27      0.23       184
        FASE REPOSO CON K5       0.54      0.60      0.57       184
INCREMENTAL CICLOERGOMETRO       0.70      0.61      0.65       184
           SENTADO LEYENDO       0.40      0.39      0.40       184
         SENTADO USANDO PC       0.13      0.16      0.15       184
      SENTADO VIENDO LA TV       0.22      0.12      0.16       184
   SUBIR Y BAJAR ESCALERAS       0.15      0.13      0.14       184
                    TROTAR       0.47      0.47      0.47       161

                  accuracy                           0.32      2737
                 macro avg       0.31      0.32      0.31      2737
              weighted avg       0.31      0.32      0.31      2737


Accuracy capturado en la ejecución 23: 31.53 [%]
F1-score capturado en la ejecución 23: 31.07 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
2025-10-31 17:06:21.535209: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:06:21.546430: 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 00:00:1761926781.559522 1872613 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926781.563709 1872613 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926781.573550 1872613 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926781.573571 1872613 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926781.573574 1872613 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926781.573575 1872613 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:06:21.576706: 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 00:00:1761926783.941071 1872613 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13749 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926785.822952 1872748 service.cc:152] XLA service 0x77340c010810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926785.822988 1872748 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:06:25.859367: 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 00:00:1761926786.089608 1872748 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926787.986643 1872748 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03:37[0m 3s/step - accuracy: 0.1250 - loss: 3.6397
[1m  26/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 2ms/step - accuracy: 0.0811 - loss: 3.3582    
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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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[1m 590/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3128 - loss: 2.1007
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Epoch 10/27

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Epoch 11/27

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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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Epoch 15/27

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Epoch 16/27

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[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 874us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:08[0m 1s/step
[1m 51/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step  
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[1m53/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 967us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 53/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 967us/step
[1m112/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 905us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 32.82 [%]
Global F1 score (validation) = 32.7 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00368857 0.00361435 0.00639825 ... 0.17387015 0.00583252 0.00281105]
 [0.00444861 0.00372917 0.00575681 ... 0.17286377 0.00689597 0.00482884]
 [0.01268857 0.01412679 0.01574473 ... 0.14678366 0.01180715 0.01141842]
 ...
 [0.0147991  0.0049872  0.03120429 ... 0.00132605 0.09816323 0.49954748]
 [0.10751979 0.03222018 0.0118077  ... 0.00081612 0.16859424 0.18083173]
 [0.1913626  0.05759049 0.10767444 ... 0.00755363 0.1743766  0.01684477]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 48.03 [%]
Global accuracy score (test) = 29.92 [%]
Global F1 score (train) = 49.46 [%]
Global F1 score (test) = 29.27 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.63      0.42       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.33      0.32       184
       CAMINAR USUAL SPEED       0.28      0.30      0.29       184
            CAMINAR ZIGZAG       0.24      0.20      0.22       184
          DE PIE BARRIENDO       0.37      0.14      0.20       184
   DE PIE DOBLANDO TOALLAS       0.22      0.16      0.18       184
    DE PIE MOVIENDO LIBROS       0.19      0.14      0.16       184
          DE PIE USANDO PC       0.19      0.48      0.27       184
        FASE REPOSO CON K5       0.72      0.37      0.49       184
INCREMENTAL CICLOERGOMETRO       0.64      0.64      0.64       184
           SENTADO LEYENDO       0.20      0.08      0.11       184
         SENTADO USANDO PC       0.13      0.10      0.11       184
      SENTADO VIENDO LA TV       0.24      0.32      0.27       184
   SUBIR Y BAJAR ESCALERAS       0.24      0.18      0.20       184
                    TROTAR       0.58      0.44      0.50       161

                  accuracy                           0.30      2737
                 macro avg       0.32      0.30      0.29      2737
              weighted avg       0.32      0.30      0.29      2737


Accuracy capturado en la ejecución 24: 29.92 [%]
F1-score capturado en la ejecución 24: 29.27 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-10-31 17:07:21.689785: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:07:21.701069: 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 00:00:1761926841.714189 1875436 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926841.718454 1875436 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926841.728272 1875436 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926841.728294 1875436 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926841.728296 1875436 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926841.728298 1875436 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:07:21.731538: 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 00:00:1761926844.061475 1875436 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13750 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926845.942222 1875546 service.cc:152] XLA service 0x7294a4004320 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926845.942258 1875546 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:07:25.979408: 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 00:00:1761926846.213047 1875546 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926848.103624 1875546 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 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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[1m1151/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3035 - loss: 2.0763
[1m1155/1155[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 2ms/step - accuracy: 0.3035 - loss: 2.0763 - val_accuracy: 0.3194 - val_loss: 2.1364
Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.1250 - loss: 2.5053
[1m  35/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2804 - loss: 2.1574  
[1m  67/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2981 - loss: 2.1373
[1m 103/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3074 - loss: 2.1097
[1m 138/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3138 - loss: 2.0888
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[1m 236/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3222 - loss: 2.0661
[1m 270/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3236 - loss: 2.0620
[1m 306/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.3248 - loss: 2.0579
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[1m 571/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3305 - loss: 2.0390
[1m 607/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3310 - loss: 2.0375
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[1m 955/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 2.0287
[1m 990/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 2.0283
[1m1024/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 2.0279
[1m1060/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 2.0275
[1m1095/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 2.0271
[1m1127/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3330 - loss: 2.0268
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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.3125 - loss: 2.1934
[1m  33/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.2957 - loss: 2.0381  
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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 22ms/step - accuracy: 0.4375 - loss: 1.9537
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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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:57[0m 1s/step
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[1m106/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 964us/step
[1m164/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 928us/step
[1m220/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 920us/step
[1m279/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 907us/step
[1m337/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 900us/step
[1m395/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 895us/step
[1m454/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 889us/step
[1m514/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 884us/step
[1m573/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 881us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 897us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 58/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 880us/step
[1m116/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 876us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 33.25 [%]
Global F1 score (validation) = 32.4 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00483586 0.00549169 0.00846066 ... 0.16719523 0.00821398 0.00430675]
 [0.00508525 0.00454416 0.00764258 ... 0.19558167 0.00830754 0.00530375]
 [0.02999903 0.01347948 0.01817263 ... 0.16009288 0.01847192 0.01137223]
 ...
 [0.01150773 0.01449277 0.24737352 ... 0.0124695  0.0481292  0.09378178]
 [0.01362518 0.11210252 0.23447375 ... 0.00734332 0.12263233 0.20376457]
 [0.3206026  0.0449857  0.14366809 ... 0.00314935 0.11897476 0.00073769]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.37 [%]
Global accuracy score (test) = 32.33 [%]
Global F1 score (train) = 51.5 [%]
Global F1 score (test) = 31.5 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.38      0.62      0.47       184
 CAMINAR CON MÓVIL O LIBRO       0.28      0.34      0.31       184
       CAMINAR USUAL SPEED       0.22      0.30      0.25       184
            CAMINAR ZIGZAG       0.26      0.18      0.21       184
          DE PIE BARRIENDO       0.27      0.23      0.25       184
   DE PIE DOBLANDO TOALLAS       0.21      0.11      0.15       184
    DE PIE MOVIENDO LIBROS       0.23      0.27      0.25       184
          DE PIE USANDO PC       0.19      0.48      0.27       184
        FASE REPOSO CON K5       0.72      0.55      0.63       184
INCREMENTAL CICLOERGOMETRO       0.85      0.50      0.63       184
           SENTADO LEYENDO       0.32      0.46      0.38       184
         SENTADO USANDO PC       0.11      0.03      0.05       184
      SENTADO VIENDO LA TV       0.36      0.21      0.27       184
   SUBIR Y BAJAR ESCALERAS       0.25      0.09      0.13       184
                    TROTAR       0.45      0.50      0.47       161

                  accuracy                           0.32      2737
                 macro avg       0.34      0.32      0.32      2737
              weighted avg       0.34      0.32      0.31      2737


Accuracy capturado en la ejecución 25: 32.33 [%]
F1-score capturado en la ejecución 25: 31.5 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
2025-10-31 17:08:17.191134: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:08:17.202489: 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 00:00:1761926897.215539 1878020 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926897.219714 1878020 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926897.229495 1878020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926897.229516 1878020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926897.229518 1878020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926897.229519 1878020 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:08:17.232821: 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 00:00:1761926899.576684 1878020 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13748 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926901.508416 1878124 service.cc:152] XLA service 0x72e63c012ab0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926901.508480 1878124 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:08:21.547241: 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 00:00:1761926901.776262 1878124 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926903.677139 1878124 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03:57[0m 3s/step - accuracy: 0.1250 - loss: 3.3727
[1m  27/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 2ms/step - accuracy: 0.0713 - loss: 3.3771    
[1m  62/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0711 - loss: 3.3394
[1m  98/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0712 - loss: 3.3089
[1m 135/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0722 - loss: 3.2858
[1m 171/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0740 - loss: 3.2672
[1m 204/1155[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0753 - loss: 3.2515
[1m 239/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0769 - loss: 3.2356
[1m 275/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0783 - loss: 3.2202
[1m 311/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0795 - loss: 3.2059
[1m 345/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0807 - loss: 3.1929
[1m 381/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0819 - loss: 3.1800
[1m 412/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0830 - loss: 3.1697
[1m 447/1155[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0842 - loss: 3.1589
[1m 479/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 1ms/step - accuracy: 0.0852 - loss: 3.1498
[1m 512/1155[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0861 - loss: 3.1411
[1m 547/1155[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0871 - loss: 3.1321
[1m 580/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0881 - loss: 3.1240
[1m 614/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.0889 - loss: 3.1161
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2326 - loss: 2.2948
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[1m1007/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2333 - loss: 2.2947
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[1m1101/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2340 - loss: 2.2945
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Epoch 7/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 21ms/step - accuracy: 0.2500 - loss: 2.0941
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[1m 903/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2612 - loss: 2.2378
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Epoch 8/27

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Epoch 9/27

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[1m 365/1155[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3127 - loss: 2.1030
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Epoch 10/27

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Epoch 11/27

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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

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[1m 925/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4251 - loss: 1.8001
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[1m1101/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4241 - loss: 1.8012
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[1m56/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 923us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/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.
(2737, 6, 250)
(18469, 6, 250)

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[1m164/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 925us/step
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[1m58/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 891us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 898us/step
[1m112/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 908us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 32.94 [%]
Global F1 score (validation) = 30.55 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[2.14234460e-03 2.66665989e-03 5.19098202e-03 ... 1.49459973e-01
  2.23401282e-03 4.83520608e-03]
 [2.77917902e-03 3.37193860e-03 4.29069530e-03 ... 1.31631717e-01
  2.78030313e-03 8.22648499e-03]
 [1.17074372e-02 1.21115129e-02 1.09245321e-02 ... 1.57555059e-01
  1.13713080e-02 1.32282367e-02]
 ...
 [2.03169405e-01 7.73765370e-02 1.22763313e-01 ... 3.34038679e-03
  2.48686329e-01 3.77511233e-02]
 [2.24320255e-02 3.48128289e-01 1.17759585e-01 ... 2.38981083e-04
  1.01382606e-01 2.32909787e-02]
 [4.04976644e-02 3.82854715e-02 3.41647267e-01 ... 5.55755047e-04
  6.02021441e-02 3.52594000e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 47.57 [%]
Global accuracy score (test) = 31.53 [%]
Global F1 score (train) = 46.6 [%]
Global F1 score (test) = 28.93 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.31      0.66      0.42       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.42      0.35       184
       CAMINAR USUAL SPEED       0.27      0.35      0.30       184
            CAMINAR ZIGZAG       0.23      0.21      0.22       184
          DE PIE BARRIENDO       0.25      0.24      0.25       184
   DE PIE DOBLANDO TOALLAS       0.25      0.14      0.18       184
    DE PIE MOVIENDO LIBROS       0.24      0.16      0.19       184
          DE PIE USANDO PC       0.15      0.34      0.21       184
        FASE REPOSO CON K5       0.57      0.76      0.65       184
INCREMENTAL CICLOERGOMETRO       0.47      0.67      0.55       184
           SENTADO LEYENDO       0.23      0.02      0.03       184
         SENTADO USANDO PC       0.25      0.05      0.08       184
      SENTADO VIENDO LA TV       0.29      0.16      0.21       184
   SUBIR Y BAJAR ESCALERAS       0.18      0.11      0.14       184
                    TROTAR       0.68      0.46      0.55       161

                  accuracy                           0.32      2737
                 macro avg       0.31      0.32      0.29      2737
              weighted avg       0.31      0.32      0.29      2737


Accuracy capturado en la ejecución 26: 31.53 [%]
F1-score capturado en la ejecución 26: 28.93 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
2025-10-31 17:09:12.539658: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:09:12.551236: 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 00:00:1761926952.564505 1880587 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761926952.568790 1880587 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761926952.578656 1880587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926952.578685 1880587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926952.578687 1880587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761926952.578689 1880587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:09:12.581972: 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 00:00:1761926954.956827 1880587 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13747 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761926956.877998 1880672 service.cc:152] XLA service 0x7ad83c002580 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761926956.878035 1880672 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:09:16.914480: 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 00:00:1761926957.141148 1880672 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761926959.051046 1880672 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:03:58[0m 3s/step - accuracy: 0.0625 - loss: 3.4344
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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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[1m1027/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1905 - loss: 2.4265
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[1m1121/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.1910 - loss: 2.4252
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Epoch 5/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 20ms/step - accuracy: 0.1250 - loss: 2.5739
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[1m 755/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2134 - loss: 2.3596
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[1m1018/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2162 - loss: 2.3544
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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.5625 - loss: 1.6938
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[1m 615/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.9862
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[1m 809/1155[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3521 - loss: 1.9872
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[1m1007/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3514 - loss: 1.9880
[1m1040/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3513 - loss: 1.9881
[1m1072/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.3512 - loss: 1.9882
[1m1106/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3511 - loss: 1.9883
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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 20ms/step - accuracy: 0.3125 - loss: 1.7619
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Epoch 12/27

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Epoch 13/27

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[1m 927/1155[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4016 - loss: 1.8296
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[1m 993/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4014 - loss: 1.8298
[1m1028/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4014 - loss: 1.8299
[1m1062/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.4013 - loss: 1.8299
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:30[0m 1s/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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:51[0m 1s/step
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[1m164/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 923us/step
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[1m279/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 905us/step
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[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
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[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 875us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step
[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 864us/step
[1m121/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 837us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 35.52 [%]
Global F1 score (validation) = 34.22 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[4.7861392e-04 8.6378184e-04 1.1282213e-03 ... 5.2755784e-02
  9.5214747e-04 1.4368575e-03]
 [5.8679096e-04 1.0547451e-03 1.2927593e-03 ... 5.5986702e-02
  1.2139047e-03 1.9415807e-03]
 [1.9461039e-02 1.5249756e-02 2.5031878e-02 ... 1.6157104e-01
  1.9591026e-02 9.7950920e-03]
 ...
 [7.3639052e-03 6.9867587e-01 2.9036466e-03 ... 1.6231977e-05
  2.1499783e-01 5.9399875e-03]
 [9.7706780e-02 5.6321114e-02 1.8882580e-02 ... 2.1124444e-05
  3.9240736e-01 8.2895812e-03]
 [2.2193268e-01 7.5402692e-02 7.3704243e-02 ... 8.7429816e-03
  2.2439392e-01 8.3308630e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 51.2 [%]
Global accuracy score (test) = 33.83 [%]
Global F1 score (train) = 51.05 [%]
Global F1 score (test) = 32.93 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.46      0.53      0.49       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.42      0.36       184
       CAMINAR USUAL SPEED       0.35      0.42      0.39       184
            CAMINAR ZIGZAG       0.31      0.20      0.24       184
          DE PIE BARRIENDO       0.26      0.39      0.31       184
   DE PIE DOBLANDO TOALLAS       0.22      0.22      0.22       184
    DE PIE MOVIENDO LIBROS       0.25      0.15      0.19       184
          DE PIE USANDO PC       0.19      0.42      0.26       184
        FASE REPOSO CON K5       0.47      0.73      0.57       184
INCREMENTAL CICLOERGOMETRO       0.82      0.56      0.67       184
           SENTADO LEYENDO       0.36      0.34      0.35       184
         SENTADO USANDO PC       0.10      0.03      0.05       184
      SENTADO VIENDO LA TV       0.30      0.18      0.23       184
   SUBIR Y BAJAR ESCALERAS       0.18      0.10      0.13       184
                    TROTAR       0.71      0.37      0.49       161

                  accuracy                           0.34      2737
                 macro avg       0.35      0.34      0.33      2737
              weighted avg       0.35      0.34      0.33      2737


Accuracy capturado en la ejecución 27: 33.83 [%]
F1-score capturado en la ejecución 27: 32.93 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-10-31 17:10:06.348155: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:10:06.360140: 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 00:00:1761927006.373969 1883045 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761927006.378366 1883045 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761927006.388679 1883045 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761927006.388701 1883045 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761927006.388704 1883045 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761927006.388706 1883045 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:10:06.391998: 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 00:00:1761927008.764210 1883045 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13748 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761927010.669266 1883177 service.cc:152] XLA service 0x7e31e8005fb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761927010.669309 1883177 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:10:10.708213: 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 00:00:1761927010.937965 1883177 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761927012.872729 1883177 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:04:45[0m 3s/step - accuracy: 0.0000e+00 - loss: 3.9461
[1m  27/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 2ms/step - accuracy: 0.0628 - loss: 3.5073        
[1m  59/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0676 - loss: 3.4482
[1m  94/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0701 - loss: 3.4017
[1m 127/1155[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.0723 - loss: 3.3681
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
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Epoch 2/27

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Epoch 3/27

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Epoch 4/27

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Epoch 5/27

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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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[1m1101/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.2929 - loss: 2.1379
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Epoch 9/27

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Epoch 10/27

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Epoch 11/27

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Epoch 12/27

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[1m1100/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3792 - loss: 1.8980
[1m1133/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3793 - loss: 1.8979
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Epoch 13/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 20ms/step - accuracy: 0.1875 - loss: 2.2121
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[1m  96/1155[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3978 - loss: 1.7921
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[1m 257/1155[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4044 - loss: 1.7939
[1m 292/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.4039 - loss: 1.7962
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[1m1017/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3990 - loss: 1.8243
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Epoch 14/27

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[1m 802/1155[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4097 - loss: 1.8188
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Epoch 15/27

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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.
(2737, 6, 250)
(18469, 6, 250)

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[1m 59/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 865us/step
[1m121/158[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 837us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 35.1 [%]
Global F1 score (validation) = 34.33 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00047928 0.00228203 0.00345773 ... 0.06590938 0.00500637 0.00122066]
 [0.00127981 0.00303598 0.00490412 ... 0.09651608 0.00766317 0.00263673]
 [0.02316293 0.00441233 0.01250196 ... 0.17195582 0.01052809 0.00957108]
 ...
 [0.00522991 0.01802342 0.40823418 ... 0.00229443 0.06247446 0.0204697 ]
 [0.03454335 0.1956484  0.03652377 ... 0.00447652 0.16657256 0.09517239]
 [0.01886624 0.03619991 0.07114735 ... 0.00062366 0.09151776 0.01004917]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 50.34 [%]
Global accuracy score (test) = 34.38 [%]
Global F1 score (train) = 51.06 [%]
Global F1 score (test) = 33.33 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.37      0.63      0.47       184
 CAMINAR CON MÓVIL O LIBRO       0.33      0.40      0.36       184
       CAMINAR USUAL SPEED       0.23      0.26      0.24       184
            CAMINAR ZIGZAG       0.30      0.38      0.33       184
          DE PIE BARRIENDO       0.27      0.33      0.30       184
   DE PIE DOBLANDO TOALLAS       0.28      0.20      0.23       184
    DE PIE MOVIENDO LIBROS       0.31      0.20      0.25       184
          DE PIE USANDO PC       0.18      0.35      0.24       184
        FASE REPOSO CON K5       0.53      0.72      0.61       184
INCREMENTAL CICLOERGOMETRO       0.79      0.56      0.66       184
           SENTADO LEYENDO       0.39      0.41      0.40       184
         SENTADO USANDO PC       0.19      0.11      0.14       184
      SENTADO VIENDO LA TV       0.27      0.14      0.19       184
   SUBIR Y BAJAR ESCALERAS       0.23      0.06      0.09       184
                    TROTAR       0.58      0.42      0.49       161

                  accuracy                           0.34      2737
                 macro avg       0.35      0.34      0.33      2737
              weighted avg       0.35      0.34      0.33      2737


Accuracy capturado en la ejecución 28: 34.38 [%]
F1-score capturado en la ejecución 28: 33.33 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
2025-10-31 17:11:04.169875: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-31 17:11:04.181236: 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 00:00:1761927064.194418 1885729 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761927064.198663 1885729 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761927064.208516 1885729 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761927064.208537 1885729 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761927064.208539 1885729 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761927064.208541 1885729 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-31 17:11:04.211788: 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 00:00:1761927066.559255 1885729 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13748 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 1/27
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761927068.469382 1885830 service.cc:152] XLA service 0x786ad8012810 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1761927068.469434 1885830 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-10-31 17:11:08.509517: 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 00:00:1761927068.742723 1885830 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1761927070.636694 1885830 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 2/27

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Epoch 3/27

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Epoch 4/27

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[1m1007/1155[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step - accuracy: 0.1806 - loss: 2.4277
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Epoch 5/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.3125 - loss: 2.2177
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[1m 586/1155[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2097 - loss: 2.3567
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[1m 888/1155[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2096 - loss: 2.3573
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[1m1053/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2099 - loss: 2.3567
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Epoch 6/27

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Epoch 7/27

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Epoch 8/27

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Epoch 9/27

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[1m1080/1155[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step - accuracy: 0.2968 - loss: 2.1126
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Epoch 10/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 21ms/step - accuracy: 0.3750 - loss: 2.0828
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[1m 298/1155[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 2ms/step - accuracy: 0.3244 - loss: 2.0407
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[1m1109/1155[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 2ms/step - accuracy: 0.3266 - loss: 2.0323
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Epoch 11/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 22ms/step - accuracy: 0.4375 - loss: 1.9677
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Epoch 12/27

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Epoch 13/27

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Epoch 14/27

[1m   1/1155[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 20ms/step - accuracy: 0.2500 - loss: 1.8818
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[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:03[0m 1s/step
[1m 51/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step  
[1m109/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 936us/step
[1m166/578[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 919us/step
[1m221/578[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 917us/step
[1m283/578[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 895us/step
[1m341/578[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 891us/step
[1m401/578[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 882us/step
[1m458/578[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 882us/step
[1m520/578[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 873us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m57/86[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 904us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 55/158[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 938us/step
[1m115/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 885us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 35.93 [%]
Global F1 score (validation) = 35.54 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[1.6048262e-03 9.7810256e-04 2.3012487e-03 ... 5.9315953e-02
  1.6594529e-03 2.6877434e-03]
 [1.8242823e-03 9.7601698e-04 2.5172229e-03 ... 9.7650208e-02
  1.7304951e-03 3.0550268e-03]
 [7.2274774e-02 1.4758606e-01 4.0819626e-02 ... 5.8511019e-02
  7.9944864e-02 2.4795527e-02]
 ...
 [4.3415385e-03 3.1724966e-01 8.7817740e-03 ... 2.0681922e-03
  3.2590099e-02 1.9666666e-01]
 [6.4341095e-03 1.7450849e-02 9.1322295e-02 ... 4.2194049e-04
  1.7058374e-02 1.7333959e-03]
 [1.2641470e-03 3.9886858e-02 6.0161567e-01 ... 5.7890918e-04
  4.1049950e-02 2.8816299e-03]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 52.96 [%]
Global accuracy score (test) = 30.58 [%]
Global F1 score (train) = 53.94 [%]
Global F1 score (test) = 30.05 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.27      0.64      0.38       184
 CAMINAR CON MÓVIL O LIBRO       0.34      0.35      0.34       184
       CAMINAR USUAL SPEED       0.31      0.24      0.28       184
            CAMINAR ZIGZAG       0.20      0.14      0.17       184
          DE PIE BARRIENDO       0.23      0.24      0.23       184
   DE PIE DOBLANDO TOALLAS       0.18      0.16      0.17       184
    DE PIE MOVIENDO LIBROS       0.15      0.09      0.11       184
          DE PIE USANDO PC       0.24      0.11      0.15       184
        FASE REPOSO CON K5       0.57      0.68      0.62       184
INCREMENTAL CICLOERGOMETRO       0.69      0.59      0.63       184
           SENTADO LEYENDO       0.37      0.39      0.38       184
         SENTADO USANDO PC       0.13      0.29      0.18       184
      SENTADO VIENDO LA TV       0.24      0.14      0.17       184
   SUBIR Y BAJAR ESCALERAS       0.22      0.16      0.19       184
                    TROTAR       0.72      0.39      0.50       161

                  accuracy                           0.31      2737
                 macro avg       0.33      0.31      0.30      2737
              weighted avg       0.32      0.31      0.30      2737


Accuracy capturado en la ejecución 29: 30.58 [%]
F1-score capturado en la ejecución 29: 30.05 [%]

=== 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.
(2737, 6, 250)
(18469, 6, 250)

[1m  1/578[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12:25[0m 1s/step
[1m 54/578[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 950us/step
[1m115/578[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 884us/step
[1m178/578[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 853us/step
[1m232/578[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 871us/step
[1m293/578[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 865us/step
[1m347/578[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 878us/step
[1m405/578[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 877us/step
[1m463/578[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 875us/step
[1m523/578[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 871us/step
[1m577/578[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 879us/step
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step  
[1m578/578[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 3ms/step

[1m 1/86[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m54/86[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 949us/step
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m86/86[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step

[1m  1/158[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step
[1m 57/158[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 893us/step
[1m114/158[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 887us/step
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step  
[1m158/158[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
Global accuracy score (validation) = 33.53 [%]
Global F1 score (validation) = 32.76 [%]
[[8.]
 [8.]
 [8.]
 ...
 [3.]
 [3.]
 [3.]]
(2737, 1)
[[0.00823099 0.00681663 0.01143599 ... 0.16533458 0.00934405 0.0056781 ]
 [0.00741418 0.00572877 0.0070139  ... 0.1763284  0.00745294 0.00713801]
 [0.00762374 0.00618869 0.00827367 ... 0.18585812 0.00688697 0.0048867 ]
 ...
 [0.02068346 0.11513538 0.03548441 ... 0.00396318 0.23377873 0.0197486 ]
 [0.01421851 0.13296129 0.03395705 ... 0.00051542 0.22466196 0.00779044]
 [0.03608681 0.03847524 0.06548405 ... 0.00319563 0.16302663 0.00254108]]
(2737, 15)
-------------------------------------------------

Global accuracy score (train) = 48.22 [%]
Global accuracy score (test) = 31.97 [%]
Global F1 score (train) = 49.04 [%]
Global F1 score (test) = 31.66 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.30      0.62      0.40       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.40      0.35       184
       CAMINAR USUAL SPEED       0.38      0.24      0.29       184
            CAMINAR ZIGZAG       0.27      0.29      0.28       184
          DE PIE BARRIENDO       0.28      0.27      0.28       184
   DE PIE DOBLANDO TOALLAS       0.23      0.18      0.20       184
    DE PIE MOVIENDO LIBROS       0.26      0.17      0.20       184
          DE PIE USANDO PC       0.19      0.51      0.28       184
        FASE REPOSO CON K5       0.78      0.34      0.47       184
INCREMENTAL CICLOERGOMETRO       0.80      0.58      0.67       184
           SENTADO LEYENDO       0.31      0.47      0.37       184
         SENTADO USANDO PC       0.12      0.08      0.09       184
      SENTADO VIENDO LA TV       0.43      0.12      0.19       184
   SUBIR Y BAJAR ESCALERAS       0.15      0.09      0.11       184
                    TROTAR       0.69      0.47      0.56       161

                  accuracy                           0.32      2737
                 macro avg       0.37      0.32      0.32      2737
              weighted avg       0.36      0.32      0.31      2737


Accuracy capturado en la ejecución 30: 31.97 [%]
F1-score capturado en la ejecución 30: 31.66 [%]

=== RESUMEN FINAL ===
Accuracies: [31.75, 33.69, 33.18, 31.53, 34.97, 32.37, 30.4, 31.46, 32.88, 32.01, 33.94, 31.31, 34.09, 32.55, 32.66, 32.81, 31.75, 32.41, 32.92, 30.84, 31.24, 34.71, 31.53, 29.92, 32.33, 31.53, 33.83, 34.38, 30.58, 31.97]
F1-scores: [31.53, 32.53, 33.48, 29.2, 34.31, 32.29, 29.58, 30.97, 31.72, 30.89, 32.89, 31.83, 32.09, 32.52, 31.19, 31.88, 30.58, 30.85, 31.78, 28.71, 29.09, 34.89, 31.07, 29.27, 31.5, 28.93, 32.93, 33.33, 30.05, 31.66]
Accuracy mean: 32.3847 | std: 1.2771
F1 mean: 31.4513 | std: 1.5545

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_M/case_M_CAPTURE24_acc_gyr_17_classes/metrics_test.npz
