2025-11-07 17:18:47.762088: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:18:47.774039: 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 00:00:1762532327.788003 3375578 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762532327.792219 3375578 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762532327.802852 3375578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532327.802871 3375578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532327.802873 3375578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532327.802875 3375578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:18:47.806035: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-07 17:18:50,782	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-07 17:18:51,431	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-07 17:18:51,496	INFO trial.py:182 -- Creating a new dirname dir_72b58_35f1 because trial dirname 'dir_72b58' already exists.
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2025-11-07 17:18:51,503	INFO trial.py:182 -- Creating a new dirname dir_72b58_6248 because trial dirname 'dir_72b58' already exists.
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2025-11-07 17:18:51,518	INFO trial.py:182 -- Creating a new dirname dir_72b58_6225 because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,521	INFO trial.py:182 -- Creating a new dirname dir_72b58_2f98 because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,524	INFO trial.py:182 -- Creating a new dirname dir_72b58_bc01 because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,527	INFO trial.py:182 -- Creating a new dirname dir_72b58_2577 because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,531	INFO trial.py:182 -- Creating a new dirname dir_72b58_3d4c because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,534	INFO trial.py:182 -- Creating a new dirname dir_72b58_2b7c because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,537	INFO trial.py:182 -- Creating a new dirname dir_72b58_0412 because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,541	INFO trial.py:182 -- Creating a new dirname dir_72b58_267a because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,545	INFO trial.py:182 -- Creating a new dirname dir_72b58_2a56 because trial dirname 'dir_72b58' already exists.
2025-11-07 17:18:51,553	INFO trial.py:182 -- Creating a new dirname dir_72b58_dd7d because trial dirname 'dir_72b58' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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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_C/case_C_CAPTURE24_acc_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-07_17-18-50_063185_3375578/artifacts/2025-11-07_17-18-51/CAPTURE24_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-07 17:18:51. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_72b58    PENDING            3   adam            relu                                   32                 64                  3                 1          0.000135481         28 │
│ trial_72b58    PENDING            3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29 │
│ trial_72b58    PENDING            3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18 │
│ trial_72b58    PENDING            3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23 │
│ trial_72b58    PENDING            2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22 │
│ trial_72b58    PENDING            3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18 │
│ trial_72b58    PENDING            3   adam            tanh                                   16                 64                  3                 0          0.000149465         17 │
│ trial_72b58    PENDING            3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20 │
│ trial_72b58    PENDING            2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28 │
│ trial_72b58    PENDING            2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18 │
│ trial_72b58    PENDING            2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27 │
│ trial_72b58    PENDING            3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18 │
│ trial_72b58    PENDING            2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24 │
│ trial_72b58    PENDING            3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21 │
│ trial_72b58    PENDING            3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22 │
│ trial_72b58    PENDING            2   adam            relu                                   32                 32                  5                 1          0.00013253          28 │
│ trial_72b58    PENDING            2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22 │
│ trial_72b58    PENDING            2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17 │
│ trial_72b58    PENDING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28 │
│ trial_72b58    PENDING            2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            17 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00015 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            22 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            28 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00014 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            19 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            18 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            28 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00014 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            28 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00013 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            18 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            27 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            24 │
│ funcion_activacion              tanh │
│ num_resblocks                      1 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
[36m(train_cnn_ray_tune pid=3377202)[0m 2025-11-07 17:18:54.781224: 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=3377202)[0m 2025-11-07 17:18:54.801239: 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=3377202)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=3377202)[0m E0000 00:00:1762532334.827440 3378341 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=3377202)[0m E0000 00:00:1762532334.834741 3378341 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=3377202)[0m W0000 00:00:1762532334.853122 3378341 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=3377202)[0m W0000 00:00:1762532334.853166 3378341 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=3377202)[0m W0000 00:00:1762532334.853170 3378341 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=3377202)[0m W0000 00:00:1762532334.853172 3378341 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=3377202)[0m 2025-11-07 17:18:54.859673: 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=3377202)[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=3377202)[0m 2025-11-07 17:18:58.042610: 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=3377202)[0m 2025-11-07 17:18:58.042659: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=3377202)[0m 2025-11-07 17:18:58.042669: 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=3377202)[0m 2025-11-07 17:18:58.042674: 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=3377202)[0m 2025-11-07 17:18:58.042680: 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=3377202)[0m 2025-11-07 17:18:58.042683: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=3377202)[0m 2025-11-07 17:18:58.042896: 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=3377202)[0m 2025-11-07 17:18:58.042930: 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=3377202)[0m 2025-11-07 17:18:58.042935: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            18 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00005 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            22 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00008 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 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             0.00007 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            21 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            20 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            17 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00009 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            29 │
│ funcion_activacion              relu │
│ num_resblocks                      0 │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            22 │
│ funcion_activacion              relu │
│ num_resblocks                      1 │
│ numero_filtros                    16 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  16 │
│ tasa_aprendizaje             0.00013 │
╰──────────────────────────────────────╯
Trial trial_72b58 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_72b58 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            23 │
│ funcion_activacion              tanh │
│ num_resblocks                      0 │
│ numero_filtros                    16 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00003 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377202)[0m Epoch 1/17
[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34:59[0m 3s/step - accuracy: 0.3125 - loss: 2.1827
[1m  4/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 24ms/step - accuracy: 0.2917 - loss: 1.9652
[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m34:31[0m 3s/step - accuracy: 0.0000e+00 - loss: 2.4798
[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m  7/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 23ms/step - accuracy: 0.2867 - loss: 1.9269
[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m  4/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 23ms/step - accuracy: 0.1068 - loss: 2.1807    
[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m 10/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 21ms/step - accuracy: 0.2765 - loss: 1.9050
[1m 12/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 22ms/step - accuracy: 0.2707 - loss: 1.8963
[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.2668 - loss: 2.0137
[1m 12/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.2641 - loss: 1.9901
[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2625 - loss: 1.9740
[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m 16/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.2638 - loss: 1.9544
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2652 - loss: 1.9376
[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.2434 - loss: 1.9458 
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.2450 - loss: 1.9413
[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2669 - loss: 1.9231
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2694 - loss: 1.9104
[36m(train_cnn_ray_tune pid=3377211)[0m Epoch 1/22[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=3377202)[0m 
[1m  2/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m42s[0m 64ms/step - accuracy: 0.2500 - loss: 2.1203  
[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m137/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 28ms/step - accuracy: 0.2870 - loss: 1.7548
[1m138/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 28ms/step - accuracy: 0.2872 - loss: 1.7544
[1m141/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m14s[0m 28ms/step - accuracy: 0.2876 - loss: 1.7530
[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m137/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m6s[0m 32ms/step - accuracy: 0.3444 - loss: 1.6526
[1m139/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m6s[0m 32ms/step - accuracy: 0.3450 - loss: 1.6506
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m6s[0m 33ms/step - accuracy: 0.3453 - loss: 1.6496
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35:02[0m 6s/step - accuracy: 0.1875 - loss: 2.1185
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 66ms/step - accuracy: 0.2344 - loss: 2.0390[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3377191)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m36:48[0m 7s/step - accuracy: 0.3125 - loss: 1.6997[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3377209)[0m 
[1m112/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m17s[0m 32ms/step - accuracy: 0.2546 - loss: 1.8951[32m [repeated 226x across cluster][0m
[36m(train_cnn_ray_tune pid=3377204)[0m 
[1m181/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m13s[0m 27ms/step - accuracy: 0.2883 - loss: 1.7391
[1m183/665[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m13s[0m 27ms/step - accuracy: 0.2885 - loss: 1.7379[32m [repeated 280x across cluster][0m
[36m(train_cnn_ray_tune pid=3377207)[0m 
[1m235/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.2858 - loss: 1.8266
[1m237/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 23ms/step - accuracy: 0.2860 - loss: 1.8256[32m [repeated 80x across cluster][0m
[36m(train_cnn_ray_tune pid=3377206)[0m 
[1m 94/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m9s[0m 38ms/step - accuracy: 0.2884 - loss: 1.7790[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=3377207)[0m 
[1m163/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 20ms/step - accuracy: 0.2800 - loss: 1.8591 
[1m166/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 20ms/step - accuracy: 0.2802 - loss: 1.8577[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
[1m144/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m20s[0m 39ms/step - accuracy: 0.2988 - loss: 1.7584
[1m145/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m20s[0m 39ms/step - accuracy: 0.2989 - loss: 1.7576
[1m147/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m20s[0m 39ms/step - accuracy: 0.2993 - loss: 1.7559[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 32ms/step - accuracy: 0.2814 - loss: 1.7848
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 41ms/step - accuracy: 0.2851 - loss: 1.7722 - val_accuracy: 0.3936 - val_loss: 1.2907
[36m(train_cnn_ray_tune pid=3377213)[0m Epoch 2/19
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m135/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m13s[0m 66ms/step - accuracy: 0.3339 - loss: 1.6648
[1m136/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m12s[0m 66ms/step - accuracy: 0.3342 - loss: 1.6637
[1m137/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m12s[0m 66ms/step - accuracy: 0.3345 - loss: 1.6626
[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m51s[0m 155ms/step - accuracy: 0.4688 - loss: 1.2613
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m 41/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3920 - loss: 1.4696
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[36m(train_cnn_ray_tune pid=3377207)[0m 
[1m594/665[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 26ms/step - accuracy: 0.3134 - loss: 1.7112
[1m596/665[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 26ms/step - accuracy: 0.3135 - loss: 1.7107[32m [repeated 199x across cluster][0m
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 116ms/step - accuracy: 0.5312 - loss: 1.1322
[36m(train_cnn_ray_tune pid=3377215)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 70ms/step - accuracy: 0.4844 - loss: 1.2092 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m453/665[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m7s[0m 36ms/step - accuracy: 0.3158 - loss: 1.6725
[1m454/665[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m7s[0m 36ms/step - accuracy: 0.3159 - loss: 1.6723
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[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m19s[0m 47ms/step - accuracy: 0.3802 - loss: 1.5349 - val_accuracy: 0.4392 - val_loss: 1.2252[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377208)[0m Epoch 2/28[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377201)[0m 
[1m415/665[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m9s[0m 40ms/step - accuracy: 0.3792 - loss: 1.5403 
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Trial status: 20 RUNNING
Current time: 2025-11-07 17:19:21. 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_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          0.000135481         28 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   16                 64                  3                 0          0.000149465         17 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28 │
│ trial_72b58    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18 │
│ trial_72b58    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22 │
│ trial_72b58    RUNNING            2   adam            relu                                   32                 32                  5                 1          0.00013253          28 │
│ trial_72b58    RUNNING            2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m Epoch 2/23[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m30s[0m 41ms/step - accuracy: 0.3275 - loss: 1.6386 - val_accuracy: 0.4198 - val_loss: 1.2413[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3377205)[0m Epoch 2/22[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m Epoch 3/18[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m Epoch 3/23[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m Epoch 4/19[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m Epoch 3/28[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 17:19:51. 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_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          0.000135481         28 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   16                 64                  3                 0          0.000149465         17 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28 │
│ trial_72b58    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18 │
│ trial_72b58    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22 │
│ trial_72b58    RUNNING            2   adam            relu                                   32                 32                  5                 1          0.00013253          28 │
│ trial_72b58    RUNNING            2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m Epoch 5/19[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m Epoch 3/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m Epoch 5/18[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m Epoch 4/29[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m Epoch 4/24[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m Epoch 6/28[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 17:20:21. 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_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          0.000135481         28 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   16                 64                  3                 0          0.000149465         17 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28 │
│ trial_72b58    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18 │
│ trial_72b58    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22 │
│ trial_72b58    RUNNING            2   adam            relu                                   32                 32                  5                 1          0.00013253          28 │
│ trial_72b58    RUNNING            2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m Epoch 4/22[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m Epoch 4/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m Epoch 7/28[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m Epoch 5/24[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m Epoch 4/18[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m Epoch 8/18[32m [repeated 4x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-11-07 17:20:51. 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_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          0.000135481         28 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   16                 64                  3                 0          0.000149465         17 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28 │
│ trial_72b58    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18 │
│ trial_72b58    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22 │
│ trial_72b58    RUNNING            2   adam            relu                                   32                 32                  5                 1          0.00013253          28 │
│ trial_72b58    RUNNING            2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 55ms/step - accuracy: 0.4168 - loss: 1.3596 - val_accuracy: 0.4514 - val_loss: 1.1821[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377192)[0m Epoch 7/18[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m Epoch 5/22[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m Epoch 6/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:27[0m 131ms/step - accuracy: 0.4375 - loss: 1.6082[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377202)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m40s[0m 60ms/step - accuracy: 0.4617 - loss: 1.1776 - val_accuracy: 0.4599 - val_loss: 1.2244
[36m(train_cnn_ray_tune pid=3377202)[0m Epoch 4/17
[36m(train_cnn_ray_tune pid=3377204)[0m 
[1m373/665[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 34ms/step - accuracy: 0.4687 - loss: 1.1753 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m Epoch 9/23
[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m Epoch 10/18[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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Trial status: 20 RUNNING
Current time: 2025-11-07 17:21:21. 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_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          0.000135481         28 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18 │
│ trial_72b58    RUNNING            3   adam            tanh                                   16                 64                  3                 0          0.000149465         17 │
│ trial_72b58    RUNNING            3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28 │
│ trial_72b58    RUNNING            2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18 │
│ trial_72b58    RUNNING            2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24 │
│ trial_72b58    RUNNING            3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21 │
│ trial_72b58    RUNNING            3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22 │
│ trial_72b58    RUNNING            2   adam            relu                                   32                 32                  5                 1          0.00013253          28 │
│ trial_72b58    RUNNING            2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17 │
│ trial_72b58    RUNNING            2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28 │
│ trial_72b58    RUNNING            2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m195/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m3s[0m 28ms/step - accuracy: 0.4906 - loss: 1.1375
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m Epoch 6/28[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m Epoch 7/27[32m [repeated 7x across cluster][0m
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[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=3377206)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377200)[0m 2025-11-07 17:18:55.256261: 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=3377200)[0m 2025-11-07 17:18:55.284556: 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=3377200)[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=3377200)[0m E0000 00:00:1762532335.312563 3378474 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=3377200)[0m E0000 00:00:1762532335.321602 3378474 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=3377200)[0m W0000 00:00:1762532335.341822 3378474 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=3377200)[0m 2025-11-07 17:18:55.347890: 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=3377200)[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=3377200)[0m 2025-11-07 17:18:58.586182: 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=3377200)[0m 2025-11-07 17:18:58.586268: 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=3377200)[0m 2025-11-07 17:18:58.586277: 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=3377200)[0m 2025-11-07 17:18:58.586283: 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=3377200)[0m 2025-11-07 17:18:58.586289: 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=3377200)[0m 2025-11-07 17:18:58.586293: 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=3377200)[0m 2025-11-07 17:18:58.586689: 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=3377200)[0m 2025-11-07 17:18:58.586750: 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=3377200)[0m 2025-11-07 17:18:58.586754: 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=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377206)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:21:32. Total running time: 2min 41s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             158.069 │
│ time_total_s                 158.069 │
│ training_iteration                 1 │
│ val_accuracy                 0.45401 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:21:32. Total running time: 2min 41s
[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m Epoch 11/28[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m Epoch 9/17[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[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=3377208)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m44/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 15ms/step
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m78/96[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3377194)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 39ms/step - accuracy: 0.4429 - loss: 1.2394 - val_accuracy: 0.4428 - val_loss: 1.1730[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377194)[0m Epoch 6/20[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377208)[0m 
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[36m(train_cnn_ray_tune pid=3377208)[0m 
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[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step

Trial trial_72b58 finished iteration 1 at 2025-11-07 17:21:45. Total running time: 2min 53s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             170.478 │
│ time_total_s                 170.478 │
│ training_iteration                 1 │
│ val_accuracy                 0.48653 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:21:45. Total running time: 2min 53s
[36m(train_cnn_ray_tune pid=3377208)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3377203)[0m 
[1m  3/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 42ms/step - accuracy: 0.4097 - loss: 1.4439  
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 88ms/step - accuracy: 0.4688 - loss: 1.2445[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377192)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 49ms/step - accuracy: 0.6424 - loss: 0.9800 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 47ms/step - accuracy: 0.6135 - loss: 1.0124
[36m(train_cnn_ray_tune pid=3377202)[0m 
[1m156/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m25s[0m 50ms/step - accuracy: 0.4932 - loss: 1.1065[32m [repeated 146x across cluster][0m
[36m(train_cnn_ray_tune pid=3377194)[0m 
[1m144/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m22s[0m 42ms/step - accuracy: 0.4278 - loss: 1.2579
[1m146/665[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m21s[0m 42ms/step - accuracy: 0.4279 - loss: 1.2576[32m [repeated 110x across cluster][0m
[36m(train_cnn_ray_tune pid=3377207)[0m 
[1m509/665[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.4695 - loss: 1.1732
[1m511/665[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.4695 - loss: 1.1731[32m [repeated 232x across cluster][0m
[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m375/665[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 34ms/step - accuracy: 0.4295 - loss: 1.2569 
[1m377/665[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 34ms/step - accuracy: 0.4296 - loss: 1.2568[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m  5/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 35ms/step - accuracy: 0.4754 - loss: 1.4037
[1m  6/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 38ms/step - accuracy: 0.4569 - loss: 1.4210
[1m  7/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 41ms/step - accuracy: 0.4452 - loss: 1.4255
[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m453/665[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m7s[0m 34ms/step - accuracy: 0.4322 - loss: 1.2525[32m [repeated 221x across cluster][0m
[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m24s[0m 36ms/step - accuracy: 0.3606 - loss: 1.5028 - val_accuracy: 0.4064 - val_loss: 1.2470[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377210)[0m Epoch 7/18[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377207)[0m 
[1m610/665[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 25ms/step - accuracy: 0.4706 - loss: 1.1701
[1m612/665[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 25ms/step - accuracy: 0.4707 - loss: 1.1700
[1m615/665[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m1s[0m 25ms/step - accuracy: 0.4707 - loss: 1.1699
[36m(train_cnn_ray_tune pid=3377201)[0m 
[1m  3/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 43ms/step - accuracy: 0.5208 - loss: 1.1667  
[1m  5/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 40ms/step - accuracy: 0.5375 - loss: 1.1306
[36m(train_cnn_ray_tune pid=3377201)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1:11[0m 108ms/step - accuracy: 0.4375 - loss: 1.2940[32m [repeated 7x across cluster][0m

Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-07 17:21:51. Total running time: 3min 0s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          0.000135481         28                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29                                              │
│ trial_72b58    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18                                              │
│ trial_72b58    RUNNING              3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18                                              │
│ trial_72b58    RUNNING              3   adam            tanh                                   16                 64                  3                 0          0.000149465         17                                              │
│ trial_72b58    RUNNING              3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20                                              │
│ trial_72b58    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22                                              │
│ trial_72b58    RUNNING              2   adam            relu                                   32                 32                  5                 1          0.00013253          28                                              │
│ trial_72b58    RUNNING              2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22                                              │
│ trial_72b58    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28                                              │
│ trial_72b58    RUNNING              2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19                                              │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28        1            170.478         0.486531 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18        1            158.069         0.454008 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m Epoch 10/17[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m Epoch 7/22[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m31s[0m 96ms/step - accuracy: 0.4688 - loss: 1.0017
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 52ms/step - accuracy: 0.4584 - loss: 1.2167 - val_accuracy: 0.4652 - val_loss: 1.1508[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377192)[0m Epoch 11/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377201)[0m 
[1m384/665[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m9s[0m 36ms/step - accuracy: 0.5215 - loss: 1.0580 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 79ms/step - accuracy: 0.6562 - loss: 0.8994
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m Epoch 13/23
[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m22s[0m 34ms/step - accuracy: 0.3744 - loss: 1.4517 - val_accuracy: 0.4182 - val_loss: 1.2420
[36m(train_cnn_ray_tune pid=3377212)[0m Epoch 9/24
[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m47s[0m 72ms/step - accuracy: 0.2500 - loss: 1.6271
[36m(train_cnn_ray_tune pid=3377204)[0m 
[1m250/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m12s[0m 30ms/step - accuracy: 0.5062 - loss: 1.0809
[1m252/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m12s[0m 30ms/step - accuracy: 0.5061 - loss: 1.0809
[1m254/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m12s[0m 30ms/step - accuracy: 0.5061 - loss: 1.0810
[36m(train_cnn_ray_tune pid=3377192)[0m 
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m8s[0m 46ms/step - accuracy: 0.4572 - loss: 1.2234
[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m8s[0m 46ms/step - accuracy: 0.4571 - loss: 1.2235
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m8s[0m 46ms/step - accuracy: 0.4571 - loss: 1.2236
[36m(train_cnn_ray_tune pid=3377204)[0m 
[1m324/665[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.5050 - loss: 1.0820 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m52s[0m 80ms/step - accuracy: 0.2500 - loss: 1.4486
[1m  3/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 39ms/step - accuracy: 0.3403 - loss: 1.3820[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 39ms/step - accuracy: 0.3793 - loss: 1.4737 - val_accuracy: 0.4060 - val_loss: 1.2379[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3377210)[0m Epoch 8/18[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 73ms/step - accuracy: 0.4375 - loss: 1.2870[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m 94/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m7s[0m 29ms/step - accuracy: 0.5177 - loss: 1.0876
[1m 96/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m6s[0m 29ms/step - accuracy: 0.5177 - loss: 1.0874
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[36m(train_cnn_ray_tune pid=3377202)[0m 
[1m  3/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 42ms/step - accuracy: 0.5139 - loss: 1.1337  
[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m 57/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 33ms/step - accuracy: 0.3523 - loss: 1.4835
[1m 59/665[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 33ms/step - accuracy: 0.3535 - loss: 1.4828
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[36m(train_cnn_ray_tune pid=3377191)[0m 
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m8s[0m 44ms/step - accuracy: 0.5107 - loss: 1.1552
[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m8s[0m 44ms/step - accuracy: 0.5106 - loss: 1.1553
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m105/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m19s[0m 34ms/step - accuracy: 0.3673 - loss: 1.4805
[1m107/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m18s[0m 34ms/step - accuracy: 0.3677 - loss: 1.4804[32m [repeated 191x across cluster][0m
[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m309/665[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.3689 - loss: 1.4457 
[1m311/665[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.3689 - loss: 1.4456
[36m(train_cnn_ray_tune pid=3377207)[0m 
[1m285/665[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m8s[0m 23ms/step - accuracy: 0.4587 - loss: 1.1403
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m26s[0m 40ms/step - accuracy: 0.4773 - loss: 1.1426 - val_accuracy: 0.4501 - val_loss: 1.2040[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m Epoch 8/22[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377193)[0m 
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Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-07 17:22:21. Total running time: 3min 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_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          0.000135481         28                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29                                              │
│ trial_72b58    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18                                              │
│ trial_72b58    RUNNING              3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18                                              │
│ trial_72b58    RUNNING              3   adam            tanh                                   16                 64                  3                 0          0.000149465         17                                              │
│ trial_72b58    RUNNING              3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20                                              │
│ trial_72b58    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22                                              │
│ trial_72b58    RUNNING              2   adam            relu                                   32                 32                  5                 1          0.00013253          28                                              │
│ trial_72b58    RUNNING              2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22                                              │
│ trial_72b58    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28                                              │
│ trial_72b58    RUNNING              2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19                                              │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28        1            170.478         0.486531 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18        1            158.069         0.454008 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m Epoch 17/19[32m [repeated 5x across cluster][0m
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[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=3377204)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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[36m(train_cnn_ray_tune pid=3377204)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:22:28. Total running time: 3min 37s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             214.486 │
│ time_total_s                 214.486 │
│ training_iteration                 1 │
│ val_accuracy                  0.4435 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:22:28. Total running time: 3min 37s
[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m Epoch 12/17[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m Epoch 10/27[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m Epoch 9/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 27ms/step
[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[0m 
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[36m(train_cnn_ray_tune pid=3377194)[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=3377194)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:22:44. Total running time: 3min 53s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             230.373 │
│ time_total_s                 230.373 │
│ training_iteration                 1 │
│ val_accuracy                 0.44251 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:22:44. Total running time: 3min 53s
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m Epoch 11/21[32m [repeated 6x across cluster][0m
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[1m45/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 20ms/step
[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 101ms/step - accuracy: 0.5000 - loss: 1.1079
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Trial status: 16 RUNNING | 4 TERMINATED
Current time: 2025-11-07 17:22:52. Total running time: 4min 0s
Logical resource usage: 16.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          0.000135481         28                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29                                              │
│ trial_72b58    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18                                              │
│ trial_72b58    RUNNING              3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18                                              │
│ trial_72b58    RUNNING              3   adam            tanh                                   16                 64                  3                 0          0.000149465         17                                              │
│ trial_72b58    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22                                              │
│ trial_72b58    RUNNING              2   adam            relu                                   32                 32                  5                 1          0.00013253          28                                              │
│ trial_72b58    RUNNING              2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22                                              │
│ trial_72b58    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17                                              │
│ trial_72b58    RUNNING              2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19                                              │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20        1            230.373         0.44251  │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28        1            170.478         0.486531 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18        1            158.069         0.454008 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28        1            214.486         0.443495 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
[1m42/96[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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[36m(train_cnn_ray_tune pid=3377202)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:22:54. Total running time: 4min 3s
[36m(train_cnn_ray_tune pid=3377202)[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=3377202)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             240.238 │
│ time_total_s                 240.238 │
│ training_iteration                 1 │
│ val_accuracy                 0.46025 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377202)[0m 
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Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:22:54. Total running time: 4min 3s
[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m Epoch 14/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
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[36m(train_cnn_ray_tune pid=3377215)[0m 
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[1m42/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 15ms/step
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 48ms/step
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:22:59. Total running time: 4min 8s
[36m(train_cnn_ray_tune pid=3377215)[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=3377215)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377213)[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=3377213)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              245.38 │
│ time_total_s                  245.38 │
│ training_iteration                 1 │
│ val_accuracy                 0.48587 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:22:59. Total running time: 4min 8s

Trial trial_72b58 finished iteration 1 at 2025-11-07 17:23:00. Total running time: 4min 8s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             245.696 │
│ time_total_s                 245.696 │
│ training_iteration                 1 │
│ val_accuracy                 0.50329 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:23:00. Total running time: 4min 8s
[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 31ms/step - accuracy: 0.5178 - loss: 1.0497 - val_accuracy: 0.5033 - val_loss: 1.1359[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377191)[0m Epoch 13/29
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377213)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step[32m [repeated 29x across cluster][0m
[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m Epoch 10/22[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
[1m239/665[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m10s[0m 25ms/step - accuracy: 0.4893 - loss: 1.1112
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[36m(train_cnn_ray_tune pid=3377200)[0m Epoch 18/23[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m Epoch 15/17[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.4627 - loss: 1.2848
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 381ms/step
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m 4/49[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 18ms/step  
[1m 8/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m11/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m15/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
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[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m22/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m27/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 27ms/step - accuracy: 0.4002 - loss: 1.4101 - val_accuracy: 0.4146 - val_loss: 1.2032[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m30/49[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 15ms/step
[1m34/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m38/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 15ms/step
[1m42/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m45/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3377207)[0m 
[1m390/665[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m4s[0m 16ms/step - accuracy: 0.5033 - loss: 1.0786
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[36m(train_cnn_ray_tune pid=3377200)[0m Epoch 19/23[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377193)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 23ms/step
[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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[36m(train_cnn_ray_tune pid=3377193)[0m 
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Trial status: 13 RUNNING | 7 TERMINATED
Current time: 2025-11-07 17:23:22. Total running time: 4min 30s
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_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          0.000135481         28                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29                                              │
│ trial_72b58    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18                                              │
│ trial_72b58    RUNNING              3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18                                              │
│ trial_72b58    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22                                              │
│ trial_72b58    RUNNING              2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22                                              │
│ trial_72b58    RUNNING              2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17                                              │
│ trial_72b58    TERMINATED           3   adam            tanh                                   16                 64                  3                 0          0.000149465         17        1            240.238         0.46025  │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20        1            230.373         0.44251  │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28        1            170.478         0.486531 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18        1            158.069         0.454008 │
│ trial_72b58    TERMINATED           2   adam            relu                                   32                 32                  5                 1          0.00013253          28        1            245.38          0.485874 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28        1            214.486         0.443495 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19        1            245.696         0.503285 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377193)[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=3377193)[0m   _log_deprecation_warning(
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:23:23. Total running time: 4min 31s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             268.711 │
│ time_total_s                 268.711 │
│ training_iteration                 1 │
│ val_accuracy                 0.49146 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:23:23. Total running time: 4min 31s
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m Epoch 17/18[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m16s[0m 24ms/step - accuracy: 0.3962 - loss: 1.3879 - val_accuracy: 0.4225 - val_loss: 1.1853
[36m(train_cnn_ray_tune pid=3377210)[0m Epoch 12/18[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
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[1m646/665[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step - accuracy: 0.5061 - loss: 1.0803
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m48s[0m 74ms/step - accuracy: 0.3750 - loss: 1.3307[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m14s[0m 20ms/step - accuracy: 0.4242 - loss: 1.2518 - val_accuracy: 0.4553 - val_loss: 1.1771[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3377209)[0m Epoch 14/27[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m Epoch 22/23[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377207)[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=3377207)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377207)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:23:49. Total running time: 4min 58s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             295.352 │
│ time_total_s                 295.352 │
│ training_iteration                 1 │
│ val_accuracy                 0.49967 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:23:49. Total running time: 4min 58s
[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m Epoch 13/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377192)[0m 
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[1m25/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
[1m35/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m40/96[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 11ms/step

Trial status: 9 TERMINATED | 11 RUNNING
Current time: 2025-11-07 17:23:52. Total running time: 5min 0s
Logical resource usage: 11.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29                                              │
│ trial_72b58    RUNNING              3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18                                              │
│ trial_72b58    RUNNING              3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18                                              │
│ trial_72b58    RUNNING              2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22                                              │
│ trial_72b58    RUNNING              2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22                                              │
│ trial_72b58    TERMINATED           3   adam            relu                                   32                 64                  3                 1          0.000135481         28        1            268.711         0.491459 │
│ trial_72b58    TERMINATED           3   adam            tanh                                   16                 64                  3                 0          0.000149465         17        1            240.238         0.46025  │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20        1            230.373         0.44251  │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28        1            170.478         0.486531 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18        1            158.069         0.454008 │
│ trial_72b58    TERMINATED           2   adam            relu                                   32                 32                  5                 1          0.00013253          28        1            245.38          0.485874 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17        1            295.352         0.499671 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28        1            214.486         0.443495 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19        1            245.696         0.503285 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377192)[0m 
[1m45/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m50/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3377214)[0m 
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 26ms/step - accuracy: 0.4977 - loss: 1.2226
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m6s[0m 26ms/step - accuracy: 0.4973 - loss: 1.2235
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[36m(train_cnn_ray_tune pid=3377192)[0m 
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[1m79/96[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 11ms/step
[1m84/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3377192)[0m 
[1m89/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 11ms/step
[1m94/96[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 11ms/step

Trial trial_72b58 finished iteration 1 at 2025-11-07 17:23:52. Total running time: 5min 1s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             298.333 │
│ time_total_s                 298.333 │
│ training_iteration                 1 │
│ val_accuracy                 0.49179 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377192)[0m 
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Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:23:52. Total running time: 5min 1s
[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m Epoch 13/22[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m Epoch 12/18[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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[36m(train_cnn_ray_tune pid=3377200)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:24:03. Total running time: 5min 12s
[36m(train_cnn_ray_tune pid=3377200)[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=3377200)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             309.029 │
│ time_total_s                 309.029 │
│ training_iteration                 1 │
│ val_accuracy                 0.44645 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:24:03. Total running time: 5min 12s
[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m Epoch 16/27[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[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=3377201)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377201)[0m 
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[36m(train_cnn_ray_tune pid=3377201)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:24:09. Total running time: 5min 18s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             315.357 │
│ time_total_s                 315.357 │
│ training_iteration                 1 │
│ val_accuracy                 0.46222 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:24:09. Total running time: 5min 18s
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m Epoch 18/21[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m Epoch 19/21[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[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=3377209)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:24:17. Total running time: 5min 26s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             323.341 │
│ time_total_s                 323.341 │
│ training_iteration                 1 │
│ val_accuracy                 0.45269 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:24:17. Total running time: 5min 26s
[36m(train_cnn_ray_tune pid=3377209)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m Epoch 15/22

Trial status: 13 TERMINATED | 7 RUNNING
Current time: 2025-11-07 17:24:22. Total running time: 5min 30s
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_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24                                              │
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22                                              │
│ trial_72b58    TERMINATED           3   adam            relu                                   32                 64                  3                 1          0.000135481         28        1            268.711         0.491459 │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18        1            298.333         0.491787 │
│ trial_72b58    TERMINATED           3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23        1            309.029         0.446452 │
│ trial_72b58    TERMINATED           3   adam            tanh                                   16                 64                  3                 0          0.000149465         17        1            240.238         0.46025  │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20        1            230.373         0.44251  │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28        1            170.478         0.486531 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18        1            158.069         0.454008 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27        1            323.341         0.452694 │
│ trial_72b58    TERMINATED           2   adam            relu                                   32                 32                  5                 1          0.00013253          28        1            245.38          0.485874 │
│ trial_72b58    TERMINATED           2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22        1            315.357         0.462221 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17        1            295.352         0.499671 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28        1            214.486         0.443495 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19        1            245.696         0.503285 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m355/665[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 13ms/step - accuracy: 0.4088 - loss: 1.3685
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[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 13ms/step - accuracy: 0.3887 - loss: 1.3616 - val_accuracy: 0.4070 - val_loss: 1.1966
[36m(train_cnn_ray_tune pid=3377212)[0m Epoch 18/24
[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 58ms/step - accuracy: 0.5000 - loss: 1.3234
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m 91/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.4347 - loss: 1.3123
[1m 96/665[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.4334 - loss: 1.3131
[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m101/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.4321 - loss: 1.3140
[1m106/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.4311 - loss: 1.3148
[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m111/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 9ms/step - accuracy: 0.4302 - loss: 1.3154
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[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m38s[0m 58ms/step - accuracy: 0.5625 - loss: 1.0264[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377212)[0m 
[1m121/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 10ms/step - accuracy: 0.4284 - loss: 1.3171
[1m126/665[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m5s[0m 10ms/step - accuracy: 0.4275 - loss: 1.3180
[36m(train_cnn_ray_tune pid=3377205)[0m 
[1m  5/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 13ms/step - accuracy: 0.5281 - loss: 0.9935 
[1m  9/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 14ms/step - accuracy: 0.5138 - loss: 1.0215[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
[1m598/665[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 13ms/step - accuracy: 0.5239 - loss: 1.0402
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[36m(train_cnn_ray_tune pid=3377214)[0m 
[1m119/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 17ms/step - accuracy: 0.4832 - loss: 1.2221[32m [repeated 125x across cluster][0m
[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 15ms/step - accuracy: 0.5240 - loss: 1.0401 - val_accuracy: 0.4859 - val_loss: 1.2206[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m Epoch 16/22[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m40s[0m 62ms/step - accuracy: 0.5625 - loss: 1.0619[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 60ms/step - accuracy: 0.3750 - loss: 1.5947
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m Epoch 19/24[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377205)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:24:34. Total running time: 5min 43s
[36m(train_cnn_ray_tune pid=3377205)[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=3377205)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377214)[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=3377214)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             340.565 │
│ time_total_s                 340.565 │
│ training_iteration                 1 │
│ val_accuracy                 0.46222 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:24:34. Total running time: 5min 43s
[36m(train_cnn_ray_tune pid=3377205)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m Epoch 17/22[32m [repeated 4x across cluster][0m

Trial trial_72b58 finished iteration 1 at 2025-11-07 17:24:38. Total running time: 5min 47s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             344.033 │
│ time_total_s                 344.033 │
│ training_iteration                 1 │
│ val_accuracy                  0.4724 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:24:38. Total running time: 5min 47s
[36m(train_cnn_ray_tune pid=3377214)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m Epoch 18/18[32m [repeated 3x across cluster][0m
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m Epoch 25/29[32m [repeated 4x across cluster][0m
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[36m(train_cnn_ray_tune pid=3377210)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 356ms/step
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[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=3377210)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377210)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:24:49. Total running time: 5min 58s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             355.257 │
│ time_total_s                 355.257 │
│ training_iteration                 1 │
│ val_accuracy                 0.45335 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:24:49. Total running time: 5min 58s
[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
[1m665/665[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 10ms/step - accuracy: 0.4625 - loss: 1.2105 - val_accuracy: 0.4754 - val_loss: 1.2009[32m [repeated 4x across cluster][0m

Trial status: 16 TERMINATED | 4 RUNNING
Current time: 2025-11-07 17:24:52. Total running time: 6min 0s
Logical resource usage: 4.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     num_resblocks     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_72b58    RUNNING              3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18                                              │
│ trial_72b58    RUNNING              2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24                                              │
│ trial_72b58    RUNNING              3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22                                              │
│ trial_72b58    TERMINATED           3   adam            relu                                   32                 64                  3                 1          0.000135481         28        1            268.711         0.491459 │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18        1            298.333         0.491787 │
│ trial_72b58    TERMINATED           3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23        1            309.029         0.446452 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22        1            340.565         0.462221 │
│ trial_72b58    TERMINATED           3   adam            tanh                                   16                 64                  3                 0          0.000149465         17        1            240.238         0.46025  │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20        1            230.373         0.44251  │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28        1            170.478         0.486531 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18        1            158.069         0.454008 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27        1            323.341         0.452694 │
│ trial_72b58    TERMINATED           3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18        1            355.257         0.453351 │
│ trial_72b58    TERMINATED           3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21        1            344.033         0.472405 │
│ trial_72b58    TERMINATED           2   adam            relu                                   32                 32                  5                 1          0.00013253          28        1            245.38          0.485874 │
│ trial_72b58    TERMINATED           2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22        1            315.357         0.462221 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17        1            295.352         0.499671 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28        1            214.486         0.443495 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19        1            245.696         0.503285 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3377191)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 13ms/step - accuracy: 0.5311 - loss: 1.0351 - val_accuracy: 0.5102 - val_loss: 1.0507
[36m(train_cnn_ray_tune pid=3377203)[0m Epoch 17/18[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3377191)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 12ms/step - accuracy: 0.5446 - loss: 1.0205 - val_accuracy: 0.5141 - val_loss: 1.0528
[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
[1m  1/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 43ms/step - accuracy: 0.5000 - loss: 0.8461
[36m(train_cnn_ray_tune pid=3377211)[0m 
[1m  8/665[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 8ms/step - accuracy: 0.5381 - loss: 0.9178  
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m Epoch 28/29[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[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=3377203)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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[36m(train_cnn_ray_tune pid=3377203)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:25:03. Total running time: 6min 12s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             369.262 │
│ time_total_s                 369.262 │
│ training_iteration                 1 │
│ val_accuracy                 0.48456 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:25:03. Total running time: 6min 12s
[36m(train_cnn_ray_tune pid=3377212)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:25:04. Total running time: 6min 13s
╭─────────────────────────────────────╮
│ Trial trial_72b58 result            │
├─────────────────────────────────────┤
│ checkpoint_dir_name                 │
│ time_this_iter_s             370.03 │
│ time_total_s                 370.03 │
│ training_iteration                1 │
│ val_accuracy                 0.4113 │
╰─────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:25:04. Total running time: 6min 13s
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:25:05. Total running time: 6min 13s
2025-11-07 17:25:05,694	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_C/case_C_CAPTURE24_acc_superclasses_CPA_METs/CAPTURE24_hyperparameters_tuning' in 0.0065s.
I0000 00:00:1762532705.823552 3375578 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             370.612 │
│ time_total_s                 370.612 │
│ training_iteration                 1 │
│ val_accuracy                 0.51084 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:25:05. Total running time: 6min 13s
[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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Trial trial_72b58 finished iteration 1 at 2025-11-07 17:25:05. Total running time: 6min 14s
╭──────────────────────────────────────╮
│ Trial trial_72b58 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             370.993 │
│ time_total_s                 370.993 │
│ training_iteration                 1 │
│ val_accuracy                 0.50197 │
╰──────────────────────────────────────╯

Trial trial_72b58 completed after 1 iterations at 2025-11-07 17:25:05. Total running time: 6min 14s

Trial status: 20 TERMINATED
Current time: 2025-11-07 17:25:05. Total running time: 6min 14s
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_72b58    TERMINATED           3   adam            relu                                   32                 64                  3                 1          0.000135481         28        1            268.711         0.491459 │
│ trial_72b58    TERMINATED           3   adam            relu                                   32                 64                  3                 0          1.17781e-05         29        1            370.612         0.510841 │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   32                 32                  3                 0          2.55641e-05         18        1            298.333         0.491787 │
│ trial_72b58    TERMINATED           3   adam            tanh                                   32                 16                  3                 0          3.00198e-05         23        1            309.029         0.446452 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 64                  3                 0          2.49655e-05         22        1            340.565         0.462221 │
│ trial_72b58    TERMINATED           3   rmsprop         relu                                   16                 32                  5                 0          1.05844e-05         18        1            369.262         0.48456  │
│ trial_72b58    TERMINATED           3   adam            tanh                                   16                 64                  3                 0          0.000149465         17        1            240.238         0.46025  │
│ trial_72b58    TERMINATED           3   rmsprop         tanh                                   16                 32                  5                 0          4.12447e-05         20        1            230.373         0.44251  │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 64                  5                 0          7.27368e-05         28        1            170.478         0.486531 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   32                 64                  3                 0          5.01603e-05         18        1            158.069         0.454008 │
│ trial_72b58    TERMINATED           2   adam            tanh                                   16                 32                  3                 0          1.70781e-05         27        1            323.341         0.452694 │
│ trial_72b58    TERMINATED           3   rmsprop         relu                                   16                 16                  5                 1          6.79854e-06         18        1            355.257         0.453351 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 1          5.16709e-06         24        1            370.03          0.411301 │
│ trial_72b58    TERMINATED           3   adam            relu                                   32                 64                  3                 1          5.74494e-06         21        1            344.033         0.472405 │
│ trial_72b58    TERMINATED           3   rmsprop         relu                                   16                 16                  5                 1          0.000128554         22        1            370.994         0.501971 │
│ trial_72b58    TERMINATED           2   adam            relu                                   32                 32                  5                 1          0.00013253          28        1            245.38          0.485874 │
│ trial_72b58    TERMINATED           2   adam            relu                                   16                 64                  5                 1          7.68381e-05         22        1            315.357         0.462221 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   16                 16                  3                 0          8.67712e-05         17        1            295.352         0.499671 │
│ trial_72b58    TERMINATED           2   rmsprop         tanh                                   16                 32                  5                 0          0.000144533         28        1            214.486         0.443495 │
│ trial_72b58    TERMINATED           2   rmsprop         relu                                   32                 32                  3                 0          3.80689e-05         19        1            245.696         0.503285 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 3, 'optimizador': 'adam', 'funcion_activacion': 'relu', 'tamanho_minilote': 32, 'numero_filtros': 64, 'tamanho_filtro': 3, 'num_resblocks': 0, 'tasa_aprendizaje': 1.177811464352205e-05, 'epochs': 29}
Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762532707.617822 3413993 service.cc:152] XLA service 0x77e7ec121a40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762532707.617851 3413993 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:25:07.648244: 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 00:00:1762532707.824441 3413993 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762532709.459076 3413993 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

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2025-11-07 17:25:13.522546: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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Epoch 3/29

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Epoch 4/29

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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3872 - loss: 1.3866
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[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3901 - loss: 1.3810
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Epoch 5/29

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Epoch 6/29

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Epoch 7/29

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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.2547
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2539
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Epoch 8/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4392 - loss: 1.1725 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4373 - loss: 1.1872
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4356 - loss: 1.2076
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[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4347 - loss: 1.2178
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.2187
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4356 - loss: 1.2193
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Epoch 9/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.2060 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.1996
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.2042
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Epoch 10/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1827 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1782
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1795
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1804
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1809
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4616 - loss: 1.1823
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1828
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1829
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4609 - loss: 1.1832
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4609 - loss: 1.1833 - val_accuracy: 0.4415 - val_loss: 1.1879
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2399
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4529 - loss: 1.1894 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1856
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1815
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.1808
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1804
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4637 - loss: 1.1800
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4637 - loss: 1.1800
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1796
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1790
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Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 1.0165
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1344 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1443
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1460
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1479
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1492
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1502
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1509
[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1510
[1m315/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1515
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4771 - loss: 1.1516 - val_accuracy: 0.4524 - val_loss: 1.1616
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4375 - loss: 1.1306
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1488 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1561
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1593
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1584
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1570
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1556
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1545
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1539
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1534
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4735 - loss: 1.1533 - val_accuracy: 0.4399 - val_loss: 1.1539
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2259
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1626 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1476
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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1356
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Epoch 15/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1363 
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1149
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1153
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Epoch 16/29

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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1192
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1170
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Epoch 17/29

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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.0901
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[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5030 - loss: 1.0893
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0900
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.0906
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0912
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Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0268
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0537 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0660
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0770
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[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0827
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0834
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.0836
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Epoch 19/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0701 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0604
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0597
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0618
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0642
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0670
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0690
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0707
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0717
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5172 - loss: 1.0718 - val_accuracy: 0.4734 - val_loss: 1.1093
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2539
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.0898 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.0836
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0811
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0806
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0794
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.0797
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0798
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0797
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0792
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Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0273
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.0591 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0599
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0619
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0613
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.0614
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0628
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0640
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.0651
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0657
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5124 - loss: 1.0657 - val_accuracy: 0.4803 - val_loss: 1.1007
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8471
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5540 - loss: 0.9918 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0159
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0248
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0305
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0344
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0376
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0393
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0405
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0413
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5324 - loss: 1.0414 - val_accuracy: 0.4852 - val_loss: 1.0934
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1069
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0828 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0683
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0604
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[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0566
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0555
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0550
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Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0802
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0494 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0459
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0417
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0415
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0415
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Epoch 25/29

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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0405
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0395
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0389
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0386
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0390
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0396
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Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9849
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0334 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0350
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0349
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0337
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0326
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0315
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0304
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0295
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0292
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5353 - loss: 1.0293 - val_accuracy: 0.4938 - val_loss: 1.0797
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9060
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5220 - loss: 1.0526 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0451
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0409
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0388
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0367
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0344
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0331
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0321
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0313
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5377 - loss: 1.0312 - val_accuracy: 0.4970 - val_loss: 1.0775
Epoch 28/29

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Epoch 29/29

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Saved model to disk.
[36m(train_cnn_ray_tune pid=3377211)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377211)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377191)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377212)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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[36m(train_cnn_ray_tune pid=3377211)[0m 
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=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 26ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m59/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 867us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 49.93 [%]
Global F1 score (validation) = 49.79 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.29045638 0.35501307 0.0588881  0.29564252]
 [0.35673344 0.4037307  0.084474   0.15506186]
 [0.1937545  0.33872315 0.11594874 0.35157356]
 ...
 [0.02429989 0.04181924 0.9027108  0.03117007]
 [0.00411345 0.00764679 0.98522955 0.00301024]
 [0.02676989 0.04221705 0.8969327  0.03408036]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.47 [%]
Global accuracy score (test) = 50.87 [%]
Global F1 score (train) = 57.96 [%]
Global F1 score (test) = 50.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.30      0.33       400
MODERATE-INTENSITY       0.46      0.57      0.51       400
         SEDENTARY       0.61      0.70      0.65       400
VIGOROUS-INTENSITY       0.60      0.46      0.52       345

          accuracy                           0.51      1545
         macro avg       0.51      0.51      0.50      1545
      weighted avg       0.51      0.51      0.50      1545


2025-11-07 17:25:48.254183: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:25:48.265476: 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 00:00:1762532748.278571 3417716 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762532748.282693 3417716 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762532748.292652 3417716 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532748.292672 3417716 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532748.292674 3417716 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532748.292675 3417716 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:25:48.295902: 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 00:00:1762532750.533803 3417716 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762532752.118216 3417855 service.cc:152] XLA service 0x748acc004f10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762532752.118270 3417855 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:25:52.150825: 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 00:00:1762532752.332873 3417855 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762532753.997068 3417855 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:43[0m 3s/step - accuracy: 0.1875 - loss: 1.7915
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2222 - loss: 1.8085  
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2435 - loss: 1.7663
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2566 - loss: 1.7407
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2655 - loss: 1.7206
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2720 - loss: 1.7033
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2769 - loss: 1.6892
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2812 - loss: 1.6770
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2851 - loss: 1.6658
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2889 - loss: 1.65552025-11-07 17:25:56.643188: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:25:57.999944: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2890 - loss: 1.6552 - val_accuracy: 0.3880 - val_loss: 1.3095
Epoch 2/29

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[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3449 - loss: 1.4822 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.4857
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.4804
[1m134/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3567 - loss: 1.4753
[1m170/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3595 - loss: 1.4694
[1m205/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3616 - loss: 1.4637
[1m241/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3634 - loss: 1.4583
[1m278/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3653 - loss: 1.4531
[1m315/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3670 - loss: 1.4481
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Epoch 3/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3950 - loss: 1.3915 
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Epoch 4/29

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Epoch 5/29

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[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4210 - loss: 1.2547
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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4251 - loss: 1.2499
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Epoch 6/29

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[1m 29/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4653 - loss: 1.1639 
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4621 - loss: 1.1774
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[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1851
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Epoch 7/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4306 - loss: 1.2182 
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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.2065
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[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4496 - loss: 1.2005
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4498 - loss: 1.2000 - val_accuracy: 0.4662 - val_loss: 1.1413
Epoch 8/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1727 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1702
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1596
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1579
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1562
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1543
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4725 - loss: 1.1539
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Epoch 9/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1495 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1500
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4669 - loss: 1.1488
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1448
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1417
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1410
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1410
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1411
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4744 - loss: 1.1413 - val_accuracy: 0.4862 - val_loss: 1.1314
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1491
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4609 - loss: 1.1857 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1637
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1559
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1530
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4728 - loss: 1.1492
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1465
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1441
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1428
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1415
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4776 - loss: 1.1414 - val_accuracy: 0.4829 - val_loss: 1.1259
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4062 - loss: 1.1063
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1065 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1100
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1125
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1116
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1118
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1121
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1122
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1123
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4860 - loss: 1.1126 - val_accuracy: 0.4915 - val_loss: 1.1154
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1071
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1185 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1188
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[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1121
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1111
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1102
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1094
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Epoch 13/29

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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.1257
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[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1105
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1096
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Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0476
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[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.0942
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.0939
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.0935
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5023 - loss: 1.0934 - val_accuracy: 0.5112 - val_loss: 1.0976
Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1207
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.0915 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0981
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0993
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0974
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0951
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0929
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0912
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0902
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0892
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Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2385
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.0935 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0808
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0740
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0735
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0729
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0726
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0724
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0722
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Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2539
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4938 - loss: 1.0744 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.0805
[1m104/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.0809
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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0759
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0738
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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0706
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0697
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Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.3438 - loss: 1.2857
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1246 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.0988
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0855
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0765
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0707
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0668
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0642
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0625
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.0609
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Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1569
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0408 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0429
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0449
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0456
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0460
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0467
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0474
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0473 - val_accuracy: 0.5148 - val_loss: 1.0770
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0783
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5428 - loss: 1.0229 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5415 - loss: 1.0290
[1m103/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0304
[1m139/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0323
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5360 - loss: 1.0342
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0354
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0368
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0380
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0386
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5337 - loss: 1.0388 - val_accuracy: 0.5138 - val_loss: 1.0692
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6562 - loss: 0.8885
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0780 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0714
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[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0674
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[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0604
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0572
[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0552
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0529
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5249 - loss: 1.0522 - val_accuracy: 0.5148 - val_loss: 1.0685
Epoch 22/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5084 - loss: 1.0345 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0331
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0382
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[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0373
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0370
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0367
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Epoch 23/29

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[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0398
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[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0344
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0323
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0314
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0307
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0297
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Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8725
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5550 - loss: 1.0085 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5531 - loss: 1.0116
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5493 - loss: 1.0158
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5466 - loss: 1.0175
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0184
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0184
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0182
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0180
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5445 - loss: 1.0178 - val_accuracy: 0.5158 - val_loss: 1.0554
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9082
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5439 - loss: 1.0003 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0099
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0171
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0197
[1m176/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0195
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0186
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0175
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5474 - loss: 1.0163
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5479 - loss: 1.0155
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5479 - loss: 1.0153 - val_accuracy: 0.5184 - val_loss: 1.0533
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1296
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0276 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0254
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Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.7188 - loss: 0.7840
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Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0694
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[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0064
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0065
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0065
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Epoch 29/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 0.8595
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5719 - loss: 0.9607 
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 432ms/step2025-11-07 17:26:17.868177: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 28ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 28ms/step
Saved model to disk.
Accuracy capturado en la ejecución 1: 50.87 [%]
F1-score capturado en la ejecución 1: 50.22 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:44[0m 1s/step
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 777us/step
[1m135/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 756us/step
[1m207/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 738us/step
[1m271/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 751us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 26ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 916us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
Global accuracy score (validation) = 50.33 [%]
Global F1 score (validation) = 49.88 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.38312832 0.3336073  0.12032127 0.16294318]
 [0.22886997 0.36172166 0.09754335 0.311865  ]
 [0.41165805 0.31439075 0.11410663 0.15984459]
 ...
 [0.02624619 0.02800865 0.928565   0.01718013]
 [0.02154587 0.02713131 0.9370005  0.01432226]
 [0.02782743 0.02506081 0.92806643 0.01904524]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.54 [%]
Global accuracy score (test) = 53.01 [%]
Global F1 score (train) = 59.52 [%]
Global F1 score (test) = 52.51 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.31      0.33       400
MODERATE-INTENSITY       0.44      0.63      0.52       400
         SEDENTARY       0.69      0.76      0.72       400
VIGOROUS-INTENSITY       0.76      0.41      0.53       345

          accuracy                           0.53      1545
         macro avg       0.56      0.53      0.53      1545
      weighted avg       0.55      0.53      0.52      1545


Accuracy capturado en la ejecución 2: 53.01 [%]
2025-11-07 17:26:30.717434: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:26:30.728890: 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 00:00:1762532790.742090 3421563 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762532790.746285 3421563 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762532790.756077 3421563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532790.756096 3421563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532790.756098 3421563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532790.756099 3421563 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:26:30.759254: 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 00:00:1762532793.030805 3421563 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762532794.650126 3421672 service.cc:152] XLA service 0x7f456800d470 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762532794.650154 3421672 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:26:34.680199: 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 00:00:1762532794.865705 3421672 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762532796.535455 3421672 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:59[0m 3s/step - accuracy: 0.2188 - loss: 1.8298
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2947 - loss: 1.7383  
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2987 - loss: 1.7159
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3013 - loss: 1.7016
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.6890
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3067 - loss: 1.6770
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3091 - loss: 1.6656
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3114 - loss: 1.6557
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3131 - loss: 1.6476
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3147 - loss: 1.6403
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.3150 - loss: 1.63902025-11-07 17:26:39.193570: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:26:40.421916: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 13ms/step - accuracy: 0.3151 - loss: 1.6388 - val_accuracy: 0.4139 - val_loss: 1.2931
Epoch 2/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3687 - loss: 1.4714 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3641 - loss: 1.4697
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3626 - loss: 1.4598
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[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.4496
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Epoch 3/29

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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4075 - loss: 1.3023
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4067 - loss: 1.3030
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Epoch 4/29

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[1m280/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4268 - loss: 1.2640
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4275 - loss: 1.2634
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Epoch 5/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.2716 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2623
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4277 - loss: 1.2543
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4285 - loss: 1.2524
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4293 - loss: 1.2500
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4303 - loss: 1.2476
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.2458
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4315 - loss: 1.2439
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4318 - loss: 1.2431 - val_accuracy: 0.4625 - val_loss: 1.1435
Epoch 6/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2072
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4210 - loss: 1.2585 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4283 - loss: 1.2492
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.2301
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.2260
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4420 - loss: 1.2229
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4452 - loss: 1.2174
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Epoch 7/29

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[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4952 - loss: 1.1616 
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[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1630
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1635
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1638
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1641
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[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1628
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1626
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Epoch 8/29

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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1520
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1526
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Epoch 9/29

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[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1597
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1494
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1454
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1424
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1402
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1385
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1377 - val_accuracy: 0.4773 - val_loss: 1.1066
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0174
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5184 - loss: 1.1025 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.1013
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.0996
[1m137/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.1002
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1026
[1m209/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1045
[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.1066
[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1084
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1096
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4998 - loss: 1.1100 - val_accuracy: 0.4744 - val_loss: 1.1038
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.4263
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1526 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1392
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1231
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[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1161
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1153
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Epoch 12/29

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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.0975
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.0983
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Epoch 13/29

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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1136
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1109
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1088
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1069
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1057
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Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 1.0376
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0610 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0681
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0764
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0774
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0795
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.0809
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0815
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0821
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5134 - loss: 1.0822 - val_accuracy: 0.4951 - val_loss: 1.0864
Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1066
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5206 - loss: 1.0623 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0605
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0603
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0580
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0580
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0586
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0597
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0609
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0618
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Epoch 16/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0420 
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[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0490
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0497
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0524
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Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9801
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[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0450
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0460
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Epoch 18/29

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[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0755
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0714
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0686
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0659
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0635
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5183 - loss: 1.0630 - val_accuracy: 0.5082 - val_loss: 1.0595
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.4451
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0712 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0612
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0581
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0551
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0527
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0515
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0509
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0506
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0503
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5222 - loss: 1.0502 - val_accuracy: 0.5049 - val_loss: 1.0577
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0193
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.0804 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.0702
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.0600
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0581
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0557
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0537
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0521
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5194 - loss: 1.0509 - val_accuracy: 0.5125 - val_loss: 1.0516
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9700
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0370 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0320
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5413 - loss: 1.0283
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0277
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0287
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0286
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5360 - loss: 1.0286
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0288
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0290
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Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8259
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5347 - loss: 1.0274 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0407
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0386
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0366
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0355
[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0343
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0336
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0330
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Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 0.8433
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.6064 - loss: 0.9783 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5817 - loss: 1.0066
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5715 - loss: 1.0134
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5657 - loss: 1.0147
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5610 - loss: 1.0162
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5581 - loss: 1.0169
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5560 - loss: 1.0170
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 1.0171
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5534 - loss: 1.0171
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5533 - loss: 1.0171 - val_accuracy: 0.5099 - val_loss: 1.0479
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1087
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5366 - loss: 1.0170 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5419 - loss: 1.0109
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0099
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0088
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0082
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0077
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0073
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0067
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5448 - loss: 1.0065
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5449 - loss: 1.0065 - val_accuracy: 0.5151 - val_loss: 1.0458
Epoch 25/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0049 
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Epoch 26/29

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Epoch 27/29

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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 0.9864
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 0.9866
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Epoch 28/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5690 - loss: 0.9704 
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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5598 - loss: 0.9887
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5594 - loss: 0.9890
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 0.9891
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 0.9889
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Epoch 29/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6068 - loss: 0.9005 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5873 - loss: 0.9276
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Saved model to disk.
F1-score capturado en la ejecución 2: 52.51 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:28[0m 1s/step
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[1m66/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 770us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
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Global accuracy score (validation) = 52.0 [%]
Global F1 score (validation) = 51.43 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.45408183 0.29224396 0.11211074 0.1415634 ]
 [0.44390145 0.32501563 0.11504842 0.11603447]
 [0.47433153 0.30055848 0.08400661 0.1411034 ]
 ...
 [0.04479457 0.03070523 0.8935207  0.03097949]
 [0.02963401 0.02471875 0.92843693 0.01721033]
 [0.04640475 0.03269734 0.88710636 0.03379157]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.66 [%]
Global accuracy score (test) = 51.26 [%]
Global F1 score (train) = 57.74 [%]
Global F1 score (test) = 51.18 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.57      0.45       400
MODERATE-INTENSITY       0.45      0.34      0.39       400
         SEDENTARY       0.65      0.73      0.69       400
VIGOROUS-INTENSITY       0.79      0.39      0.53       345

          accuracy                           0.51      1545
         macro avg       0.56      0.51      0.51      1545
      weighted avg       0.56      0.51      0.51      1545


Accuracy capturado en la ejecución 3: 51.26 [%]
2025-11-07 17:27:12.668451: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:27:12.679683: 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 00:00:1762532832.692702 3425389 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762532832.696838 3425389 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762532832.706643 3425389 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532832.706661 3425389 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532832.706663 3425389 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532832.706664 3425389 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:27:12.709792: 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 00:00:1762532834.968732 3425389 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762532836.563943 3425519 service.cc:152] XLA service 0x7e3b1c00cc70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762532836.564002 3425519 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:27:16.604389: 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 00:00:1762532836.780656 3425519 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762532838.406896 3425519 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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[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2663 - loss: 1.8020
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2710 - loss: 1.7875
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.7735
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2762 - loss: 1.77172025-11-07 17:27:21.092817: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:27:22.263615: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3551 - loss: 1.5032
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3566 - loss: 1.4999
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.4945
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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.4856
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3637 - loss: 1.4821
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Epoch 3/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3981 - loss: 1.4107 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4056 - loss: 1.3943
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4058 - loss: 1.3887
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4067 - loss: 1.3826
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4069 - loss: 1.3782
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4073 - loss: 1.3741
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4076 - loss: 1.3710
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4081 - loss: 1.3684
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4085 - loss: 1.3661
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4087 - loss: 1.3653 - val_accuracy: 0.4609 - val_loss: 1.1555
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.3438 - loss: 1.2382
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3951 - loss: 1.3522 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4073 - loss: 1.3373
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4115 - loss: 1.3333
[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4150 - loss: 1.3284
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4180 - loss: 1.3232
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4200 - loss: 1.3191
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4212 - loss: 1.3153
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4220 - loss: 1.3125
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4226 - loss: 1.3103 - val_accuracy: 0.4704 - val_loss: 1.1364
Epoch 5/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.3922
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4215 - loss: 1.3063 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.2861
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4389 - loss: 1.2741
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4415 - loss: 1.2675
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2639
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4435 - loss: 1.2604
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.2580
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4447 - loss: 1.2562
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4453 - loss: 1.2543 - val_accuracy: 0.4737 - val_loss: 1.1270
Epoch 6/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4647 - loss: 1.2030 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4631 - loss: 1.2048
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.2018
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.2002
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1993
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[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.2020
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.2028
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.2036
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4619 - loss: 1.2037 - val_accuracy: 0.4773 - val_loss: 1.1185
Epoch 7/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1209 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1342
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1477
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1573
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1640
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1677
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1698
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1717
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4738 - loss: 1.1732 - val_accuracy: 0.4862 - val_loss: 1.1143
Epoch 8/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1881
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.2223 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.2078
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1899
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1854
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.1834
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1814
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1794
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4692 - loss: 1.1782
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4692 - loss: 1.1781 - val_accuracy: 0.4777 - val_loss: 1.1106
Epoch 9/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4375 - loss: 1.3073
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1641 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1474
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1403
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4900 - loss: 1.1385
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1395
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1410
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1421
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1435
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1446
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Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 1.0495
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0970 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.1146
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[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1230
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[1m269/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1268
[1m305/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1275
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4910 - loss: 1.1277 - val_accuracy: 0.4836 - val_loss: 1.0985
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1782
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1078 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1252
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.1259
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1235
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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1200
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1198
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Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2812 - loss: 1.3859
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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1022
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1055
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1052
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1050
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1056
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1062
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1064
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4975 - loss: 1.1064 - val_accuracy: 0.4852 - val_loss: 1.0859
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0275
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0908 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.0954
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.0983
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1008
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1011
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1000
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.0994
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.0992
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4976 - loss: 1.0991 - val_accuracy: 0.4915 - val_loss: 1.0842
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 24ms/step - accuracy: 0.5312 - loss: 1.2338
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5010 - loss: 1.1089 
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4980 - loss: 1.1021
[1m101/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4976 - loss: 1.1007
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.0950
[1m175/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.0912
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.0887
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.0875
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0869
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.0867
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6875 - loss: 0.8043
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0467 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0744
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0810
[1m155/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0818
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.0825
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0835
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Epoch 16/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0659 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.0681
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0638
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[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0643
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0651
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5157 - loss: 1.0661 - val_accuracy: 0.4908 - val_loss: 1.0758
Epoch 17/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.0762 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.0737
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.0637
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.0604
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5058 - loss: 1.0592
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.0586
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.0584
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5092 - loss: 1.0582 - val_accuracy: 0.4862 - val_loss: 1.0697
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3184
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.0866 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0730
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0671
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0647
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0631
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0609
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0598
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0594
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5186 - loss: 1.0592 - val_accuracy: 0.4898 - val_loss: 1.0713
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0158
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5073 - loss: 1.0524 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0506
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0513
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.0507
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0497
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0488
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0483
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0482
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0484
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5279 - loss: 1.0484 - val_accuracy: 0.5000 - val_loss: 1.0706
Epoch 20/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0396 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0435
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0456
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0476
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0486
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0485
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0485
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0482
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Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0285
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0828 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0709
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0623
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0581
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0549
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0519
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0496
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0478
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0461
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Epoch 22/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0282 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0256
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0255
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0245
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0241
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0242
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0247
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0254
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0261
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5309 - loss: 1.0261 - val_accuracy: 0.4961 - val_loss: 1.0590
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.5312 - loss: 0.9103
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0363 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0286
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0233
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0207
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0202
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0208
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0213
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0216
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0218
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5366 - loss: 1.0218 - val_accuracy: 0.4967 - val_loss: 1.0557
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0975
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 0.9941 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 0.9973
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0058
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[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0119
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5330 - loss: 1.0130
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0141
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5334 - loss: 1.0143 - val_accuracy: 0.4977 - val_loss: 1.0555
Epoch 25/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5242 - loss: 1.0464 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0421
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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0191
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0180
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Epoch 26/29

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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0327
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[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0268
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0256
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0240
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5276 - loss: 1.0226 - val_accuracy: 0.5023 - val_loss: 1.0510
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0274
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5651 - loss: 0.9960 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5581 - loss: 0.9986
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0034
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0049
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0054
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0060
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0062
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0061
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5414 - loss: 1.0061 - val_accuracy: 0.5092 - val_loss: 1.0467
Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0706
[1m 31/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5460 - loss: 0.9790 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 0.9867
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 0.9950
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 0.9977
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 0.9982
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 0.9987
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 0.9992
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5427 - loss: 0.9993
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 0.9994
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Epoch 29/29

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Saved model to disk.
F1-score capturado en la ejecución 3: 51.18 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:39[0m 1s/step
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[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 775us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
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Global accuracy score (validation) = 50.3 [%]
Global F1 score (validation) = 48.54 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.2181994  0.37584513 0.03216209 0.3737934 ]
 [0.33650592 0.3187048  0.13482332 0.20996594]
 [0.43823695 0.34358338 0.08722224 0.13095744]
 ...
 [0.04865734 0.03627368 0.8927565  0.02231251]
 [0.01857283 0.01752295 0.95087016 0.01303412]
 [0.04761478 0.03630152 0.89249825 0.0235855 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.86 [%]
Global accuracy score (test) = 48.22 [%]
Global F1 score (train) = 56.49 [%]
Global F1 score (test) = 46.67 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.64      0.47       400
MODERATE-INTENSITY       0.39      0.20      0.27       400
         SEDENTARY       0.60      0.70      0.64       400
VIGOROUS-INTENSITY       0.69      0.37      0.48       345

          accuracy                           0.48      1545
         macro avg       0.51      0.48      0.47      1545
      weighted avg       0.51      0.48      0.47      1545


Accuracy capturado en la ejecución 4: 48.22 [%]
2025-11-07 17:27:54.472507: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:27:54.483643: 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 00:00:1762532874.496722 3429204 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762532874.500900 3429204 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762532874.511040 3429204 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532874.511058 3429204 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532874.511061 3429204 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532874.511062 3429204 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:27:54.514278: 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 00:00:1762532876.741130 3429204 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762532878.335689 3429334 service.cc:152] XLA service 0x78138800dd10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762532878.335719 3429334 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:27:58.366298: 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 00:00:1762532878.548074 3429334 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762532880.180191 3429334 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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[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2837 - loss: 1.7236
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2881 - loss: 1.7097
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2922 - loss: 1.6969
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[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3000 - loss: 1.6746
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3038 - loss: 1.6634
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3071 - loss: 1.6536
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2025-11-07 17:28:04.200635: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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[1m313/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3911 - loss: 1.3920
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Epoch 3/29

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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4112 - loss: 1.3172
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4120 - loss: 1.3169
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4128 - loss: 1.3162
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4135 - loss: 1.3157
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4137 - loss: 1.3154
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4137 - loss: 1.3152 - val_accuracy: 0.4320 - val_loss: 1.2142
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.2697
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4532 - loss: 1.2687 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4482 - loss: 1.2604
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4470 - loss: 1.2569
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4461 - loss: 1.2546
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4455 - loss: 1.2514
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4447 - loss: 1.2498
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.2495
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2493
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.2498
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Epoch 5/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.3312
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4185 - loss: 1.3193 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4243 - loss: 1.3017
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Epoch 6/29

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Epoch 7/29

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[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1795
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Epoch 8/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.0994 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1150
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4822 - loss: 1.1306
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[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1405
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1419
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Epoch 9/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.1681 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1596
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1488
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[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1467
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1458
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1451
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Epoch 10/29

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[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5286 - loss: 1.1209 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.1216
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.1180
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.1155
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.1133
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1118
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.1108
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.1102
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.1102
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5063 - loss: 1.1103 - val_accuracy: 0.5010 - val_loss: 1.1174
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1609
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.0815 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4987 - loss: 1.0822
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.0867
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0908
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.0941
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0953
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.0959
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.0968
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.0976
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5014 - loss: 1.0977 - val_accuracy: 0.5082 - val_loss: 1.1041
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1786
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1205 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1140
[1m118/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.1141
[1m157/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1157
[1m194/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1164
[1m232/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1165
[1m270/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1158
[1m309/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1143
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4975 - loss: 1.1132 - val_accuracy: 0.5007 - val_loss: 1.0945
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0655
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1256 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1225
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1170
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1127
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1095
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1081
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.1067
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1057
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1046
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4984 - loss: 1.1045 - val_accuracy: 0.5000 - val_loss: 1.0891
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 0.8968
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.0583 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.0647
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.0669
[1m156/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.0674
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[1m235/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0685
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Epoch 15/29

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[1m318/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0619
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Epoch 16/29

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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0498
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0503
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Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1125
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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0308
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0335
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0349
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[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0357
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0361
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0366
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5311 - loss: 1.0367 - val_accuracy: 0.5135 - val_loss: 1.0717
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2194
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5059 - loss: 1.0970 
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[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0672
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Epoch 19/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0308 
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[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0346
[1m212/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0357
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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0368
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Epoch 20/29

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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0333
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Epoch 21/29

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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.0449
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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0367
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0353
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0339
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0326
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0313
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5302 - loss: 1.0311 - val_accuracy: 0.5141 - val_loss: 1.0611
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1195
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5216 - loss: 1.0456 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0334
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0287
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0256
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0236
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0226
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0220
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0213
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0203
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5340 - loss: 1.0202 - val_accuracy: 0.5227 - val_loss: 1.0595
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1591
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5326 - loss: 1.0638 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0462
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[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0333
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[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0302
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0296
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5447 - loss: 1.0288
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5447 - loss: 1.0287 - val_accuracy: 0.5177 - val_loss: 1.0496
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.8681
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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5588 - loss: 0.9880
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[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 0.9907
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Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1043
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[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5507 - loss: 1.0047
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5510 - loss: 1.0040
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5515 - loss: 1.0035
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 1.0027
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Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0978
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0157 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 1.0007
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5531 - loss: 0.9951
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5536 - loss: 0.9948
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 0.9939
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5554 - loss: 0.9926
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5560 - loss: 0.9922
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 0.9922
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Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8736
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5765 - loss: 0.9689 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5700 - loss: 0.9818
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5691 - loss: 0.9841
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5667 - loss: 0.9870
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5651 - loss: 0.9886
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5645 - loss: 0.9891
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5643 - loss: 0.9892
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5638 - loss: 0.9892
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5633 - loss: 0.9892
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Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 451ms/step2025-11-07 17:28:23.928546: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads

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Saved model to disk.
F1-score capturado en la ejecución 4: 46.67 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

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[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 782us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
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Global accuracy score (validation) = 50.82 [%]
Global F1 score (validation) = 50.23 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.40327558 0.31920454 0.10280656 0.17471333]
 [0.27416053 0.49845827 0.033379   0.19400223]
 [0.40790576 0.40621173 0.02860471 0.15727773]
 ...
 [0.1936495  0.14593369 0.57723814 0.08317867]
 [0.00743848 0.01178118 0.976648   0.00413247]
 [0.05217477 0.06048618 0.86208636 0.02525268]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.38 [%]
Global accuracy score (test) = 50.81 [%]
Global F1 score (train) = 59.71 [%]
Global F1 score (test) = 50.12 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.32      0.32      0.32       400
MODERATE-INTENSITY       0.43      0.69      0.53       400
         SEDENTARY       0.74      0.69      0.71       400
VIGOROUS-INTENSITY       0.78      0.31      0.44       345

          accuracy                           0.51      1545
         macro avg       0.57      0.50      0.50      1545
      weighted avg       0.56      0.51      0.50      1545


Accuracy capturado en la ejecución 5: 50.81 [%]
2025-11-07 17:28:36.519385: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:28:36.530873: 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 00:00:1762532916.544286 3433040 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762532916.548713 3433040 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762532916.559171 3433040 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532916.559193 3433040 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532916.559196 3433040 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532916.559199 3433040 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:28:36.562438: 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 00:00:1762532918.819501 3433040 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762532920.389930 3433150 service.cc:152] XLA service 0x7896dc110ee0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762532920.389971 3433150 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:28:40.420146: 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 00:00:1762532920.601875 3433150 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762532922.222891 3433150 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:29[0m 3s/step - accuracy: 0.1875 - loss: 1.8762
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2344 - loss: 1.8263  
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2501 - loss: 1.7992
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2598 - loss: 1.7770
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2663 - loss: 1.7607
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2723 - loss: 1.7450
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2771 - loss: 1.7312
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2815 - loss: 1.7188
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 1.7076
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2886 - loss: 1.69882025-11-07 17:28:44.917748: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:28:46.182835: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2887 - loss: 1.6985 - val_accuracy: 0.3913 - val_loss: 1.3072
Epoch 2/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.2182
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3688 - loss: 1.4814 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3668 - loss: 1.4758
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3682 - loss: 1.4698
[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3704 - loss: 1.4653
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3729 - loss: 1.4600
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3748 - loss: 1.4556
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3762 - loss: 1.4516
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3774 - loss: 1.4477
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3784 - loss: 1.4447 - val_accuracy: 0.4264 - val_loss: 1.2491
Epoch 3/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.5611
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3992 - loss: 1.3973 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4012 - loss: 1.3850
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4012 - loss: 1.3790
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4032 - loss: 1.3711
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4044 - loss: 1.3664
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4051 - loss: 1.3630
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4060 - loss: 1.3597
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4071 - loss: 1.3568
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4082 - loss: 1.3542
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4082 - loss: 1.3541 - val_accuracy: 0.4455 - val_loss: 1.2265
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2111
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.2657 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4387 - loss: 1.2675
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2712
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.2737
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.2742
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.2749
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Epoch 5/29

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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.2581
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Epoch 6/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1990 
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4670 - loss: 1.2036
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Epoch 7/29

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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1924
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[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1925
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4614 - loss: 1.1923
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1924
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4623 - loss: 1.1925 - val_accuracy: 0.4681 - val_loss: 1.1553
Epoch 8/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1528 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1576
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1626
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1650
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1652
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1647
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1644
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.1636
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1628
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4805 - loss: 1.1625 - val_accuracy: 0.4747 - val_loss: 1.1497
Epoch 9/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1548 
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1548
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[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1526
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1526
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1525
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1529
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4808 - loss: 1.1530 - val_accuracy: 0.4737 - val_loss: 1.1402
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.2812 - loss: 1.4212
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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1995
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[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4737 - loss: 1.1731
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[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1667
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1645
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Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1590
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.1034 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1292
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1398
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1390
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1376
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4900 - loss: 1.1367
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1363
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1358
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4888 - loss: 1.1357 - val_accuracy: 0.4806 - val_loss: 1.1180
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.3661
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1561 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1392
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1302
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.1224
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.1190
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1186
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1182
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.1178
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5042 - loss: 1.1175 - val_accuracy: 0.4855 - val_loss: 1.1129
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2039
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0955 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0971
[1m119/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0992
[1m155/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0992
[1m194/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0999
[1m231/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.1013
[1m270/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.1027
[1m306/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.1034
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5114 - loss: 1.1036 - val_accuracy: 0.4915 - val_loss: 1.1111
Epoch 14/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4875 - loss: 1.1392 
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Epoch 15/29

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[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.0967
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Epoch 16/29

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[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0741
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0752
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0762
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5202 - loss: 1.0763 - val_accuracy: 0.4990 - val_loss: 1.0941
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0872
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 1.0213 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0377
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[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0613
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0631
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0643
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5254 - loss: 1.0648 - val_accuracy: 0.5049 - val_loss: 1.0909
Epoch 18/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1427 
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1102
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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0981
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.0962
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5061 - loss: 1.0957 - val_accuracy: 0.5016 - val_loss: 1.0917
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.6250 - loss: 1.1318
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0747 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0727
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0710
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0683
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5360 - loss: 1.0663
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5359 - loss: 1.0649
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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0631
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0623
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Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1334
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[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1048
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0937
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0858
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5131 - loss: 1.0810
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0769
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0738
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0713
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0697
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Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0994
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0419 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0462
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0494
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0509
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5372 - loss: 1.0520
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0523
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0526
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5360 - loss: 1.0525
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5360 - loss: 1.0525 - val_accuracy: 0.5066 - val_loss: 1.0784
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2316
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0481 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0509
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0505
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5371 - loss: 1.0492
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0480
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0468
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0459
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0452
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0449
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5391 - loss: 1.0449 - val_accuracy: 0.5092 - val_loss: 1.0713
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0781
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0430 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0400
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0404
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0392
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5289 - loss: 1.0386
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[1m269/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0373
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0376
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Epoch 24/29

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[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5599 - loss: 1.0218
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 1.0240
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5509 - loss: 1.0242
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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 1.0273
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Epoch 25/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5674 - loss: 0.9880 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5577 - loss: 1.0020
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 1.0134
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5487 - loss: 1.0160
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0180
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5476 - loss: 1.0191
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0204
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5470 - loss: 1.0216 - val_accuracy: 0.5125 - val_loss: 1.0605
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1731
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0517 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0332
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0276
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5484 - loss: 1.0230
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5492 - loss: 1.0204
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5494 - loss: 1.0189
[1m268/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5492 - loss: 1.0183
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5488 - loss: 1.0182
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5486 - loss: 1.0182 - val_accuracy: 0.5161 - val_loss: 1.0623
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9845
[1m 41/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5593 - loss: 0.9897 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5617 - loss: 0.9936
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5605 - loss: 0.9983
[1m156/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5596 - loss: 1.0020
[1m194/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5586 - loss: 1.0037
[1m232/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 1.0057
[1m271/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5566 - loss: 1.0072
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5555 - loss: 1.0084
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5549 - loss: 1.0091 - val_accuracy: 0.5174 - val_loss: 1.0588
Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 445ms/step2025-11-07 17:29:05.747902: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads

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Saved model to disk.
F1-score capturado en la ejecución 5: 50.12 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

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[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 759us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
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Global accuracy score (validation) = 52.53 [%]
Global F1 score (validation) = 52.2 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.40128383 0.36530203 0.10881751 0.12459664]
 [0.36520192 0.4369012  0.05196632 0.14593059]
 [0.4156438  0.32992807 0.12473354 0.12969452]
 ...
 [0.00866991 0.00593148 0.9834055  0.00199307]
 [0.18587486 0.16224754 0.5678635  0.08401406]
 [0.13254385 0.1053111  0.7035782  0.05856683]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.35 [%]
Global accuracy score (test) = 48.87 [%]
Global F1 score (train) = 57.9 [%]
Global F1 score (test) = 48.05 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.40      0.39       400
MODERATE-INTENSITY       0.42      0.56      0.48       400
         SEDENTARY       0.60      0.68      0.63       400
VIGOROUS-INTENSITY       0.73      0.30      0.42       345

          accuracy                           0.49      1545
         macro avg       0.53      0.48      0.48      1545
      weighted avg       0.52      0.49      0.48      1545


Accuracy capturado en la ejecución 6: 48.87 [%]
F1-score capturado en la ejecución 6: 48.05 [%]

=== EJECUCIÓN 7 ===
2025-11-07 17:29:18.465297: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:29:18.476581: 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 00:00:1762532958.489667 3436847 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762532958.493771 3436847 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762532958.503556 3436847 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532958.503574 3436847 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532958.503576 3436847 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762532958.503577 3436847 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:29:18.506766: 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 00:00:1762532960.773399 3436847 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762532962.353226 3436990 service.cc:152] XLA service 0x7ec4c4017f80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762532962.353258 3436990 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:29:22.383790: 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 00:00:1762532962.564408 3436990 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762532964.155852 3436990 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:22[0m 3s/step - accuracy: 0.3750 - loss: 1.3424
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2579 - loss: 1.7225  
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[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2616 - loss: 1.7082
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2651 - loss: 1.6973
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2690 - loss: 1.6861
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2732 - loss: 1.6738
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2770 - loss: 1.6637
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2805 - loss: 1.6546
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.2838 - loss: 1.64612025-11-07 17:29:26.932804: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:29:28.262275: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2839 - loss: 1.6458 - val_accuracy: 0.3791 - val_loss: 1.3076
Epoch 2/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.4345
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3799 - loss: 1.4178 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3696 - loss: 1.4272
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3662 - loss: 1.4295
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3638 - loss: 1.4327
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.4335
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.4324
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.4299
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3641 - loss: 1.4264
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3652 - loss: 1.4231
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3653 - loss: 1.4226 - val_accuracy: 0.4080 - val_loss: 1.2157
Epoch 3/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4160
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4028 - loss: 1.3606 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4021 - loss: 1.3488
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4035 - loss: 1.3410
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4049 - loss: 1.3360
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4056 - loss: 1.3327
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4055 - loss: 1.3308
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4055 - loss: 1.3287
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4059 - loss: 1.3261
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4065 - loss: 1.3237
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4066 - loss: 1.3234 - val_accuracy: 0.4333 - val_loss: 1.1922
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1836
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.2289 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4376 - loss: 1.2412
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4332 - loss: 1.2488
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4304 - loss: 1.2529
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4293 - loss: 1.2546
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.2558
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Epoch 5/29

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[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4524 - loss: 1.1735 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.1982
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Epoch 6/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4573 - loss: 1.1831 
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[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1804
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1794
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4527 - loss: 1.1812
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.1823
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Epoch 7/29

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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4396 - loss: 1.1877
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[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4466 - loss: 1.1828
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4491 - loss: 1.1807
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.1793
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4528 - loss: 1.1781
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4532 - loss: 1.1778 - val_accuracy: 0.4589 - val_loss: 1.1502
Epoch 8/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1630 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1529
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1553
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1567
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1571
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1569
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1568
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4776 - loss: 1.1566
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4776 - loss: 1.1561 - val_accuracy: 0.4612 - val_loss: 1.1474
Epoch 9/29

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[1m 31/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4135 - loss: 1.2132 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4341 - loss: 1.1826
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[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4527 - loss: 1.1591
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[1m209/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1500
[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4623 - loss: 1.1471
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4646 - loss: 1.1448
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1438
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4667 - loss: 1.1436 - val_accuracy: 0.4652 - val_loss: 1.1399
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4375 - loss: 1.0198
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1273 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1381
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4623 - loss: 1.1399
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1404
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1401
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1401
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1392
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1383
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1376
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Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 1.0701
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[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1491
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1402
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1326
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1270
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1231
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1208
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1195
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.1185
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4998 - loss: 1.1184 - val_accuracy: 0.4813 - val_loss: 1.1278
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3438 - loss: 1.0440
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.0821 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.0895
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.0919
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4936 - loss: 1.0932
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4941 - loss: 1.0951
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.0975
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.0993
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1007
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4928 - loss: 1.1018 - val_accuracy: 0.4839 - val_loss: 1.1208
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1001
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.0936 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1074
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1066
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1053
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1040
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1034
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1023
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1013
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4987 - loss: 1.1007
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4987 - loss: 1.1007 - val_accuracy: 0.4918 - val_loss: 1.1166
Epoch 14/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0708 
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[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0739
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Epoch 15/29

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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.0798
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Epoch 16/29

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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5112 - loss: 1.0724
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0728
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0725
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.0722
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0717
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Epoch 17/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0809 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0694
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0679
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[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0684
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0679
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0674
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0668
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0666
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Epoch 18/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0773 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0770
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[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0670
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0655
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0639
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5205 - loss: 1.0628 - val_accuracy: 0.4984 - val_loss: 1.0953
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2921
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5353 - loss: 1.0453 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0500
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0510
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0513
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0510
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0502
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0498
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0498
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Epoch 20/29

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[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0272
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0308
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0332
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0344
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0357
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0362
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0368
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Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2468
[1m 31/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5226 - loss: 1.0360 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5192 - loss: 1.0377
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0356
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0340
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0340
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0333
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0335
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0339
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0339
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5228 - loss: 1.0339 - val_accuracy: 0.5013 - val_loss: 1.0835
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2319
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.0967 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.0645
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.0562
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0525
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0497
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0471
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0456
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0437
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0424
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5183 - loss: 1.0423 - val_accuracy: 0.5007 - val_loss: 1.0813
Epoch 23/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0234 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0272
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0270
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0252
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5371 - loss: 1.0251
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0244
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0242
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0239
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0237
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5357 - loss: 1.0236 - val_accuracy: 0.4984 - val_loss: 1.0760
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8926
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5624 - loss: 1.0312 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5578 - loss: 1.0267
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5579 - loss: 1.0219
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5582 - loss: 1.0185
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5577 - loss: 1.0176
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5575 - loss: 1.0163
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5568 - loss: 1.0160
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5559 - loss: 1.0156
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5549 - loss: 1.0153
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Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 0.9936
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5451 - loss: 1.0184 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0200
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0195
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0182
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0184
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0176
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0167
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0159
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5435 - loss: 1.0151 - val_accuracy: 0.5033 - val_loss: 1.0711
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2933
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1005 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.0776
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0623
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0507
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5194 - loss: 1.0452
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0422
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0396
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0367
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5243 - loss: 1.0345 - val_accuracy: 0.5049 - val_loss: 1.0718
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1564
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0272 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0173
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0134
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0093
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0061
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Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 436ms/step2025-11-07 17:29:47.931311: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 26ms/step
Saved model to disk.
2025-11-07 17:30:00.554570: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:30:00.565829: 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 00:00:1762533000.579121 3440677 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533000.583438 3440677 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533000.593474 3440677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533000.593498 3440677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533000.593501 3440677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533000.593511 3440677 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:30:00.596885: 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 00:00:1762533002.814832 3440677 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533004.394488 3440817 service.cc:152] XLA service 0x7350e010ee00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533004.394518 3440817 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:30:04.427977: 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 00:00:1762533004.619949 3440817 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533006.254588 3440817 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:39[0m 3s/step - accuracy: 0.2812 - loss: 1.9158
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2821 - loss: 1.71422025-11-07 17:30:08.914270: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:30:10.236277: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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Epoch 3/29

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[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4094 - loss: 1.3311
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4084 - loss: 1.3317
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[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4070 - loss: 1.3341
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4067 - loss: 1.3346
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Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3742
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4313 - loss: 1.2747 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4257 - loss: 1.2856
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4166 - loss: 1.2956
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4147 - loss: 1.2972
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4143 - loss: 1.2972
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4150 - loss: 1.2954
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4156 - loss: 1.2936
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4161 - loss: 1.2920
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Epoch 5/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 1.3366
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4495 - loss: 1.2777 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4453 - loss: 1.2649
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4409 - loss: 1.2646
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2642
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4357 - loss: 1.2627
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4350 - loss: 1.2610
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4348 - loss: 1.2587
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4348 - loss: 1.2574
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Epoch 6/29

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Epoch 7/29

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Epoch 8/29

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[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1722
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1710
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Epoch 9/29

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[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4520 - loss: 1.2074 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1964
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[1m209/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1625
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[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1568
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1549
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Epoch 10/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1181 
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[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.1208
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[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.1219
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Epoch 11/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1618 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4834 - loss: 1.1533
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[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1445
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1378
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1359
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Epoch 12/29

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[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1043
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.1104
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[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1123
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1121
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1121
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4882 - loss: 1.1121 - val_accuracy: 0.4773 - val_loss: 1.1368
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0833
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5197 - loss: 1.0735 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0855
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.0926
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5036 - loss: 1.0944
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.0967
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.0983
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.0992
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0992
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.0989
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5027 - loss: 1.0989 - val_accuracy: 0.4852 - val_loss: 1.1320
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1929
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1323 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1180
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1110
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1070
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1042
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1027
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1012
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4960 - loss: 1.0999
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.0987
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Epoch 15/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5283 - loss: 1.0476 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0524
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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0591
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[1m246/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0656
[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0674
[1m314/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0684
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5229 - loss: 1.0687 - val_accuracy: 0.4852 - val_loss: 1.1161
Epoch 16/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1099 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.0977
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[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.0871
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.0858
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Epoch 17/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0413 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0495
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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0597
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0601
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5209 - loss: 1.0602 - val_accuracy: 0.4839 - val_loss: 1.1140
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5312 - loss: 0.9936
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5504 - loss: 1.0125 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5464 - loss: 1.0218
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0315
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0344
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0369
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0386
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5393 - loss: 1.0405
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0421
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Epoch 19/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5251 - loss: 1.0630 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0511
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[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0398
[1m245/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0390
[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0385
[1m311/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0383
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5352 - loss: 1.0382 - val_accuracy: 0.4885 - val_loss: 1.1084
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 0.9818
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0176 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0270
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0289
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0299
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0309
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0322
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0333
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0342
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Epoch 21/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0560 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0553
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0493
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0435
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0413
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0404
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0394
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0382
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5316 - loss: 1.0374
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Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.5312 - loss: 1.0015
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5512 - loss: 1.0136 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0214
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5427 - loss: 1.0259
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0272
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0281
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0283
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0282
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0278
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0272
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5424 - loss: 1.0271 - val_accuracy: 0.4869 - val_loss: 1.1048
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0030
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5535 - loss: 0.9921 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5529 - loss: 0.9970
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5541 - loss: 0.9966
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 0.9978
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5535 - loss: 0.9994
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5529 - loss: 1.0013
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 1.0030
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0046
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5507 - loss: 1.0060
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Epoch 24/29

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[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5553 - loss: 0.9939 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5494 - loss: 1.0075
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5469 - loss: 1.0129
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0171
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[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5460 - loss: 1.0184
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0184
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0182
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5453 - loss: 1.0182 - val_accuracy: 0.4921 - val_loss: 1.0949
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2222
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.0681 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5243 - loss: 1.0563
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0344
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0296
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0260
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0239
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0225
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0211
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Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9649
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5580 - loss: 0.9907 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 0.9868
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5555 - loss: 0.9923
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 0.9942
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5532 - loss: 0.9953
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5521 - loss: 0.9962
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 0.9967
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 0.9974
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5512 - loss: 0.9976 - val_accuracy: 0.4980 - val_loss: 1.0945
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9858
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5567 - loss: 0.9781 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 0.9902
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0008
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5416 - loss: 1.0056
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0081
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0089
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0087
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5413 - loss: 1.0081
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0072
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5421 - loss: 1.0072 - val_accuracy: 0.5036 - val_loss: 1.0869
Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.9350
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 1.0428 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5533 - loss: 1.0233
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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 430ms/step2025-11-07 17:30:30.052899: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads

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Saved model to disk.

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:47[0m 1s/step
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 762us/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 920us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 49.38 [%]
Global F1 score (validation) = 49.31 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.25838837 0.40940097 0.0349955  0.29721513]
 [0.3535422  0.26156577 0.1127785  0.27211353]
 [0.41801083 0.3050075  0.09968103 0.17730065]
 ...
 [0.04270623 0.07278486 0.8386859  0.04582305]
 [0.04212879 0.07099579 0.8424858  0.04438966]
 [0.00281054 0.00351284 0.99174446 0.00193209]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.56 [%]
Global accuracy score (test) = 50.87 [%]
Global F1 score (train) = 58.61 [%]
Global F1 score (test) = 51.11 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.41      0.38       400
MODERATE-INTENSITY       0.42      0.48      0.45       400
         SEDENTARY       0.66      0.72      0.69       400
VIGOROUS-INTENSITY       0.74      0.41      0.52       345

          accuracy                           0.51      1545
         macro avg       0.54      0.51      0.51      1545
      weighted avg       0.54      0.51      0.51      1545


Accuracy capturado en la ejecución 7: 50.87 [%]
F1-score capturado en la ejecución 7: 51.11 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
2025-11-07 17:30:42.757792: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:30:42.769555: 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 00:00:1762533042.782775 3444522 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533042.786932 3444522 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533042.796888 3444522 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533042.796907 3444522 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533042.796909 3444522 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533042.796911 3444522 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:30:42.799897: 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 00:00:1762533045.059682 3444522 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533046.656651 3444633 service.cc:152] XLA service 0x788acc10e050 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533046.656687 3444633 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:30:46.688105: 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 00:00:1762533046.872048 3444633 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533048.500036 3444633 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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[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2827 - loss: 1.7620
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2862 - loss: 1.7492
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2899 - loss: 1.7370
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2939 - loss: 1.7243
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2973 - loss: 1.7131
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2980 - loss: 1.71052025-11-07 17:30:51.194074: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:30:52.494044: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2981 - loss: 1.7102 - val_accuracy: 0.4297 - val_loss: 1.2784
Epoch 2/29

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[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3784 - loss: 1.4962 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3796 - loss: 1.4859
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3838 - loss: 1.4733
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3861 - loss: 1.4664
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3885 - loss: 1.4594
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3907 - loss: 1.4531
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3920 - loss: 1.4484
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3929 - loss: 1.4444
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3939 - loss: 1.4403 - val_accuracy: 0.4192 - val_loss: 1.2169
Epoch 3/29

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[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4155 - loss: 1.3220
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4144 - loss: 1.3257
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4140 - loss: 1.3296
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4144 - loss: 1.3318
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4151 - loss: 1.3324
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4161 - loss: 1.3317
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.3311
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4172 - loss: 1.3306
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4173 - loss: 1.3305 - val_accuracy: 0.4471 - val_loss: 1.2023
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.4743
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4287 - loss: 1.2987 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.2848
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4356 - loss: 1.2852
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4353 - loss: 1.2879
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4356 - loss: 1.2896
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4356 - loss: 1.2907
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4355 - loss: 1.2904
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4357 - loss: 1.2896 - val_accuracy: 0.4632 - val_loss: 1.1913
Epoch 5/29

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Epoch 6/29

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Epoch 7/29

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[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1905
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4725 - loss: 1.1908
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Epoch 8/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.1938 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.2059
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.2020
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4614 - loss: 1.1996
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1974
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4637 - loss: 1.1963
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4648 - loss: 1.1954
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4657 - loss: 1.1945
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Epoch 9/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1533 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1507
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[1m270/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1581
[1m307/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1585
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4822 - loss: 1.1586 - val_accuracy: 0.4905 - val_loss: 1.1268
Epoch 10/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.1776 
[1m 80/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1794
[1m120/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1752
[1m158/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1731
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[1m235/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1690
[1m276/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1667
[1m315/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4737 - loss: 1.1647
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4742 - loss: 1.1638 - val_accuracy: 0.4846 - val_loss: 1.1198
Epoch 11/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1306 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1365
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1356
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1334
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1317
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1309
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1300
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4955 - loss: 1.1293 - val_accuracy: 0.4915 - val_loss: 1.1137
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.2387
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.1396 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.1246
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.1158
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.1126
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[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.1129
[1m268/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.1134
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1135
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5052 - loss: 1.1135 - val_accuracy: 0.4957 - val_loss: 1.1054
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5625 - loss: 1.0936
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0704 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0734
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0773
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0824
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0878
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.0912
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5073 - loss: 1.0935
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.0952
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.0965
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5062 - loss: 1.0967 - val_accuracy: 0.5010 - val_loss: 1.1020
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4062 - loss: 1.2514
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1245 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1172
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1117
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[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.1030
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1019
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1016
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1013
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Epoch 15/29

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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.0633
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0686
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0729
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0738
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Epoch 16/29

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[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0690
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0793
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[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0819
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0825
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0827
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5154 - loss: 1.0828 - val_accuracy: 0.4944 - val_loss: 1.0855
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2356
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0964 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0837
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.0828
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0814
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0800
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0796
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0795
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0793
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5158 - loss: 1.0792 - val_accuracy: 0.4957 - val_loss: 1.0797
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3438 - loss: 1.2588
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.0823 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.0705
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0652
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0630
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0607
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0587
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0576
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0574
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5201 - loss: 1.0574 - val_accuracy: 0.4977 - val_loss: 1.0758
Epoch 19/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0882 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0779
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0684
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0656
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0627
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0607
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0596
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0590
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Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0245
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0480
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0485
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Epoch 21/29

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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0352
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0385
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5372 - loss: 1.0390
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5359 - loss: 1.0396
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0402
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0400
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0401
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5346 - loss: 1.0401 - val_accuracy: 0.5033 - val_loss: 1.0566
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1003
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5534 - loss: 1.0184 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0177
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5460 - loss: 1.0250
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0298
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0317
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0334
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5399 - loss: 1.0339
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0338
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0335
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5397 - loss: 1.0335 - val_accuracy: 0.5036 - val_loss: 1.0630
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1057
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0711 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0628
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0527
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[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0469
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0456
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0444
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Epoch 24/29

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[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 0.9970
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0033
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[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0102
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0114
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Epoch 25/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5515 - loss: 1.0113 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0193
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0254
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5399 - loss: 1.0255
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0252
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0243
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0234
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5413 - loss: 1.0226 - val_accuracy: 0.5102 - val_loss: 1.0517
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9002
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0427 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0342
[1m103/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0271
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0210
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0167
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0145
[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0135
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0129
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0125
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5444 - loss: 1.0123 - val_accuracy: 0.5105 - val_loss: 1.0492
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0771
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0584 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0386
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0279
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0227
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0196
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0162
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0137
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0121
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0111
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Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 425ms/step2025-11-07 17:31:11.988232: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:50[0m 1s/step
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[1m59/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 863us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 51.35 [%]
Global F1 score (validation) = 50.09 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.43179667 0.2762672  0.11610793 0.17582819]
 [0.4600535  0.27264628 0.09819436 0.1691059 ]
 [0.2773066  0.313438   0.06011681 0.3491386 ]
 ...
 [0.05934753 0.08757813 0.81666934 0.03640493]
 [0.15360057 0.14919862 0.58220434 0.11499646]
 [0.00711043 0.01198451 0.9787942  0.00211085]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.97 [%]
Global accuracy score (test) = 49.9 [%]
Global F1 score (train) = 56.76 [%]
Global F1 score (test) = 49.29 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.56      0.44       400
MODERATE-INTENSITY       0.44      0.26      0.33       400
         SEDENTARY       0.62      0.73      0.67       400
VIGOROUS-INTENSITY       0.68      0.43      0.53       345

          accuracy                           0.50      1545
         macro avg       0.53      0.50      0.49      1545
      weighted avg       0.52      0.50      0.49      1545


Accuracy capturado en la ejecución 8: 49.9 [%]
F1-score capturado en la ejecución 8: 49.29 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
2025-11-07 17:31:24.731720: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:31:24.743277: 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 00:00:1762533084.756717 3448343 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533084.760804 3448343 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533084.771146 3448343 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533084.771167 3448343 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533084.771170 3448343 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533084.771171 3448343 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:31:24.774371: 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 00:00:1762533087.044216 3448343 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533088.641709 3448475 service.cc:152] XLA service 0x75298810ebc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533088.641763 3448475 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:31:28.678167: 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 00:00:1762533088.861211 3448475 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533090.509911 3448475 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:48[0m 3s/step - accuracy: 0.1875 - loss: 1.8442
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[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2804 - loss: 1.7991
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 1.7864
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2887 - loss: 1.7775
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2930 - loss: 1.7662
[1m244/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2964 - loss: 1.7572
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3005 - loss: 1.7455
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.7355
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.3049 - loss: 1.73292025-11-07 17:31:33.277863: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:31:34.669137: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 14ms/step - accuracy: 0.3050 - loss: 1.7326 - val_accuracy: 0.3781 - val_loss: 1.2870
Epoch 2/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.3708
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3890 - loss: 1.5067 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3849 - loss: 1.5057
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3869 - loss: 1.4982
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.4932
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3877 - loss: 1.4881
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3881 - loss: 1.4833
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3887 - loss: 1.4785
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3896 - loss: 1.4727
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3903 - loss: 1.4676
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3904 - loss: 1.4672 - val_accuracy: 0.4202 - val_loss: 1.2683
Epoch 3/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4688 - loss: 1.0975
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4307 - loss: 1.3048 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4137 - loss: 1.3353
[1m 99/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4102 - loss: 1.3439
[1m139/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4103 - loss: 1.3466
[1m171/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4113 - loss: 1.3461
[1m205/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4120 - loss: 1.3461
[1m242/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4126 - loss: 1.3463
[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4132 - loss: 1.3458
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4139 - loss: 1.3445
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4143 - loss: 1.3439 - val_accuracy: 0.4478 - val_loss: 1.2489
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.3811
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4115 - loss: 1.3318 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.3146
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4277 - loss: 1.3107
[1m155/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4306 - loss: 1.3060
[1m194/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4320 - loss: 1.3029
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[1m273/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.3006
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Epoch 5/29

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Epoch 6/29

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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4478 - loss: 1.2250
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Epoch 7/29

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[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1575
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[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1702
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4662 - loss: 1.1722
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1736
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1748
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Epoch 8/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3438 - loss: 1.3661
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1894 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1902
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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1841
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1820
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Epoch 9/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1507 
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[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4759 - loss: 1.1679
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Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2276
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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.1506
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1494
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Epoch 11/29

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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1241
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1234
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4812 - loss: 1.1225
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1222
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1220
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Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0931
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0972 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.1044
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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1050
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.1062
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1072
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1079
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.1085
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1086
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5008 - loss: 1.1086 - val_accuracy: 0.4980 - val_loss: 1.1160
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.5938 - loss: 0.9688
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.0875 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.0971
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[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5036 - loss: 1.0993
[1m175/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0981
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.0971
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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.0969
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.0971
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5055 - loss: 1.0972 - val_accuracy: 0.5053 - val_loss: 1.1080
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.3356
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.0940
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.0934
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Epoch 15/29

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[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0868
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[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.0874
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.0865
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0861
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0857
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Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.3216
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5073 - loss: 1.1249 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5146 - loss: 1.1091
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0972
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0950
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0930
[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0916
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0898
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0887
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5134 - loss: 1.0882 - val_accuracy: 0.5030 - val_loss: 1.0877
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.6250 - loss: 0.9046
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5590 - loss: 1.0180 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5587 - loss: 1.0224
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5515 - loss: 1.0296
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5478 - loss: 1.0326
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 1.0349
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0366
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0386
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0406
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Epoch 18/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0700 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0671
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0643
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0618
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0607
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0597
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0585
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0573
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5271 - loss: 1.0567 - val_accuracy: 0.5135 - val_loss: 1.0811
Epoch 19/29

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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0232
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0339
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0360
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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0393
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0401
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Epoch 20/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0806 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0692
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0625
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0584
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0553
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0530
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0514
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5248 - loss: 1.0504 - val_accuracy: 0.5056 - val_loss: 1.0789
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4688 - loss: 1.2553
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0813 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0590
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5289 - loss: 1.0506
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0473
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0454
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0439
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5322 - loss: 1.0427
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0418
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5336 - loss: 1.0412 - val_accuracy: 0.5059 - val_loss: 1.0726
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.2812 - loss: 1.4813
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.0742 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0519
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0424
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0382
[1m193/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0361
[1m230/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0345
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0328
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0314
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5335 - loss: 1.0306 - val_accuracy: 0.5085 - val_loss: 1.0683
Epoch 23/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0191 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0218
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0213
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0191
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5373 - loss: 1.0190
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0200
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0208
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0208
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0205
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5386 - loss: 1.0204 - val_accuracy: 0.5053 - val_loss: 1.0647
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 1.0079
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5575 - loss: 1.0358 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 1.0326
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5531 - loss: 1.0268
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5516 - loss: 1.0231
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5512 - loss: 1.0199
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 1.0177
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 1.0166
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5515 - loss: 1.0154
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5516 - loss: 1.0148
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Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0774
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5373 - loss: 1.0411 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0304
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0243
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0199
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0165
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5471 - loss: 1.0146
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5481 - loss: 1.0135
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0131
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5488 - loss: 1.0127 - val_accuracy: 0.5036 - val_loss: 1.0661
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2434
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5601 - loss: 1.0381 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0233
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5499 - loss: 1.0153
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5504 - loss: 1.0102
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 1.0083
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5497 - loss: 1.0077
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5492 - loss: 1.0076
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0076
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5484 - loss: 1.0073 - val_accuracy: 0.5085 - val_loss: 1.0528
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 0.9670
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0052 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5447 - loss: 0.9987
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 0.9975
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 0.9975
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5495 - loss: 0.9970
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5504 - loss: 0.9962
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 0.9957
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 0.9958
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5523 - loss: 0.9956
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5523 - loss: 0.9956 - val_accuracy: 0.5036 - val_loss: 1.0519
Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6562 - loss: 0.8710
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5936 - loss: 0.9494 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5841 - loss: 0.9660
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5758 - loss: 0.9764
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5721 - loss: 0.9806
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5698 - loss: 0.9833
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5689 - loss: 0.9842
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5683 - loss: 0.9849
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5680 - loss: 0.9852
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Epoch 29/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.8493
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5636 - loss: 0.9596 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5623 - loss: 0.9699
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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5610 - loss: 0.9766
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[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5611 - loss: 0.9785
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5609 - loss: 0.9791
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5609 - loss: 0.9793
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 496ms/step2025-11-07 17:31:54.358521: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 23ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 23ms/step
Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:52[0m 1s/step
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 713us/step
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 25ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 783us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
Global accuracy score (validation) = 50.99 [%]
Global F1 score (validation) = 49.84 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.30108693 0.50453275 0.02789726 0.16648299]
 [0.47545162 0.30509233 0.03212599 0.18733005]
 [0.43763644 0.29563794 0.03344578 0.23327985]
 ...
 [0.00440393 0.00996256 0.98015803 0.0054754 ]
 [0.03755131 0.05946616 0.88176775 0.02121482]
 [0.063171   0.07645237 0.8238859  0.03649069]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.41 [%]
Global accuracy score (test) = 50.03 [%]
Global F1 score (train) = 56.82 [%]
Global F1 score (test) = 49.02 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.53      0.43       400
MODERATE-INTENSITY       0.42      0.45      0.43       400
         SEDENTARY       0.67      0.74      0.70       400
VIGOROUS-INTENSITY       0.83      0.26      0.39       345

          accuracy                           0.50      1545
         macro avg       0.57      0.49      0.49      1545
      weighted avg       0.56      0.50      0.49      1545


Accuracy capturado en la ejecución 9: 50.03 [%]
F1-score capturado en la ejecución 9: 49.02 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-07 17:32:06.881532: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:32:06.893468: 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 00:00:1762533126.907616 3452158 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533126.912071 3452158 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533126.921994 3452158 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533126.922015 3452158 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533126.922017 3452158 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533126.922019 3452158 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:32:06.925188: 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 00:00:1762533129.186341 3452158 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533130.774609 3452291 service.cc:152] XLA service 0x7bce84010d30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533130.774642 3452291 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:32:10.805538: 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 00:00:1762533130.979469 3452291 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533132.648561 3452291 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:42[0m 3s/step - accuracy: 0.2188 - loss: 2.0281
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[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2813 - loss: 1.6997
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2025-11-07 17:32:16.668898: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads

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Epoch 2/29

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[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3655 - loss: 1.4627
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3722 - loss: 1.4494
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3743 - loss: 1.4433
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.4381
[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3775 - loss: 1.4330
[1m246/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3783 - loss: 1.4289
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3790 - loss: 1.4251
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3795 - loss: 1.4217
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Epoch 3/29

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[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3988 - loss: 1.3333
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4001 - loss: 1.3305
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4013 - loss: 1.3285
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Epoch 4/29

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Epoch 5/29

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Epoch 6/29

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[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.2053
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4494 - loss: 1.2048
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Epoch 7/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.2044 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.1975
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1904
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[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1838
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[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4631 - loss: 1.1813
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4633 - loss: 1.1805 - val_accuracy: 0.4645 - val_loss: 1.1531
Epoch 8/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4534 - loss: 1.1886 
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[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1607
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1615
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1620
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4689 - loss: 1.1621 - val_accuracy: 0.4711 - val_loss: 1.1470
Epoch 9/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1322 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1324
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4812 - loss: 1.1389
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1435
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4823 - loss: 1.1464
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1480
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1488
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1488
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4837 - loss: 1.1485 - val_accuracy: 0.4717 - val_loss: 1.1364
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1219
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0816 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.1031
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1153
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1230
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1272
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1284
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1286
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1291
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1297
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4938 - loss: 1.1298 - val_accuracy: 0.4760 - val_loss: 1.1303
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0271
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.1107 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.1072
[1m104/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.1069
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.1076
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5051 - loss: 1.1091
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.1105
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1108
[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1106
[1m316/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.1101
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5027 - loss: 1.1099 - val_accuracy: 0.4717 - val_loss: 1.1254
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5625 - loss: 1.0108
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4895 - loss: 1.0955 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.0921
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.0895
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.0922
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.0947
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.0959
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.0971
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.0983
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4996 - loss: 1.0990 - val_accuracy: 0.4763 - val_loss: 1.1205
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1855
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1161 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1107
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.1084
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1072
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[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1068
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Epoch 14/29

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[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0886
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Epoch 15/29

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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0939
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0930
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0919
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0903
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0888
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5167 - loss: 1.0885 - val_accuracy: 0.4849 - val_loss: 1.1032
Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5625 - loss: 1.1369
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5096 - loss: 1.0606 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.0710
[1m102/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5053 - loss: 1.0753
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0749
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0738
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.0730
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0725
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.0715
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0708
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5162 - loss: 1.0706 - val_accuracy: 0.4800 - val_loss: 1.0933
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0188
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0638 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0574
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0623
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[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0650
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0660
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0663
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Epoch 18/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5098 - loss: 1.0644 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0631
[1m103/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0618
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0597
[1m174/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0583
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0572
[1m245/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0563
[1m280/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0560
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0564
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5243 - loss: 1.0565 - val_accuracy: 0.4859 - val_loss: 1.0923
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1668
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.0917 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.0592
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0511
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0489
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0482
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0476
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.0470
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0467
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0469
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Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0843
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0524 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0475
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0442
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0422
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0407
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0402
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0399
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0393
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5318 - loss: 1.0393 - val_accuracy: 0.4842 - val_loss: 1.0835
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1294
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0671 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0559
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0504
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0472
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0462
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0459
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5289 - loss: 1.0454
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0446
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0440
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5301 - loss: 1.0439 - val_accuracy: 0.4869 - val_loss: 1.0802
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4688 - loss: 1.1442
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0602 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0476
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0401
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0359
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[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0321
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0322
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0323
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0324
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5405 - loss: 1.0325 - val_accuracy: 0.4895 - val_loss: 1.0808
Epoch 23/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.0669 
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[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0515
[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0483
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0410
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0397
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5331 - loss: 1.0388 - val_accuracy: 0.4964 - val_loss: 1.0725
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0905
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[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5535 - loss: 1.0145
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 1.0153
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5514 - loss: 1.0164
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5502 - loss: 1.0180
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0186
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 1.0192
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5487 - loss: 1.0193
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5487 - loss: 1.0193 - val_accuracy: 0.4924 - val_loss: 1.0768
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9772
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5615 - loss: 0.9947 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5629 - loss: 0.9975
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5615 - loss: 0.9990
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5602 - loss: 1.0013
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5581 - loss: 1.0039
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5561 - loss: 1.0065
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 1.0086
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5534 - loss: 1.0094
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5529 - loss: 1.0098 - val_accuracy: 0.4954 - val_loss: 1.0726
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9594
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0260 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0237
[1m104/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0194
[1m139/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0166
[1m175/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0155
[1m209/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0140
[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0125
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 1.0118
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0112
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5477 - loss: 1.0111 - val_accuracy: 0.4987 - val_loss: 1.0695
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1960
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Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 426ms/step2025-11-07 17:32:36.465828: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

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[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 51.35 [%]
Global F1 score (validation) = 50.28 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.28807777 0.41045642 0.05385361 0.24761227]
 [0.15896568 0.23979339 0.01995694 0.5812839 ]
 [0.19794442 0.30995804 0.10192139 0.3901762 ]
 ...
 [0.06210063 0.11438881 0.79681623 0.02669431]
 [0.00283145 0.00314028 0.9927574  0.001271  ]
 [0.15373722 0.21278875 0.5362573  0.09721681]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.73 [%]
Global accuracy score (test) = 52.62 [%]
Global F1 score (train) = 58.54 [%]
Global F1 score (test) = 51.29 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.34      0.36       400
MODERATE-INTENSITY       0.46      0.65      0.53       400
         SEDENTARY       0.62      0.76      0.68       400
VIGOROUS-INTENSITY       0.80      0.33      0.47       345

          accuracy                           0.53      1545
         macro avg       0.57      0.52      0.51      1545
      weighted avg       0.56      0.53      0.51      1545


Accuracy capturado en la ejecución 10: 52.62 [%]
F1-score capturado en la ejecución 10: 51.29 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
2025-11-07 17:32:49.192163: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:32:49.203385: 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 00:00:1762533169.216585 3455999 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533169.220547 3455999 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533169.230734 3455999 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533169.230756 3455999 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533169.230758 3455999 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533169.230760 3455999 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:32:49.234014: 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 00:00:1762533171.465507 3455999 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533173.082090 3456108 service.cc:152] XLA service 0x7cdc8800f8c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533173.082120 3456108 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:32:53.112649: 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 00:00:1762533173.295605 3456108 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533174.923814 3456108 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:42[0m 3s/step - accuracy: 0.1875 - loss: 2.0660
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[1m243/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2811 - loss: 1.7838
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2025-11-07 17:32:58.895128: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3807 - loss: 1.4575 
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.4675
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Epoch 3/29

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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.3772
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Epoch 4/29

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Epoch 5/29

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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4198 - loss: 1.2819
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Epoch 6/29

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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.2040
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4480 - loss: 1.2077
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.2106
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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4460 - loss: 1.2149
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4457 - loss: 1.2156
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Epoch 7/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.5625 - loss: 1.3193
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.2015 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4619 - loss: 1.2020
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.2044
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[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4568 - loss: 1.2014
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Epoch 8/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4478 - loss: 1.1603 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4568 - loss: 1.1592
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1599
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1642
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1652
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4615 - loss: 1.1656
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4622 - loss: 1.1657
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1660
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Epoch 9/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.5312 - loss: 1.0640
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1497
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1508
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Epoch 10/29

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[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1394 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1356
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4689 - loss: 1.1433
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1450
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1445
[1m269/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1442
[1m306/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1437
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4738 - loss: 1.1434 - val_accuracy: 0.4625 - val_loss: 1.1445
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.3193
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1712 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1575
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1536
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1508
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1500
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1493
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1487
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4815 - loss: 1.1474
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1459
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4827 - loss: 1.1456 - val_accuracy: 0.4721 - val_loss: 1.1400
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0875
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.1162 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.1240
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1262
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1262
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1257
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1244
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1234
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1229
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Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1384
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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5073 - loss: 1.0858
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.0873
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Epoch 14/29

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[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.0937
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.0935
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.0932
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1194
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4937 - loss: 1.0902 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.0921
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.0933
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.0950
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.0951
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.0947
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.0937
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.0928
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0917
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5062 - loss: 1.0916 - val_accuracy: 0.4869 - val_loss: 1.1192
Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.1352
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.0962 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0884
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0816
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[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0761
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0750
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0744
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0741
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Epoch 17/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5032 - loss: 1.0817 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0728
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0737
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0765
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0776
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0772
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0766
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0759
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0753
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5107 - loss: 1.0752 - val_accuracy: 0.4911 - val_loss: 1.1093
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1826
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0936 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0794
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0750
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.0719
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0715
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0710
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0699
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0687
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0672
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Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2020
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.0781 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.0690
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0657
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0615
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0586
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0563
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0544
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0533
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0522
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5285 - loss: 1.0521 - val_accuracy: 0.4924 - val_loss: 1.1029
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0464
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5241 - loss: 1.0116 
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5242 - loss: 1.0185
[1m102/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5245 - loss: 1.0221
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0258
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0258
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0272
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0285
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0299
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0313
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5283 - loss: 1.0316 - val_accuracy: 0.5013 - val_loss: 1.0942
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6562 - loss: 0.7601
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0289 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0327
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[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0299
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0297
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0295
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0297
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0299
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5381 - loss: 1.0300 - val_accuracy: 0.5049 - val_loss: 1.0895
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.4634
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0601 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0411
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0369
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0342
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0314
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0295
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0285
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0278
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5389 - loss: 1.0275 - val_accuracy: 0.5023 - val_loss: 1.0913
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0748
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0775 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0566
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0493
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0453
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0426
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0408
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0392
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0377
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0356
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5327 - loss: 1.0355 - val_accuracy: 0.5046 - val_loss: 1.0866
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9605
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0198 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0204
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0163
[1m155/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5461 - loss: 1.0140
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0127
[1m232/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0125
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0122
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5481 - loss: 1.0123
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5480 - loss: 1.0124 - val_accuracy: 0.5039 - val_loss: 1.0808
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9377
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5327 - loss: 1.0424 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0403
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0374
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0340
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0315
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0286
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0260
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0240
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5402 - loss: 1.0226 - val_accuracy: 0.5066 - val_loss: 1.0812
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1471
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Epoch 27/29

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Epoch 28/29

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[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5669 - loss: 0.9838
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Epoch 29/29

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:47[0m 1s/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 24ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m60/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 860us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
Global accuracy score (validation) = 50.2 [%]
Global F1 score (validation) = 48.73 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.40777624 0.27574813 0.15162411 0.16485146]
 [0.40444264 0.3189926  0.12040057 0.1561642 ]
 [0.34244156 0.43074328 0.05695983 0.16985539]
 ...
 [0.04925532 0.06100352 0.8741683  0.01557284]
 [0.13709483 0.1611434  0.63733244 0.06442929]
 [0.00848118 0.0077557  0.9822233  0.00153984]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.9 [%]
Global accuracy score (test) = 50.61 [%]
Global F1 score (train) = 57.58 [%]
Global F1 score (test) = 49.44 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.32      0.34       400
MODERATE-INTENSITY       0.45      0.69      0.54       400
         SEDENTARY       0.61      0.68      0.64       400
VIGOROUS-INTENSITY       0.80      0.32      0.46       345

          accuracy                           0.51      1545
         macro avg       0.56      0.50      0.49      1545
      weighted avg       0.55      0.51      0.50      1545


Accuracy capturado en la ejecución 11: 50.61 [%]
F1-score capturado en la ejecución 11: 49.44 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
2025-11-07 17:33:31.096680: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:33:31.108654: 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 00:00:1762533211.122412 3459808 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533211.126573 3459808 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533211.136691 3459808 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533211.136711 3459808 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533211.136713 3459808 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533211.136715 3459808 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:33:31.139996: 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 00:00:1762533213.390103 3459808 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533214.989737 3459930 service.cc:152] XLA service 0x751ae800cf40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533214.989780 3459930 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:33:35.028668: 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 00:00:1762533215.209586 3459930 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533216.877082 3459930 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:52[0m 3s/step - accuracy: 0.2188 - loss: 1.7210
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2314 - loss: 1.8123  
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2479 - loss: 1.7771
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2574 - loss: 1.7521
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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2699 - loss: 1.7128
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2735 - loss: 1.7019
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2025-11-07 17:33:40.935642: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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Epoch 3/29

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Epoch 4/29

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[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2732
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4181 - loss: 1.2677
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Epoch 5/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4116 - loss: 1.2505 
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4251 - loss: 1.2352
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4276 - loss: 1.2321
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4298 - loss: 1.2296
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4314 - loss: 1.2282
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4326 - loss: 1.2271
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4337 - loss: 1.2259
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Epoch 6/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1711
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1511 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4632 - loss: 1.1605
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[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4589 - loss: 1.1701
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1743
[1m230/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1779
[1m267/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4563 - loss: 1.1802
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Epoch 7/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1864 
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[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1701
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1683
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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4700 - loss: 1.1652
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Epoch 8/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4407 - loss: 1.1987 
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Epoch 9/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1186 
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[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1089
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1113
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4873 - loss: 1.1130
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1141
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1154
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4866 - loss: 1.1156 - val_accuracy: 0.5003 - val_loss: 1.1180
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2283
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1320 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1406
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1422
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1420
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1409
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1400
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1394
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1385
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1378
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4829 - loss: 1.1376 - val_accuracy: 0.4941 - val_loss: 1.1142
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0320
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0889 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.0975
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1069
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[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1102
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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1115
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1116
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Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1511
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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1392
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[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1203
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[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.1105
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.1090
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Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1294
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4998 - loss: 1.0781 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.0888
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.0909
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.0925
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5006 - loss: 1.0937
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.0940
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.0933
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.0927
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.0920
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Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 0.7631
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0655 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0769
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0839
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0845
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0848
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0851
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0848
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0847
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5153 - loss: 1.0847 - val_accuracy: 0.4951 - val_loss: 1.0915
Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1295
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.0857 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0825
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0767
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0744
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0745
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0749
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0751
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0749
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0749
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5196 - loss: 1.0749 - val_accuracy: 0.4934 - val_loss: 1.0875
Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1795
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0912 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.0847
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0725
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[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0682
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0678
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Epoch 17/29

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[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0483
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0486
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Epoch 18/29

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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0510
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0527
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0537
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0544
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0550
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5285 - loss: 1.0550 - val_accuracy: 0.5010 - val_loss: 1.0734
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.7188 - loss: 0.8974
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5478 - loss: 1.0527 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5371 - loss: 1.0563
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0569
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5346 - loss: 1.0567
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0565
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0560
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5334 - loss: 1.0554
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0548
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5329 - loss: 1.0546 - val_accuracy: 0.4984 - val_loss: 1.0696
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.5625 - loss: 0.9557
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0523 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5255 - loss: 1.0531
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[1m137/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0433
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[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0411
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[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0410
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0408
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Epoch 21/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 0.9948 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0061
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0135
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0208
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0249
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Epoch 22/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5743 - loss: 0.9921 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5661 - loss: 0.9952
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5555 - loss: 1.0059
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0119
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0166
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 1.0200
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5428 - loss: 1.0233 - val_accuracy: 0.5062 - val_loss: 1.0504
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6875 - loss: 0.6756
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5109 - loss: 1.0418 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0372
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5289 - loss: 1.0293
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0267
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5322 - loss: 1.0253
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0239
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0233
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5339 - loss: 1.0230 - val_accuracy: 0.5062 - val_loss: 1.0533
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.8935
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5759 - loss: 0.9784 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5640 - loss: 0.9965
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5592 - loss: 1.0050
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 1.0098
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 1.0123
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5505 - loss: 1.0135
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5493 - loss: 1.0139
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0141
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5476 - loss: 1.0146
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Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.8475
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5602 - loss: 0.9447 
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5527 - loss: 0.9855
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 0.9898
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[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 0.9945
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5493 - loss: 0.9963
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Epoch 26/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0032 
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5492 - loss: 1.0060
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[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0059
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Epoch 27/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0247 
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[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0118
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0110
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Epoch 28/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5497 - loss: 1.0118 
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[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0121
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5490 - loss: 1.0084
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 1.0067
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5503 - loss: 1.0050
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5509 - loss: 1.0038
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 1.0028
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5513 - loss: 1.0021 - val_accuracy: 0.5187 - val_loss: 1.0295
Epoch 29/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5509 - loss: 0.9824 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5556 - loss: 0.9845
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5560 - loss: 0.9873
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5549 - loss: 0.9898
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5541 - loss: 0.9901
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 0.9888
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 0.9884
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 0.9884
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5539 - loss: 0.9888
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5537 - loss: 0.9889 - val_accuracy: 0.5200 - val_loss: 1.0328

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 424ms/step2025-11-07 17:34:00.689385: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads

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Saved model to disk.
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:34[0m 1s/step
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 799us/step
[1m130/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 781us/step
[1m195/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 780us/step
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 763us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 23ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 23ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 749us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 50.85 [%]
Global F1 score (validation) = 51.34 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.26751152 0.3291928  0.06709241 0.33620334]
 [0.33494383 0.27835986 0.07020372 0.31649256]
 [0.32889068 0.46101946 0.03873095 0.17135885]
 ...
 [0.00294395 0.00457038 0.9891454  0.00334024]
 [0.06470132 0.11623579 0.730463   0.08859994]
 [0.00289663 0.00452677 0.9893532  0.00322348]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.55 [%]
Global accuracy score (test) = 52.49 [%]
Global F1 score (train) = 60.07 [%]
Global F1 score (test) = 52.7 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.36      0.36       400
MODERATE-INTENSITY       0.46      0.59      0.51       400
         SEDENTARY       0.69      0.69      0.69       400
VIGOROUS-INTENSITY       0.70      0.45      0.55       345

          accuracy                           0.52      1545
         macro avg       0.55      0.52      0.53      1545
      weighted avg       0.54      0.52      0.53      1545


Accuracy capturado en la ejecución 12: 52.49 [%]
F1-score capturado en la ejecución 12: 52.7 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:07[0m 1s/step
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 807us/step
[1m132/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 771us/step
[1m200/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 759us/step
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 760us/step
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 765us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 23ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 24ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 782us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 51.81 [%]
Global F1 score (validation) = 51.46 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3815576  0.33163428 0.11704151 0.1697667 ]
 [0.36299938 0.32991534 0.12389422 0.18319102]
 [0.29324818 0.32028022 0.0520109  0.33446068]
 ...
 [0.00349729 0.00569539 0.98929757 0.0015098 ]
 [0.00347315 0.0056775  0.98938006 0.00146922]
 [0.0563878  0.06957576 0.8477689  0.02626751]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.81 [%]
Global accuracy score (test) = 52.1 [%]
Global F1 score (train) = 58.63 [%]
Global F1 score (test) = 51.92 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.52      0.45       400
MODERATE-INTENSITY       0.45      0.45      0.45       400
         SEDENTARY       0.63      0.72      0.67       400
VIGOROUS-INTENSITY       0.77      0.37      0.50       345

          accuracy                           0.52      1545
         macro avg       0.56      0.52      0.52      1545
      weighted avg       0.56      0.52      0.52      1545


Accuracy capturado en la ejecución 13: 52.1 [%]
2025-11-07 17:34:13.372885: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:34:13.384195: 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 00:00:1762533253.397372 3463624 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533253.401521 3463624 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533253.411437 3463624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533253.411456 3463624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533253.411459 3463624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533253.411461 3463624 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:34:13.414649: 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 00:00:1762533255.680239 3463624 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533257.266480 3463761 service.cc:152] XLA service 0x7fc1b410e1f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533257.266522 3463761 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:34:17.302590: 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 00:00:1762533257.485308 3463761 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533259.130643 3463761 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:41[0m 3s/step - accuracy: 0.2500 - loss: 2.1005
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[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2679 - loss: 1.7934
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2714 - loss: 1.7838
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2755 - loss: 1.7721
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2803 - loss: 1.7602
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2844 - loss: 1.7495
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2890 - loss: 1.7377
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2924 - loss: 1.7288
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2932 - loss: 1.72662025-11-07 17:34:21.937029: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:34:23.219122: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2933 - loss: 1.7263 - val_accuracy: 0.3798 - val_loss: 1.2941
Epoch 2/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.5209
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.4681 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3778 - loss: 1.4805
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3743 - loss: 1.4814
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3733 - loss: 1.4797
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3734 - loss: 1.4762
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3744 - loss: 1.4715
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3757 - loss: 1.4663
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3770 - loss: 1.4620
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3778 - loss: 1.4591
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3779 - loss: 1.4587 - val_accuracy: 0.4261 - val_loss: 1.2545
Epoch 3/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.5094
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3903 - loss: 1.4098 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3946 - loss: 1.3978
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3957 - loss: 1.3910
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3962 - loss: 1.3874
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3963 - loss: 1.3859
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3968 - loss: 1.3840
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3973 - loss: 1.3818
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3977 - loss: 1.3796
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3983 - loss: 1.3775 - val_accuracy: 0.4491 - val_loss: 1.2405
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0743
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4433 - loss: 1.2902 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4305 - loss: 1.3140
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4225 - loss: 1.3238
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.3269
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4194 - loss: 1.3256
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4185 - loss: 1.3254
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Epoch 5/29

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Epoch 6/29

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[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4460 - loss: 1.2321
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Epoch 7/29

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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4396 - loss: 1.2226
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.2202
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4443 - loss: 1.2185
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.2170
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4470 - loss: 1.2158
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4479 - loss: 1.2146
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Epoch 8/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.3750 - loss: 1.2847
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.2262 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4469 - loss: 1.2081
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.2016
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1987
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1972
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1958
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Epoch 9/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1891 
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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.1978
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4558 - loss: 1.1963
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4558 - loss: 1.1957
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.1955
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4562 - loss: 1.1951
[1m316/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1946
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4566 - loss: 1.1942 - val_accuracy: 0.4721 - val_loss: 1.1399
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9832
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4899 - loss: 1.1593 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1687
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1673
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1653
[1m175/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1628
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1601
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1587
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1579
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1570
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4831 - loss: 1.1569 - val_accuracy: 0.4760 - val_loss: 1.1302
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3125 - loss: 1.3111
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4544 - loss: 1.1804 
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4680 - loss: 1.1606
[1m101/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4734 - loss: 1.1497
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1435
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1410
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1384
[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1370
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1364
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4847 - loss: 1.1364
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4848 - loss: 1.1363 - val_accuracy: 0.4777 - val_loss: 1.1222
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0908
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1022 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1061
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1063
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1070
[1m193/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1081
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1101
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1124
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1141
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4933 - loss: 1.1156 - val_accuracy: 0.4918 - val_loss: 1.1169
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2721
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1331 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.1205
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.1156
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.1123
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.1109
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.1106
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5058 - loss: 1.1111
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1118
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5042 - loss: 1.1124 - val_accuracy: 0.5076 - val_loss: 1.1125
Epoch 14/29

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[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1101
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1102
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0991
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[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1258
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1211
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1178
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1152
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1130
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Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4688 - loss: 1.0622
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[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.1019
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0941
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0920
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0898
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5194 - loss: 1.0882
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0867
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5200 - loss: 1.0866 - val_accuracy: 0.5099 - val_loss: 1.0905
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 0.9287
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0207 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0445
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0552
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0561
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0575
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0593
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0605
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0616
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Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9185
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5193 - loss: 1.0318 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0450
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[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0567
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0573
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0577
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5239 - loss: 1.0577 - val_accuracy: 0.5115 - val_loss: 1.0759
Epoch 19/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0757 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0768
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0635
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[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0572
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5330 - loss: 1.0564
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5329 - loss: 1.0558 - val_accuracy: 0.5089 - val_loss: 1.0713
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 0.9981
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5673 - loss: 1.0076 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5526 - loss: 1.0288
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0463
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0476
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0482
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0486
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0485
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0487
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5346 - loss: 1.0487 - val_accuracy: 0.5135 - val_loss: 1.0663
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0298
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 1.0253 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0312
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0324
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0346
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0374
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0389
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0397
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0401
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0403
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5381 - loss: 1.0404 - val_accuracy: 0.5148 - val_loss: 1.0639
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1792
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0378 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0289
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0308
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0308
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0302
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0295
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0292
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0294
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5389 - loss: 1.0297 - val_accuracy: 0.5161 - val_loss: 1.0609
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8732
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0012 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0050
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0166
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0181
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Epoch 24/29

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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5472 - loss: 1.0175
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Epoch 25/29

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[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 1.0270
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 1.0259
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5518 - loss: 1.0242
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0228
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5516 - loss: 1.0217
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Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.8581
[1m 31/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5103 - loss: 1.0080 
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5226 - loss: 1.0078
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[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0094
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0094
[1m208/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0096
[1m242/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0090
[1m278/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0088
[1m315/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0089
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Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7601
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5556 - loss: 0.9969 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5530 - loss: 1.0001
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 0.9996
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Epoch 28/29

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[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5548 - loss: 0.9882
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 0.9903
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Epoch 29/29

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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5410 - loss: 1.0066 - val_accuracy: 0.5171 - val_loss: 1.0424

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 431ms/step2025-11-07 17:34:42.882753: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 27ms/step
Saved model to disk.
F1-score capturado en la ejecución 13: 51.92 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:35[0m 1s/step
[1m 55/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 933us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m62/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 820us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 52.46 [%]
Global F1 score (validation) = 51.27 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.10591015 0.37812516 0.05738612 0.45857862]
 [0.37794977 0.36960608 0.0853188  0.16712533]
 [0.37794977 0.36960608 0.0853188  0.16712533]
 ...
 [0.06033162 0.10557581 0.76369536 0.07039716]
 [0.13718997 0.16612498 0.5551716  0.1415134 ]
 [0.00398255 0.00721554 0.98695624 0.0018456 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.42 [%]
Global accuracy score (test) = 52.43 [%]
Global F1 score (train) = 58.95 [%]
Global F1 score (test) = 51.2 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.27      0.32       400
MODERATE-INTENSITY       0.43      0.69      0.53       400
         SEDENTARY       0.66      0.73      0.69       400
VIGOROUS-INTENSITY       0.75      0.38      0.50       345

          accuracy                           0.52      1545
         macro avg       0.56      0.52      0.51      1545
      weighted avg       0.55      0.52      0.51      1545


Accuracy capturado en la ejecución 14: 52.43 [%]
2025-11-07 17:34:55.353931: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:34:55.365329: 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 00:00:1762533295.379270 3467475 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533295.383540 3467475 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533295.393785 3467475 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533295.393807 3467475 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533295.393809 3467475 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533295.393810 3467475 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:34:55.396956: 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 00:00:1762533297.638892 3467475 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533299.231288 3467582 service.cc:152] XLA service 0x76875c005b90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533299.231341 3467582 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:34:59.267043: 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 00:00:1762533299.441662 3467582 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533301.075273 3467582 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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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2437 - loss: 1.7614
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[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2535 - loss: 1.7343
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2573 - loss: 1.7229
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2025-11-07 17:35:05.159241: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads

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Epoch 2/29

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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.4941
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3421 - loss: 1.4830
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[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.4686
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3492 - loss: 1.4631
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Epoch 3/29

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[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3969 - loss: 1.3287
[1m174/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3966 - loss: 1.3309
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[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3963 - loss: 1.3345
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3965 - loss: 1.3341
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Epoch 4/29

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Epoch 5/29

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Epoch 6/29

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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4396 - loss: 1.2134
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.2125
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Epoch 7/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4113 - loss: 1.2494 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4310 - loss: 1.2237
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4455 - loss: 1.2053
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[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4488 - loss: 1.1992
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.1966
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.1954
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4512 - loss: 1.1943 - val_accuracy: 0.4694 - val_loss: 1.1398
Epoch 8/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1781 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4657 - loss: 1.1744
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4589 - loss: 1.1817
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[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4590 - loss: 1.1767
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Epoch 9/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.1989 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4515 - loss: 1.1871
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4533 - loss: 1.1826
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1774
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.1733
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1701
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1675
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1655
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4646 - loss: 1.1633 - val_accuracy: 0.4740 - val_loss: 1.1249
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.2812 - loss: 1.3115
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4574 - loss: 1.1528 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1488
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1453
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1444
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1445
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1439
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1434
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1432
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4717 - loss: 1.1427 - val_accuracy: 0.4790 - val_loss: 1.1161
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1024
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4609 - loss: 1.1496 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1449
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1388
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1355
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1335
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[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1313
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1306
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1303
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4748 - loss: 1.1302 - val_accuracy: 0.4770 - val_loss: 1.1096
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.0509
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1299 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1236
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1194
[1m156/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1168
[1m195/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1153
[1m234/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1146
[1m272/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1149
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1144
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4930 - loss: 1.1138 - val_accuracy: 0.4865 - val_loss: 1.0978
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0555
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1178 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1160
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.1160
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1143
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1134
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1124
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1112
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1103
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Epoch 14/29

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[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0693
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9783
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5292 - loss: 1.0561 
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[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0768
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0764
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0764
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Epoch 16/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4924 - loss: 1.0930 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.0781
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[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.0704
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.0713
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0724
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0730
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.0733
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Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0757
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0707 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0701
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0680
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0668
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0660
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0656
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0656
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0655
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0655
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Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2472
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0859 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0714
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0700
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[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0707
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0704
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Epoch 19/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.0719 
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[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0610
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0609
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[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0612
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0620
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0624
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Epoch 20/29

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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0455
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[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0483
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0493
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0497
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5298 - loss: 1.0497 - val_accuracy: 0.5069 - val_loss: 1.0619
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0925
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5557 - loss: 0.9696 
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0143
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0179
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0216
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0243
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0260
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0273
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5343 - loss: 1.0274 - val_accuracy: 0.5059 - val_loss: 1.0564
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.0568
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0233 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0310
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0331
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5344 - loss: 1.0345
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5344 - loss: 1.0352
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5344 - loss: 1.0362
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0371
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0373
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0373
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Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9264
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5516 - loss: 0.9961 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5471 - loss: 1.0092
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0150
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0169
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[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0194
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0202
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Epoch 24/29

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[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 1.0008
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5472 - loss: 1.0104
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[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0149
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0158
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5413 - loss: 1.0168
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Epoch 25/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0345 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0230
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0202
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[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0179
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0178
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0180
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5397 - loss: 1.0179 - val_accuracy: 0.5125 - val_loss: 1.0493
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.7500 - loss: 0.7377
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5650 - loss: 0.9673 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5637 - loss: 0.9763
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5594 - loss: 0.9867
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5571 - loss: 0.9906
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 0.9935
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 0.9955
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 0.9977
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5513 - loss: 0.9993 - val_accuracy: 0.5112 - val_loss: 1.0456
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3125 - loss: 1.1779
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0424 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0311
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0172
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5393 - loss: 1.0150
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0126
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0109
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0098
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0094
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Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 419ms/step2025-11-07 17:35:24.552923: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads

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Saved model to disk.
F1-score capturado en la ejecución 14: 51.2 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:29[0m 1s/step
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[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 760us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
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Global accuracy score (validation) = 50.49 [%]
Global F1 score (validation) = 50.72 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.34441626 0.37396482 0.02176975 0.25984916]
 [0.410386   0.37051392 0.0749742  0.1441259 ]
 [0.13819611 0.21638039 0.01739973 0.6280238 ]
 ...
 [0.00536257 0.00449047 0.9867071  0.00343975]
 [0.05469755 0.04123217 0.8532361  0.05083413]
 [0.03524198 0.02912669 0.90461206 0.03101927]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.78 [%]
Global accuracy score (test) = 50.49 [%]
Global F1 score (train) = 58.99 [%]
Global F1 score (test) = 50.71 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.39      0.38       400
MODERATE-INTENSITY       0.41      0.50      0.45       400
         SEDENTARY       0.68      0.71      0.70       400
VIGOROUS-INTENSITY       0.68      0.40      0.51       345

          accuracy                           0.50      1545
         macro avg       0.53      0.50      0.51      1545
      weighted avg       0.53      0.50      0.51      1545


Accuracy capturado en la ejecución 15: 50.49 [%]
F1-score capturado en la ejecución 15: 50.71 [%]
2025-11-07 17:35:37.397280: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:35:37.408591: 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 00:00:1762533337.421783 3471300 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533337.425908 3471300 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533337.435742 3471300 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533337.435760 3471300 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533337.435762 3471300 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533337.435771 3471300 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:35:37.439034: 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 00:00:1762533339.688630 3471300 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533341.253037 3471429 service.cc:152] XLA service 0x70b56c1101d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533341.253064 3471429 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:35:41.282983: 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 00:00:1762533341.457852 3471429 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533343.121408 3471429 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:38[0m 3s/step - accuracy: 0.2188 - loss: 2.0855
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2243 - loss: 1.8224  
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2418 - loss: 1.7878
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2541 - loss: 1.7594
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2619 - loss: 1.7399
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2691 - loss: 1.7223
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2746 - loss: 1.7083
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2785 - loss: 1.6982
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2830 - loss: 1.6868
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2869 - loss: 1.6767
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.2875 - loss: 1.67502025-11-07 17:35:45.952514: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:35:47.229440: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2876 - loss: 1.6747 - val_accuracy: 0.3850 - val_loss: 1.3026
Epoch 2/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.7170
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3865 - loss: 1.4923 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3795 - loss: 1.4817
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3771 - loss: 1.4730
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3768 - loss: 1.4632
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.4560
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.4507
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3766 - loss: 1.4457
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3772 - loss: 1.4410
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3778 - loss: 1.4369
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3779 - loss: 1.4365 - val_accuracy: 0.4208 - val_loss: 1.2115
Epoch 3/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3125 - loss: 1.3767
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3972 - loss: 1.3616 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3963 - loss: 1.3530
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3978 - loss: 1.3473
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3992 - loss: 1.3432
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4002 - loss: 1.3399
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4011 - loss: 1.3368
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4018 - loss: 1.3339
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4025 - loss: 1.3313
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4029 - loss: 1.3294
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4030 - loss: 1.3291 - val_accuracy: 0.4300 - val_loss: 1.1886
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3960
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4049 - loss: 1.3256 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4106 - loss: 1.3115
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4116 - loss: 1.3066
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4121 - loss: 1.3051
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[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4162 - loss: 1.2986
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4173 - loss: 1.2965
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4182 - loss: 1.2941
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Epoch 5/29

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[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4362 - loss: 1.2410
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[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.2377
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Epoch 6/29

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[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4652 - loss: 1.1828
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1923
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4569 - loss: 1.1951
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[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1973
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Epoch 7/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3750 - loss: 1.1673
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4476 - loss: 1.2265 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4523 - loss: 1.2093
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.2041
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4521 - loss: 1.2023
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4525 - loss: 1.2014
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4530 - loss: 1.2010
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4535 - loss: 1.1999
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1989
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4544 - loss: 1.1984 - val_accuracy: 0.4806 - val_loss: 1.1340
Epoch 8/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.3739
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.2341 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.2139
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.2036
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1952
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[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4677 - loss: 1.1826
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1788
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1758
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Epoch 9/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4817 - loss: 1.1576 
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[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1641
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4726 - loss: 1.1633
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1621
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1614
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1607
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1596
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1582
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Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3978
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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4638 - loss: 1.1554
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.1529
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1527
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1523
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1498
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1485
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Epoch 11/29

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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1030
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1132
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1160
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1175
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1189
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1202
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1207
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4898 - loss: 1.1207 - val_accuracy: 0.4967 - val_loss: 1.1034
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 0.9869
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1132 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1094
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1086
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1075
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1055
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1044
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1043
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.1044
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4966 - loss: 1.1042 - val_accuracy: 0.5049 - val_loss: 1.0948
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0409
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0699 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0868
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.0925
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.0956
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.0963
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[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.0961
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.0963
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5025 - loss: 1.0960 - val_accuracy: 0.5102 - val_loss: 1.0923
Epoch 14/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.1082 
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[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0905
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0891
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Epoch 15/29

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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0749
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0752
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.0754
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0755
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Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0802
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0458 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0592
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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0715
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.0726
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.0727
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0726
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0724
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5133 - loss: 1.0723 - val_accuracy: 0.5171 - val_loss: 1.0762
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.5938 - loss: 1.0426
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0781 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0744
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0735
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0727
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0736
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0737
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0731
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0724
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0719
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Epoch 18/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5493 - loss: 1.0306 
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[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0457
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0466
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0475
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0481
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0484
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Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0904
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[1m 63/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5314 - loss: 1.0497
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[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0420
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[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0409
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0407
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Epoch 20/29

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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0055
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0096
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0125
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0149
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0168
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0182
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5394 - loss: 1.0184 - val_accuracy: 0.5250 - val_loss: 1.0559
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3438 - loss: 1.2022
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0262 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0278
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0293
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5330 - loss: 1.0293
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0291
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0281
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0274
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0272
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0268
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5368 - loss: 1.0267 - val_accuracy: 0.5217 - val_loss: 1.0546
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3750 - loss: 1.1156
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0739 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0597
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0413
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0372
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0340
[1m267/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0319
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0306
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5454 - loss: 1.0298 - val_accuracy: 0.5214 - val_loss: 1.0500
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0141
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5514 - loss: 1.0013 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0071
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0118
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0149
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[1m232/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0162
[1m270/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0159
[1m310/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0160
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5445 - loss: 1.0161 - val_accuracy: 0.5256 - val_loss: 1.0420
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 1.2645
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5370 - loss: 1.0512 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5452 - loss: 1.0299
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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0259
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0247
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5459 - loss: 1.0237
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5460 - loss: 1.0220
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0209
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0197
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5456 - loss: 1.0194 - val_accuracy: 0.5246 - val_loss: 1.0442
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1500
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5619 - loss: 0.9877 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5532 - loss: 0.9949
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 0.9995
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5509 - loss: 0.9994
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0005
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5521 - loss: 1.0017
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0029
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0035
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 1.0037
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5520 - loss: 1.0037 - val_accuracy: 0.5250 - val_loss: 1.0371
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0204
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0336 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5330 - loss: 1.0269
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0238
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0199
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0170
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0140
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0120
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0104
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0092
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5455 - loss: 1.0088 - val_accuracy: 0.5276 - val_loss: 1.0339
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.9245
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5631 - loss: 0.9857 
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Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 427ms/step2025-11-07 17:36:06.840539: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 25ms/step
Saved model to disk.
2025-11-07 17:36:19.502926: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:36:19.514353: 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 00:00:1762533379.527623 3475118 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533379.531561 3475118 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533379.542074 3475118 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533379.542094 3475118 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533379.542097 3475118 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533379.542099 3475118 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:36:19.545106: 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 00:00:1762533381.789917 3475118 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533383.399189 3475249 service.cc:152] XLA service 0x736cd8021d30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533383.399219 3475249 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:36:23.440363: 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 00:00:1762533383.626725 3475249 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533385.255779 3475249 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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[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 1.7155
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2755 - loss: 1.6999
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2807 - loss: 1.6854
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2854 - loss: 1.6721
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2025-11-07 17:36:29.367582: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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Epoch 3/29

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[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4232 - loss: 1.2916
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4228 - loss: 1.2914
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4226 - loss: 1.2911
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4225 - loss: 1.2910 - val_accuracy: 0.4389 - val_loss: 1.2073
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2985
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4363 - loss: 1.2772 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.2576
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4445 - loss: 1.2477
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2460
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2449
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.2447
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4429 - loss: 1.2442
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2436
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.2434
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4423 - loss: 1.2434 - val_accuracy: 0.4465 - val_loss: 1.1883
Epoch 5/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2947
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4224 - loss: 1.2735 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2607
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4326 - loss: 1.2540
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[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.2440
[1m230/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.2414
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4368 - loss: 1.2398
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.2386
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Epoch 6/29

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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.2074
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.2057
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Epoch 7/29

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[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.1992
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[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1893
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[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4544 - loss: 1.1856
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.1841
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Epoch 8/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1654 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1619
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1655
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1645
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1623
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1603
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1595
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4711 - loss: 1.1588 - val_accuracy: 0.4668 - val_loss: 1.1239
Epoch 9/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1823
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5043 - loss: 1.1036 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1085
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1148
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1207
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1233
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4960 - loss: 1.1249
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1262
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4936 - loss: 1.1272
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1278
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Epoch 10/29

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[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5078 - loss: 1.1172 
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[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1234
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4936 - loss: 1.1267
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Epoch 11/29

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[1m279/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.1114
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1118
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Epoch 12/29

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[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.0955
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.0966
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0966
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.0970
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.0975
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Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1451
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.0794 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0786
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0773
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0747
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0742
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0748
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0763
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0778
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0791
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5132 - loss: 1.0793 - val_accuracy: 0.4908 - val_loss: 1.0897
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1159
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5059 - loss: 1.0748 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0684
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0739
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0763
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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.0805
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Epoch 15/29

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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0606
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Epoch 16/29

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[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0692
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Epoch 17/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0734 
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[1m174/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0565
[1m212/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0564
[1m246/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0561
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0567
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0571
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5170 - loss: 1.0572 - val_accuracy: 0.4947 - val_loss: 1.0686
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 1.0432
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0406 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0636
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0713
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0716
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0704
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0689
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0678
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Epoch 19/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5434 - loss: 1.0526 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0444
[1m104/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0442
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0443
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0442
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0451
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0464
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0478
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0483
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5243 - loss: 1.0484 - val_accuracy: 0.5020 - val_loss: 1.0580
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7915
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[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5505 - loss: 1.0197
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0307
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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0331
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5371 - loss: 1.0337
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5370 - loss: 1.0337 - val_accuracy: 0.4987 - val_loss: 1.0601
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.8287
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5730 - loss: 1.0108 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5626 - loss: 1.0165
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5502 - loss: 1.0271
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5468 - loss: 1.0292
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0308
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0315
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5413 - loss: 1.0320
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0320
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5406 - loss: 1.0320 - val_accuracy: 0.4964 - val_loss: 1.0548
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0240
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5278 - loss: 1.0556 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0480
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0410
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0358
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0337
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0328
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0319
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0312
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5329 - loss: 1.0306 - val_accuracy: 0.4997 - val_loss: 1.0470
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.0625
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4843 - loss: 1.0818 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.0812
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.0766
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0703
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[1m232/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0604
[1m272/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0566
[1m309/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0534
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5186 - loss: 1.0516 - val_accuracy: 0.4993 - val_loss: 1.0492
Epoch 24/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0421 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0346
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.0328
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0302
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0301
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0305
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0304
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0299
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5297 - loss: 1.0294 - val_accuracy: 0.5033 - val_loss: 1.0416
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6875 - loss: 0.8123
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5722 - loss: 0.9393 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5688 - loss: 0.9602
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5578 - loss: 0.9859
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5549 - loss: 0.9916
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5529 - loss: 0.9959
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 0.9994
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 1.0018
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5488 - loss: 1.0036
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5485 - loss: 1.0041 - val_accuracy: 0.5043 - val_loss: 1.0374
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5000 - loss: 1.3332
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5588 - loss: 1.0378 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5571 - loss: 1.0308
[1m119/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 1.0257
[1m159/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5564 - loss: 1.0229
[1m198/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5560 - loss: 1.0207
[1m233/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5558 - loss: 1.0190
[1m270/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5554 - loss: 1.0178
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5552 - loss: 1.0166
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5551 - loss: 1.0159 - val_accuracy: 0.5066 - val_loss: 1.0333
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9131
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5515 - loss: 1.0104 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 1.0019
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5575 - loss: 1.0041
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 1.0053
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5552 - loss: 1.0057
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 1.0049
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 1.0039
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5547 - loss: 1.0033
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5541 - loss: 1.0029
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Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.8347
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5667 - loss: 0.9523 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5527 - loss: 0.9839
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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 425ms/step2025-11-07 17:36:49.038192: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 24ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 24ms/step
Saved model to disk.

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:32[0m 1s/step
[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 791us/step
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 24ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[1m61/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 838us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 53.12 [%]
Global F1 score (validation) = 52.67 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.25485998 0.5176873  0.02314098 0.20431165]
 [0.42288563 0.26117492 0.1251399  0.19079947]
 [0.33599418 0.50216246 0.00959095 0.15225238]
 ...
 [0.05757535 0.03438483 0.8755135  0.03252639]
 [0.11420779 0.08000331 0.726506   0.07928295]
 [0.09418045 0.05987407 0.78384525 0.06210029]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.7 [%]
Global accuracy score (test) = 50.74 [%]
Global F1 score (train) = 58.63 [%]
Global F1 score (test) = 50.95 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.33      0.34      0.33       400
MODERATE-INTENSITY       0.44      0.58      0.50       400
         SEDENTARY       0.65      0.69      0.67       400
VIGOROUS-INTENSITY       0.76      0.41      0.53       345

          accuracy                           0.51      1545
         macro avg       0.55      0.50      0.51      1545
      weighted avg       0.54      0.51      0.51      1545


Accuracy capturado en la ejecución 16: 50.74 [%]
F1-score capturado en la ejecución 16: 50.95 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:40[0m 1s/step
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[1m133/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 768us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 25ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 800us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 50.39 [%]
Global F1 score (validation) = 49.38 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.27637526 0.3647995  0.0336119  0.3252133 ]
 [0.2708759  0.41019306 0.08126535 0.23766568]
 [0.29898512 0.52564836 0.04345835 0.13190821]
 ...
 [0.02928265 0.03447101 0.9087815  0.02746481]
 [0.02448954 0.03016919 0.92071867 0.02462251]
 [0.14527059 0.2077062  0.54037756 0.10664564]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.68 [%]
Global accuracy score (test) = 52.1 [%]
Global F1 score (train) = 57.3 [%]
Global F1 score (test) = 50.94 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.38      0.39       400
MODERATE-INTENSITY       0.44      0.59      0.50       400
         SEDENTARY       0.64      0.78      0.70       400
VIGOROUS-INTENSITY       0.79      0.31      0.45       345

          accuracy                           0.52      1545
         macro avg       0.56      0.51      0.51      1545
      weighted avg       0.56      0.52      0.51      1545


Accuracy capturado en la ejecución 17: 52.1 [%]
2025-11-07 17:37:01.733945: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:37:01.745297: 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 00:00:1762533421.758255 3478956 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533421.762362 3478956 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533421.772064 3478956 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533421.772083 3478956 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533421.772086 3478956 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533421.772088 3478956 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:37:01.775173: 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 00:00:1762533424.023653 3478956 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533425.630626 3479061 service.cc:152] XLA service 0x7a4d94020e30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533425.630659 3479061 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:37:05.661043: 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 00:00:1762533425.846123 3479061 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533427.507118 3479061 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:51[0m 3s/step - accuracy: 0.3125 - loss: 1.6789
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2697 - loss: 1.7874  
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2688 - loss: 1.7860
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2699 - loss: 1.7750
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2715 - loss: 1.7649
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2734 - loss: 1.7539
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2772 - loss: 1.7414
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2814 - loss: 1.7294
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 1.7182
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2892 - loss: 1.7077
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2025-11-07 17:37:11.596384: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2894 - loss: 1.7072 - val_accuracy: 0.3840 - val_loss: 1.3030
Epoch 2/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3548 - loss: 1.4656 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3591 - loss: 1.4657
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3630 - loss: 1.4633
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3653 - loss: 1.4600
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3670 - loss: 1.4571
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.4537
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3697 - loss: 1.4507
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3710 - loss: 1.4473
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3720 - loss: 1.4448
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Epoch 3/29

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Epoch 4/29

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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.2921
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.2944
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Epoch 5/29

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[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4232 - loss: 1.2795
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[1m268/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4327 - loss: 1.2680
[1m306/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4337 - loss: 1.2659
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4345 - loss: 1.2645 - val_accuracy: 0.4790 - val_loss: 1.1640
Epoch 6/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4449 - loss: 1.2481 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4472 - loss: 1.2432
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[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4507 - loss: 1.2328
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4507 - loss: 1.2323
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4507 - loss: 1.2320 - val_accuracy: 0.4770 - val_loss: 1.1576
Epoch 7/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4468 - loss: 1.2466 
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4521 - loss: 1.2210 - val_accuracy: 0.4813 - val_loss: 1.1452
Epoch 8/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4224 - loss: 1.2635 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4453 - loss: 1.2223
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4541 - loss: 1.2107
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4574 - loss: 1.2054
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.2033
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.2022
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.2008
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1996
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1985
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Epoch 9/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1554 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1680
[1m118/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1730
[1m157/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1738
[1m197/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1736
[1m235/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1727
[1m273/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4723 - loss: 1.1723
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1720
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Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1687
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.1690 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4537 - loss: 1.1679
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1654
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4618 - loss: 1.1630
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1614
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4669 - loss: 1.1599
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1583
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1572
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1559
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4708 - loss: 1.1555 - val_accuracy: 0.4898 - val_loss: 1.1205
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3750 - loss: 1.1990
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1428 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1391
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1380
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1419
[1m193/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1430
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1432
[1m269/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1436
[1m309/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1435
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4871 - loss: 1.1432 - val_accuracy: 0.5003 - val_loss: 1.1073
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0381
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1100 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1148
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1156
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[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1177
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[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1190
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.1195
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Epoch 13/29

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[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1285
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[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1256
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Epoch 14/29

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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.1105
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1096
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1089
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1084
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1080
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0709
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0851 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0928
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.0964
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0985
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.0991
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.0995
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.0999
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1001
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5047 - loss: 1.0997 - val_accuracy: 0.4997 - val_loss: 1.0837
Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8629
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5568 - loss: 0.9913 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0224
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0383
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0501
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0575
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0616
[1m268/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0647
[1m307/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0668
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5192 - loss: 1.0678 - val_accuracy: 0.5039 - val_loss: 1.0812
Epoch 17/29

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[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0720 
[1m 80/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0739
[1m119/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0800
[1m158/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0840
[1m197/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0849
[1m231/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0851
[1m269/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0854
[1m307/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0854
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5148 - loss: 1.0855 - val_accuracy: 0.5076 - val_loss: 1.0756
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.6562 - loss: 0.7596
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0475 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0489
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0555
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0597
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0627
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0650
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0670
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0687
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5185 - loss: 1.0695 - val_accuracy: 0.5095 - val_loss: 1.0682
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0007
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4945 - loss: 1.0783 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.0814
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.0778
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0751
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0731
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0719
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0705
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0687
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0673
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5177 - loss: 1.0671 - val_accuracy: 0.5056 - val_loss: 1.0708
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 0.9377
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0557 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0512
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0467
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0447
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0438
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0432
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0433
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5393 - loss: 1.0436
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5382 - loss: 1.0441 - val_accuracy: 0.5122 - val_loss: 1.0653
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1503
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.0873 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0761
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0692
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0664
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0641
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0616
[1m269/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0594
[1m309/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0579
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5249 - loss: 1.0570 - val_accuracy: 0.5141 - val_loss: 1.0630
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.1426
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0337 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0308
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0286
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0290
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0273
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5416 - loss: 1.0257
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0244
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0237
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0237
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5426 - loss: 1.0237 - val_accuracy: 0.5122 - val_loss: 1.0593
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.8526
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0200 
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5427 - loss: 1.0194
[1m102/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5445 - loss: 1.0166
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0144
[1m175/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0144
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0154
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0165
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0179
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0190
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Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.3040
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0627 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0536
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0425
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0377
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0351
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0339
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0333
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5430 - loss: 1.0328 - val_accuracy: 0.5128 - val_loss: 1.0535
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0807
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 0.9925 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 0.9998
[1m100/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5447 - loss: 1.0019
[1m134/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5458 - loss: 1.0032
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5468 - loss: 1.0032
[1m209/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0033
[1m243/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0044
[1m275/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0056
[1m313/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0071
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5476 - loss: 1.0078 - val_accuracy: 0.5164 - val_loss: 1.0482
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2036
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5504 - loss: 1.0207 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0259
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[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5461 - loss: 1.0247
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Epoch 27/29

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Epoch 28/29

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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0090
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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 470ms/step2025-11-07 17:37:31.229725: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


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Saved model to disk.
F1-score capturado en la ejecución 17: 50.94 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

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[1m204/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 746us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 23ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 24ms/step

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[1m64/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 807us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
Global accuracy score (validation) = 52.3 [%]
Global F1 score (validation) = 52.43 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4071035  0.30705273 0.1121887  0.17365511]
 [0.2919291  0.44793472 0.04563627 0.2144999 ]
 [0.25543222 0.34992757 0.05370674 0.3409335 ]
 ...
 [0.00475391 0.0075396  0.9839517  0.00375482]
 [0.00470078 0.00730666 0.98423904 0.00375366]
 [0.01988079 0.03835264 0.92159706 0.0201695 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.86 [%]
Global accuracy score (test) = 50.36 [%]
Global F1 score (train) = 58.77 [%]
Global F1 score (test) = 49.65 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.39      0.39       400
MODERATE-INTENSITY       0.42      0.51      0.46       400
         SEDENTARY       0.62      0.75      0.68       400
VIGOROUS-INTENSITY       0.67      0.35      0.46       345

          accuracy                           0.50      1545
         macro avg       0.53      0.50      0.50      1545
      weighted avg       0.52      0.50      0.50      1545


Accuracy capturado en la ejecución 18: 50.36 [%]
F1-score capturado en la ejecución 18: 49.65 [%]
2025-11-07 17:37:43.941804: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:37:43.953306: 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 00:00:1762533463.967414 3482774 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533463.971934 3482774 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533463.982610 3482774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533463.982634 3482774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533463.982637 3482774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533463.982639 3482774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:37:43.986025: 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 00:00:1762533466.209596 3482774 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533467.820817 3482906 service.cc:152] XLA service 0x7797e400c9f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533467.820846 3482906 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:37:47.851150: 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 00:00:1762533468.028602 3482906 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533469.648597 3482906 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:26[0m 3s/step - accuracy: 0.0625 - loss: 1.9742
[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2137 - loss: 1.9063  
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2336 - loss: 1.8650
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2423 - loss: 1.8381
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2462 - loss: 1.8202
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2502 - loss: 1.8055
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2553 - loss: 1.7892
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2597 - loss: 1.7747
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2636 - loss: 1.7614
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2669 - loss: 1.7508
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2025-11-07 17:37:53.661482: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2673 - loss: 1.7493 - val_accuracy: 0.3275 - val_loss: 1.3069
Epoch 2/29

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[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3715 - loss: 1.4814 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3554 - loss: 1.4933
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.4971
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3520 - loss: 1.4961
[1m176/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.4913
[1m212/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3555 - loss: 1.4868
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[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3600 - loss: 1.4723
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Epoch 3/29

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Epoch 4/29

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[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4256 - loss: 1.2872
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4235 - loss: 1.2862
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4230 - loss: 1.2862
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Epoch 5/29

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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4283 - loss: 1.2674
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[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4256 - loss: 1.2694
[1m243/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4252 - loss: 1.2694
[1m279/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4249 - loss: 1.2686
[1m314/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4246 - loss: 1.2678
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4246 - loss: 1.2671 - val_accuracy: 0.4534 - val_loss: 1.1643
Epoch 6/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2500 - loss: 1.4332
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4023 - loss: 1.2343 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4187 - loss: 1.2367
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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.2410
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4240 - loss: 1.2406
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4257 - loss: 1.2390
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4271 - loss: 1.2381
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4281 - loss: 1.2376
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4290 - loss: 1.2370 - val_accuracy: 0.4648 - val_loss: 1.1480
Epoch 7/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1504 
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[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1853
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1874
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1890
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Epoch 8/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1356 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4622 - loss: 1.1547
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1745
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[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1792
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1793
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4602 - loss: 1.1791 - val_accuracy: 0.4691 - val_loss: 1.1302
Epoch 9/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3750 - loss: 1.3254
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1670 
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1695
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1693
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1695
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1699
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1701
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1701
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4686 - loss: 1.1701 - val_accuracy: 0.4691 - val_loss: 1.1266
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1202
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4606 - loss: 1.1454 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1481
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1490
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1488
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1482
[1m212/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1473
[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1463
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1454
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4764 - loss: 1.1447
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4765 - loss: 1.1444 - val_accuracy: 0.4744 - val_loss: 1.1164
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3024
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.2048 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4491 - loss: 1.1854
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4542 - loss: 1.1729
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1643
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4621 - loss: 1.1586
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1554
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1530
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1511
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1493
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Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4062 - loss: 1.1171
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4706 - loss: 1.1258 
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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1240
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Epoch 13/29

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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1027
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Epoch 14/29

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[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.1068
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1063
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.1060
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.1054
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5074 - loss: 1.1054 - val_accuracy: 0.4839 - val_loss: 1.0940
Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0031
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5159 - loss: 1.0612 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0636
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0722
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0750
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0772
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0795
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.0810
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.0820
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Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1945
[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5044 - loss: 1.1185 
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5073 - loss: 1.0977
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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.0815
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5071 - loss: 1.0817
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Epoch 17/29

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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0565
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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0635
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.0649
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Epoch 18/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.1015 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0875
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0791
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0767
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0736
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0720
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0712
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5198 - loss: 1.0706 - val_accuracy: 0.4901 - val_loss: 1.0840
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6875 - loss: 1.0039
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5242 - loss: 1.0866 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0931
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0943
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0902
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.0860
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0834
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0811
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.0792
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5143 - loss: 1.0790 - val_accuracy: 0.4931 - val_loss: 1.0801
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0693
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1078 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.1007
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0930
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0880
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0832
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.0790
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0749
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0717
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5177 - loss: 1.0695 - val_accuracy: 0.4878 - val_loss: 1.0782
Epoch 21/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0446 
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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0425
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[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0409
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0405
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Epoch 22/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5309 - loss: 1.0187 
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0296
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[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0356
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0364
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Epoch 23/29

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[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0344
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0351
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0356
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0354
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5281 - loss: 1.0354 - val_accuracy: 0.4918 - val_loss: 1.0650
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9536
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5617 - loss: 1.0332 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 1.0410
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0436
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0446
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0437
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0434
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0429
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0416
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0402
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Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6562 - loss: 0.7874
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5957 - loss: 0.9711 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5812 - loss: 0.9853
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5686 - loss: 0.9960
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5641 - loss: 1.0006
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5603 - loss: 1.0042
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Epoch 26/29

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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0089
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Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.6250 - loss: 0.8049
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Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9651
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0210
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0171
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0145
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5472 - loss: 1.0125
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0107
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0098
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Epoch 29/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9494
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 0.9954 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 0.9990
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0000
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5471 - loss: 0.9999
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0008
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[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5471 - loss: 1.0026
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 441ms/step2025-11-07 17:38:13.334197: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 26ms/step
Saved model to disk.
2025-11-07 17:38:25.913273: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:38:25.925081: 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 00:00:1762533505.938821 3486588 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533505.943206 3486588 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533505.953476 3486588 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533505.953505 3486588 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533505.953507 3486588 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533505.953509 3486588 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:38:25.956640: 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 00:00:1762533508.226114 3486588 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533509.831633 3486721 service.cc:152] XLA service 0x79accc121a50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533509.831687 3486721 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:38:29.865822: 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 00:00:1762533510.051259 3486721 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533511.700981 3486721 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:47[0m 3s/step - accuracy: 0.1875 - loss: 1.9577
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[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2684 - loss: 1.6899
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2025-11-07 17:38:35.686114: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads

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Epoch 2/29

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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3960 - loss: 1.3726
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3937 - loss: 1.3729
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3916 - loss: 1.3732
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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3896 - loss: 1.3716
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3895 - loss: 1.3700
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Epoch 3/29

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[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4131 - loss: 1.2917
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Epoch 4/29

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Epoch 5/29

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Epoch 6/29

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[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1769
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.1773
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.1774
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Epoch 7/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1246 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1386
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1478
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1500
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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1521
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1523
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Epoch 8/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4688 - loss: 1.3873
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1554 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1406
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[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1365
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1371
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1376
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Epoch 9/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4062 - loss: 1.0442
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4661 - loss: 1.1710 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4656 - loss: 1.1736
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1618
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4727 - loss: 1.1584
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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1484
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1455
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Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1524
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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1388
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1294
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1236
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1197
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1174
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1154
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1140
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Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.4688 - loss: 1.0966
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0659 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.0820
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.0872
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0875
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.0873
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.0879
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0886
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.0894
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5056 - loss: 1.0894 - val_accuracy: 0.5141 - val_loss: 1.0715
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0240
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1156 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1130
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1106
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1068
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1058
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1047
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1036
[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1024
[1m316/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1009
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Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9495
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.0919 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5081 - loss: 1.0882
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Epoch 14/29

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Epoch 15/29

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[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0728
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Epoch 16/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0342 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0387
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0466
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0477
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[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0512
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0521
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5251 - loss: 1.0527 - val_accuracy: 0.5095 - val_loss: 1.0442
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3125 - loss: 1.1871
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.0943 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.0892
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0744
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0696
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[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0617
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0594
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0581
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Epoch 18/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0276 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0327
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0370
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0387
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5316 - loss: 1.0396
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0402
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0406
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0411
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0413
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5305 - loss: 1.0413 - val_accuracy: 0.5177 - val_loss: 1.0381
Epoch 19/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5448 - loss: 1.0335 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0383
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[1m155/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0393
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[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0375
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0358
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5371 - loss: 1.0345
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5373 - loss: 1.0335 - val_accuracy: 0.5154 - val_loss: 1.0334
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9660
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0338 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0306
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0254
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0242
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5322 - loss: 1.0234
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0233
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0234
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0235
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5333 - loss: 1.0235 - val_accuracy: 0.5145 - val_loss: 1.0332
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2222
[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4763 - loss: 1.0942 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0643
[1m103/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0525
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0454
[1m175/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0403
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0371
[1m246/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0351
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0340
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0331
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5272 - loss: 1.0327 - val_accuracy: 0.5174 - val_loss: 1.0321
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.6562 - loss: 0.9393
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5540 - loss: 1.0138 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 1.0088
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[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 1.0087
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[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5488 - loss: 1.0064
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 1.0063
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5487 - loss: 1.0066
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Epoch 23/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5554 - loss: 0.9702 
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[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0027
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[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0053
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5413 - loss: 1.0057
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Epoch 24/29

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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0382
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0224
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0189
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0166
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0147
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0134
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0122
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Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6875 - loss: 0.7539
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5569 - loss: 0.9763 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 0.9811
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[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5567 - loss: 0.9879
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5570 - loss: 0.9905
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 0.9921
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5578 - loss: 0.9929
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 0.9930
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 0.9932
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5580 - loss: 0.9932 - val_accuracy: 0.5141 - val_loss: 1.0150
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5312 - loss: 0.8936
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0046 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0031
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5448 - loss: 1.0017
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 0.9983
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 0.9971
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5484 - loss: 0.9971
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5490 - loss: 0.9968
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5494 - loss: 0.9964
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Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5625 - loss: 0.8616
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Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 426ms/step2025-11-07 17:38:55.498983: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


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Saved model to disk.

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

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[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Global accuracy score (validation) = 51.71 [%]
Global F1 score (validation) = 51.1 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.23261122 0.3885475  0.06841194 0.31042925]
 [0.45123154 0.30396596 0.11197931 0.13282321]
 [0.31674877 0.4237594  0.04470337 0.21478847]
 ...
 [0.05134396 0.04883193 0.86734134 0.03248278]
 [0.0026268  0.00414232 0.9912895  0.00194134]
 [0.00266628 0.00407545 0.9912929  0.00196531]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.15 [%]
Global accuracy score (test) = 52.75 [%]
Global F1 score (train) = 59.39 [%]
Global F1 score (test) = 52.53 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.38      0.39       400
MODERATE-INTENSITY       0.42      0.63      0.50       400
         SEDENTARY       0.70      0.71      0.70       400
VIGOROUS-INTENSITY       0.78      0.37      0.50       345

          accuracy                           0.53      1545
         macro avg       0.58      0.52      0.53      1545
      weighted avg       0.57      0.53      0.53      1545


Accuracy capturado en la ejecución 19: 52.75 [%]
F1-score capturado en la ejecución 19: 52.53 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---
2025-11-07 17:39:08.229482: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:39:08.240810: 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 00:00:1762533548.253993 3490424 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533548.258135 3490424 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533548.267850 3490424 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533548.267868 3490424 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533548.267871 3490424 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533548.267872 3490424 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:39:08.271035: 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 00:00:1762533550.495527 3490424 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533552.129144 3490534 service.cc:152] XLA service 0x7d15e011f560 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533552.129174 3490534 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:39:12.159283: 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 00:00:1762533552.342974 3490534 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533553.999970 3490534 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:55[0m 3s/step - accuracy: 0.2812 - loss: 1.5039
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2546 - loss: 1.7609  
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2637 - loss: 1.7458
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2671 - loss: 1.7362
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2703 - loss: 1.7267
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2756 - loss: 1.7130
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2807 - loss: 1.7005
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2848 - loss: 1.6894
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[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2918 - loss: 1.6706
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2025-11-07 17:39:18.107826: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 14ms/step - accuracy: 0.2919 - loss: 1.6702 - val_accuracy: 0.4126 - val_loss: 1.3238
Epoch 2/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.4818 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.4996
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.5011
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.4977
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.4923
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3504 - loss: 1.4874
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[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3571 - loss: 1.4713
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Epoch 3/29

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Epoch 4/29

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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4347 - loss: 1.2923
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Epoch 5/29

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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4478 - loss: 1.2560
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4482 - loss: 1.2554
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.2541
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.2512
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.2498
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Epoch 6/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4444 - loss: 1.2190 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.2269
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4438 - loss: 1.2316
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.2324
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4450 - loss: 1.2320
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4458 - loss: 1.2318
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.2306
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.2297
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Epoch 7/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3438 - loss: 1.1807
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4258 - loss: 1.2170 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.2109
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[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.2105
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[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.2096
[1m268/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4498 - loss: 1.2086
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4506 - loss: 1.2075
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Epoch 8/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4644 - loss: 1.1535 
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1801
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1812
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4617 - loss: 1.1803
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1799
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4637 - loss: 1.1793 - val_accuracy: 0.4675 - val_loss: 1.1567
Epoch 9/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0766
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[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1626
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1622
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1595
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[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1563
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1551
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1544
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Epoch 10/29

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[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1196
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4811 - loss: 1.1333
[1m176/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1337
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1340
[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4812 - loss: 1.1351
[1m279/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1356
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4816 - loss: 1.1356
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Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 0.9931
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.1188 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1311
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1305
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.1301
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1295
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1294
[1m267/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1295
[1m305/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1296
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4887 - loss: 1.1296 - val_accuracy: 0.4767 - val_loss: 1.1359
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0303
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1131 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1136
[1m102/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1142
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1132
[1m175/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1129
[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1136
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1143
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1149
[1m315/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1152
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Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.4095
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4982 - loss: 1.1459 
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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.1076
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.1068
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Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1745
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4960 - loss: 1.1408 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1367
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1214
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1150
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4987 - loss: 1.1099
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5002 - loss: 1.1066
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1040
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.1021
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1409
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5217 - loss: 1.0861 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5120 - loss: 1.0861
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0881
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0880
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0887
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0890
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0892
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0892
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5089 - loss: 1.0891 - val_accuracy: 0.4878 - val_loss: 1.1091
Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9886
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1117 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1079
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1045
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1019
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1002
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0980
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5066 - loss: 1.0956
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0928
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0910
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5099 - loss: 1.0908 - val_accuracy: 0.4898 - val_loss: 1.1054
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1982
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5262 - loss: 1.0927 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0778
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0754
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0746
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0733
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0718
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0711
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0710
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5279 - loss: 1.0709 - val_accuracy: 0.4895 - val_loss: 1.1038
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9783
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0189 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0340
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0476
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[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0551
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0565
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[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0574
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0577
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5279 - loss: 1.0578 - val_accuracy: 0.4888 - val_loss: 1.0974
Epoch 19/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 0.9960 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0137
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[1m155/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0275
[1m195/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0305
[1m233/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0327
[1m271/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0341
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0353
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5341 - loss: 1.0362 - val_accuracy: 0.4954 - val_loss: 1.0941
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.9255
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0068 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0145
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0210
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0263
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0300
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0322
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5288 - loss: 1.0333
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0344
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5294 - loss: 1.0359 - val_accuracy: 0.4997 - val_loss: 1.0893
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0238
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0140 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5476 - loss: 1.0261
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 1.0320
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0368
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0384
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0402
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0416
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0426
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0429
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5380 - loss: 1.0430 - val_accuracy: 0.4931 - val_loss: 1.0858
Epoch 22/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0261 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0256
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5373 - loss: 1.0238
[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0249
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0263
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0267
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0275
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Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0123
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0293 
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[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0319
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[1m267/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5330 - loss: 1.0313
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0312
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5328 - loss: 1.0314 - val_accuracy: 0.4947 - val_loss: 1.0778
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0774
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5640 - loss: 1.0124 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5600 - loss: 1.0178
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5559 - loss: 1.0198
[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 1.0208
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 1.0211
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 1.0211
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5539 - loss: 1.0211
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5535 - loss: 1.0213
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5528 - loss: 1.0217
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5528 - loss: 1.0217 - val_accuracy: 0.4974 - val_loss: 1.0759
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1195
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 1.0084 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5541 - loss: 1.0059
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0094
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5490 - loss: 1.0138
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5478 - loss: 1.0154
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0159
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0167
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5460 - loss: 1.0176
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5453 - loss: 1.0185 - val_accuracy: 0.4918 - val_loss: 1.0674
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 27ms/step - accuracy: 0.5625 - loss: 0.9457
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0450 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0207
[1m116/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0172
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0139
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0130
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0126
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0124
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0125
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5442 - loss: 1.0127 - val_accuracy: 0.4961 - val_loss: 1.0669
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6875 - loss: 0.8487
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5988 - loss: 0.9767 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5842 - loss: 0.9840
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5760 - loss: 0.9904
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5709 - loss: 0.9952
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5666 - loss: 0.9987
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5587 - loss: 1.0044
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 1.0051
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5572 - loss: 1.0052 - val_accuracy: 0.4987 - val_loss: 1.0625
Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.6250 - loss: 0.8870
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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5528 - loss: 0.9992
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5531 - loss: 1.0015
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 1.0020
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5550 - loss: 1.0025
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5544 - loss: 1.0035
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 1.0040
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5539 - loss: 1.0042
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5535 - loss: 1.0044
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Epoch 29/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1394
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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0031
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[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 1.0031
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5498 - loss: 1.0026 - val_accuracy: 0.5013 - val_loss: 1.0624

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 436ms/step2025-11-07 17:39:37.793528: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 26ms/step
Saved model to disk.

--- TEST (ejecución 20) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:57[0m 1s/step
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[1m62/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 823us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Global accuracy score (validation) = 53.06 [%]
Global F1 score (validation) = 51.72 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.2845443  0.47428638 0.01449279 0.22667661]
 [0.41672805 0.35436088 0.08387296 0.14503814]
 [0.42158562 0.40454346 0.05586205 0.11800893]
 ...
 [0.12825668 0.1365355  0.6523826  0.08282528]
 [0.02880673 0.05172812 0.90239197 0.01707324]
 [0.07119414 0.09541437 0.784413   0.04897854]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.69 [%]
Global accuracy score (test) = 50.68 [%]
Global F1 score (train) = 58.38 [%]
Global F1 score (test) = 49.36 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.32      0.33       400
MODERATE-INTENSITY       0.43      0.64      0.51       400
         SEDENTARY       0.64      0.74      0.69       400
VIGOROUS-INTENSITY       0.82      0.30      0.44       345

          accuracy                           0.51      1545
         macro avg       0.56      0.50      0.49      1545
      weighted avg       0.55      0.51      0.50      1545


Accuracy capturado en la ejecución 20: 50.68 [%]
F1-score capturado en la ejecución 20: 49.36 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:44[0m 1s/step
[1m 59/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 865us/step
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[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 795us/step
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 792us/step
[1m318/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 793us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m64/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 798us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 50.82 [%]
Global F1 score (validation) = 47.98 [%]
[[1.]
 [1.]
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 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.45068496 0.27140248 0.10628533 0.17162724]
 [0.45969757 0.27398735 0.09547972 0.17083536]
 [0.3156366  0.4200456  0.04781443 0.21650346]
 ...
 [0.14421135 0.11010886 0.6921972  0.05348254]
 [0.04196227 0.03399399 0.9086889  0.01535478]
 [0.11309729 0.08245013 0.7628474  0.04160517]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.16 [%]
Global accuracy score (test) = 48.41 [%]
Global F1 score (train) = 54.8 [%]
Global F1 score (test) = 47.42 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.63      0.46       400
MODERATE-INTENSITY       0.41      0.23      0.30       400
         SEDENTARY       0.62      0.70      0.66       400
VIGOROUS-INTENSITY       0.76      0.36      0.49       345

          accuracy                           0.48      1545
         macro avg       0.54      0.48      0.47      1545
      weighted avg       0.53      0.48      0.47      1545


Accuracy capturado en la ejecución 21: 48.41 [%]
2025-11-07 17:39:50.413300: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:39:50.424625: 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 00:00:1762533590.437726 3494253 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533590.441876 3494253 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533590.451805 3494253 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533590.451829 3494253 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533590.451831 3494253 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533590.451833 3494253 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:39:50.455185: 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 00:00:1762533592.689780 3494253 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533594.277359 3494377 service.cc:152] XLA service 0x7205c810d890 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533594.277390 3494377 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:39:54.307360: 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 00:00:1762533594.482375 3494377 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533596.131996 3494377 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:39[0m 3s/step - accuracy: 0.3750 - loss: 1.7434
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[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3343 - loss: 1.6252
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3384 - loss: 1.5919
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[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.5550
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step - accuracy: 0.3503 - loss: 1.55402025-11-07 17:39:58.910004: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:40:00.206969: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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Epoch 3/29

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[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4496 - loss: 1.2496
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.2518
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.2538
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Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 24ms/step - accuracy: 0.3750 - loss: 1.5105
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4548 - loss: 1.2774 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.2717
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4517 - loss: 1.2685
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.2641
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.2603
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4547 - loss: 1.2572
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.2542
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4561 - loss: 1.2517
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.2493
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4567 - loss: 1.2487 - val_accuracy: 0.4330 - val_loss: 1.2396
Epoch 5/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.4499
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.2918 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.2596
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.2300
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.2222
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.2170
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.2137
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.2113
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Epoch 6/29

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Epoch 7/29

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Epoch 8/29

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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4904 - loss: 1.1474
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1455
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[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1440
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1435
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Epoch 9/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.0937 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1104
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1198
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[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1223
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1233
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4941 - loss: 1.1238
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4940 - loss: 1.1242 - val_accuracy: 0.4747 - val_loss: 1.1559
Epoch 10/29

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[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5422 - loss: 1.1013 
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[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.1088
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.1092
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.1092
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5081 - loss: 1.1092 - val_accuracy: 0.4839 - val_loss: 1.1384
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0708
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.1082 
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[1m246/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1035
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.1020
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.1011
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Epoch 12/29

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[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0731
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5194 - loss: 1.0845
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0845
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0847
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0853
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0855
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0857
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Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1347
[1m 31/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4804 - loss: 1.1061 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4971 - loss: 1.0957
[1m103/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.0912
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5073 - loss: 1.0896
[1m172/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0881
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.0880
[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0877
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0867
[1m316/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0858
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5116 - loss: 1.0853 - val_accuracy: 0.4947 - val_loss: 1.1134
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 0.9547
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5484 - loss: 1.0629 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0772
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0849
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0881
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0893
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0891
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0886
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0880
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5158 - loss: 1.0869 - val_accuracy: 0.4997 - val_loss: 1.1052
Epoch 15/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.0403 
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[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0516
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Epoch 16/29

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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0594
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0592
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Epoch 17/29

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[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0533
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0539
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0542
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0538
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Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 1.2034
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5688 - loss: 1.0577 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5605 - loss: 1.0485
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 1.0451
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5499 - loss: 1.0444
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0441
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0437
[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0430
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 1.0419
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Epoch 19/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0470 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0429
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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0327
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0324
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5373 - loss: 1.0327
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0329
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5368 - loss: 1.0329 - val_accuracy: 0.5059 - val_loss: 1.0860
Epoch 20/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5463 - loss: 0.9739 
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0039
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[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0143
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0161
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5366 - loss: 1.0172 - val_accuracy: 0.5003 - val_loss: 1.0874
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.5625 - loss: 0.8662
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0275
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0278
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0275
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0272
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5373 - loss: 1.0268
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0264
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5375 - loss: 1.0263 - val_accuracy: 0.4997 - val_loss: 1.0874
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1307
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0140 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5471 - loss: 1.0157
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5459 - loss: 1.0152
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0140
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0129
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5463 - loss: 1.0125
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0123
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5464 - loss: 1.0128
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5463 - loss: 1.0130
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5463 - loss: 1.0130 - val_accuracy: 0.4967 - val_loss: 1.0702
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.8803
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 0.9766 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 0.9935
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 0.9984
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 0.9996
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0000
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0009
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0008
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 1.0004
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0003
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5459 - loss: 1.0004 - val_accuracy: 0.4967 - val_loss: 1.0695
Epoch 24/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5540 - loss: 0.9963 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5427 - loss: 1.0075
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0095
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0096
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0091
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0086
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0079
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0073
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0069
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5416 - loss: 1.0069 - val_accuracy: 0.4987 - val_loss: 1.0650
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 0.9861
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5706 - loss: 0.9807 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5658 - loss: 0.9871
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5614 - loss: 0.9890
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5597 - loss: 0.9905
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 0.9918
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5577 - loss: 0.9928
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5573 - loss: 0.9935
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5570 - loss: 0.9940 - val_accuracy: 0.5049 - val_loss: 1.0615
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9994
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5269 - loss: 0.9950 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 0.9963
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 0.9978
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5362 - loss: 0.9999
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0012
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0016
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0022
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0027
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0029
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5392 - loss: 1.0029 - val_accuracy: 0.5043 - val_loss: 1.0617
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9813
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 1.0244 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0197
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5503 - loss: 1.0111
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5523 - loss: 1.0057
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 1.0021
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5555 - loss: 0.9994
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 0.9980
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5568 - loss: 0.9968
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5570 - loss: 0.9958
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5571 - loss: 0.9957 - val_accuracy: 0.5049 - val_loss: 1.0600
Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8950
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5825 - loss: 0.9628 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5737 - loss: 0.9670
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5675 - loss: 0.9712
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Epoch 29/29

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Saved model to disk.
F1-score capturado en la ejecución 21: 47.42 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:39[0m 1s/step
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[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 752us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
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Global accuracy score (validation) = 51.08 [%]
Global F1 score (validation) = 49.2 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3994751  0.4163523  0.0540648  0.13010788]
 [0.3881591  0.3376214  0.11131048 0.16290903]
 [0.4495684  0.30495992 0.12567136 0.11980035]
 ...
 [0.07021391 0.05700585 0.8483278  0.02445241]
 [0.01024019 0.00541074 0.9831509  0.00119821]
 [0.07084771 0.05761562 0.847082   0.0244547 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.87 [%]
Global accuracy score (test) = 49.97 [%]
Global F1 score (train) = 57.52 [%]
Global F1 score (test) = 49.09 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.46      0.40       400
MODERATE-INTENSITY       0.41      0.50      0.45       400
         SEDENTARY       0.69      0.75      0.72       400
VIGOROUS-INTENSITY       0.87      0.26      0.39       345

          accuracy                           0.50      1545
         macro avg       0.58      0.49      0.49      1545
      weighted avg       0.57      0.50      0.49      1545


Accuracy capturado en la ejecución 22: 49.97 [%]
2025-11-07 17:40:32.708878: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:40:32.720841: 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 00:00:1762533632.734176 3498071 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533632.738475 3498071 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533632.748479 3498071 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533632.748504 3498071 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533632.748507 3498071 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533632.748508 3498071 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:40:32.751814: 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 00:00:1762533634.990767 3498071 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
This activity can't be balanced (in a downsampling way)
Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533636.579033 3498204 service.cc:152] XLA service 0x744a4410d710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533636.579087 3498204 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:40:36.614040: 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 00:00:1762533636.793852 3498204 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533638.467714 3498204 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:49[0m 3s/step - accuracy: 0.2812 - loss: 1.7807
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[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3075 - loss: 1.6639
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3106 - loss: 1.6550
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3128 - loss: 1.6468
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3155 - loss: 1.6373
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3179 - loss: 1.6285
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.6216
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.6138
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.3217 - loss: 1.61212025-11-07 17:40:41.240886: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:40:42.460734: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3898 - loss: 1.3824
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3902 - loss: 1.3790
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3911 - loss: 1.3800
[1m174/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3919 - loss: 1.3796
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.3788
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3932 - loss: 1.3779
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3934 - loss: 1.3766
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3938 - loss: 1.3751
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.3939 - loss: 1.3747 - val_accuracy: 0.4382 - val_loss: 1.1911
Epoch 3/29

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[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3986 - loss: 1.3162
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4013 - loss: 1.3148
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4042 - loss: 1.3116
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4067 - loss: 1.3087
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4083 - loss: 1.3071
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4096 - loss: 1.3059
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4104 - loss: 1.3050
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4106 - loss: 1.3047 - val_accuracy: 0.4560 - val_loss: 1.1731
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1660
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4271 - loss: 1.2474 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4301 - loss: 1.2494
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.2515
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.2525
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.2532
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4307 - loss: 1.2539
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4304 - loss: 1.2548
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4307 - loss: 1.2546
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4312 - loss: 1.2541
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Epoch 5/29

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Epoch 6/29

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Epoch 7/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1846 
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[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4754 - loss: 1.1765
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Epoch 8/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1749 
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Epoch 9/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1699 
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[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1502
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1483
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1473
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1467
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4815 - loss: 1.1466 - val_accuracy: 0.4951 - val_loss: 1.1064
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4688 - loss: 1.2258
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[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1500
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1444
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1435
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Epoch 11/29

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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.0979
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.1042
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1061
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1073
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1081
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1087
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Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2190
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1082 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1003
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.0938
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.0930
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.0933
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.0949
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.0961
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.0971
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4988 - loss: 1.0973 - val_accuracy: 0.4951 - val_loss: 1.0840
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1123
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0984 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.1033
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.1047
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.1054
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.1050
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1044
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.1037
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1031
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.1025
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Epoch 14/29

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[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5243 - loss: 1.1002 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.1002
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1020
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.0971
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.0962
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0949
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1662 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1318
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1178
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1098
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1034
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.0991
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.0960
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.0940
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5033 - loss: 1.0926 - val_accuracy: 0.5076 - val_loss: 1.0695
Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6250 - loss: 0.8921
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5106 - loss: 1.0875 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0866
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0821
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0810
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0797
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0787
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0780
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0775
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5172 - loss: 1.0775 - val_accuracy: 0.5026 - val_loss: 1.0657
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4375 - loss: 1.0647
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.0853 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.0685
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.0613
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0583
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.0574
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0570
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0568
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0571
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0576
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Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9653
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5622 - loss: 1.0340 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 1.0339
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0388
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[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 1.0424
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5393 - loss: 1.0448
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0466
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5360 - loss: 1.0481 - val_accuracy: 0.5095 - val_loss: 1.0596
Epoch 19/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5156 - loss: 1.0265 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0430
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0498
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0496
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[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0502
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0506
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Epoch 20/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5654 - loss: 1.0221 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5679 - loss: 1.0125
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5603 - loss: 1.0151
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5561 - loss: 1.0187
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5528 - loss: 1.0218
[1m267/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5500 - loss: 1.0243
[1m305/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0266
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5460 - loss: 1.0280 - val_accuracy: 0.5210 - val_loss: 1.0525
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1609
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5481 - loss: 1.0037 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0212
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0304
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0350
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0380
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0399
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0407
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0413
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0417
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5271 - loss: 1.0417 - val_accuracy: 0.5197 - val_loss: 1.0522
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1899
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0428 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0319
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0296
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0287
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0277
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0269
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0263
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0261
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5384 - loss: 1.0261 - val_accuracy: 0.5122 - val_loss: 1.0517
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0386
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0341 
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[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0400
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[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0390
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[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0361
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Epoch 24/29

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Epoch 25/29

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[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0101
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 1.0100
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0100
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5458 - loss: 1.0099 - val_accuracy: 0.5112 - val_loss: 1.0432
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9245
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5605 - loss: 0.9679 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 0.9873
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0005
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0040
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0061
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0078
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0087
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0093
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Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1370
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0647 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0445
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Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1701
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0065
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0072
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Epoch 29/29

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[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5662 - loss: 0.9863
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 426ms/step2025-11-07 17:41:02.096526: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 26ms/step
Saved model to disk.
F1-score capturado en la ejecución 22: 49.09 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:31[0m 1s/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[1m66/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 773us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 51.22 [%]
Global F1 score (validation) = 50.86 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.33918887 0.28652233 0.14789866 0.22639014]
 [0.29321736 0.4101739  0.03142855 0.26518017]
 [0.33918887 0.28652233 0.14789866 0.22639014]
 ...
 [0.03322864 0.03226277 0.9175334  0.01697517]
 [0.03484268 0.03181742 0.91626644 0.01707348]
 [0.11814938 0.11384514 0.68122137 0.08678412]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.83 [%]
Global accuracy score (test) = 51.13 [%]
Global F1 score (train) = 59.04 [%]
Global F1 score (test) = 50.45 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.39      0.38       400
MODERATE-INTENSITY       0.43      0.55      0.48       400
         SEDENTARY       0.66      0.75      0.70       400
VIGOROUS-INTENSITY       0.77      0.32      0.45       345

          accuracy                           0.51      1545
         macro avg       0.56      0.50      0.50      1545
      weighted avg       0.55      0.51      0.51      1545


Accuracy capturado en la ejecución 23: 51.13 [%]
2025-11-07 17:41:14.929442: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:41:14.940737: 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 00:00:1762533674.954183 3501914 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533674.958151 3501914 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533674.968150 3501914 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533674.968169 3501914 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533674.968171 3501914 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533674.968173 3501914 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:41:14.971234: 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 00:00:1762533677.239294 3501914 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533678.857677 3502024 service.cc:152] XLA service 0x72581c10ffa0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533678.857732 3502024 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:41:18.899610: 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 00:00:1762533679.077067 3502024 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533680.734984 3502024 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:55[0m 3s/step - accuracy: 0.2812 - loss: 1.7708
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[1m246/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 1.8249
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2025-11-07 17:41:24.824570: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.5412
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3581 - loss: 1.5332
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.5272
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.5214
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.5154
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.5108
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.5067
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Epoch 3/29

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[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3751 - loss: 1.4143
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3763 - loss: 1.4105
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3801 - loss: 1.4022
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3816 - loss: 1.3989
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Epoch 4/29

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Epoch 5/29

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Epoch 6/29

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[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4306 - loss: 1.2592
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Epoch 7/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4256 - loss: 1.2877 
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[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4470 - loss: 1.2401
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Epoch 8/29

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[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4825 - loss: 1.1715 
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[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1881
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1885
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4684 - loss: 1.1886 - val_accuracy: 0.4711 - val_loss: 1.1373
Epoch 9/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.2027 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4573 - loss: 1.1942
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1872
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[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1774
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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1745
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4694 - loss: 1.1733
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Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0305
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1513 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1503
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1551
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1571
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1596
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4834 - loss: 1.1611
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1617
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1613
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Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9590
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5095 - loss: 1.1024 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1222
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1238
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1263
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4900 - loss: 1.1276
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1284
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1294
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4873 - loss: 1.1304
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1309
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4870 - loss: 1.1310 - val_accuracy: 0.4819 - val_loss: 1.1133
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9755
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1174 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1250
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1247
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.1235
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.1221
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1215
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1218
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1223
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4945 - loss: 1.1225 - val_accuracy: 0.4895 - val_loss: 1.1088
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4375 - loss: 1.3265
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[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1195
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Epoch 14/29

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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.0963
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Epoch 15/29

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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0684
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0718
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[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0754
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0765
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Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0008
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0853 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0834
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[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0807
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0808
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0815
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0819
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5101 - loss: 1.0820 - val_accuracy: 0.5007 - val_loss: 1.0786
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2771
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1140 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.0898
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.0746
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[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0701
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0692
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0688
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0686
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5152 - loss: 1.0686 - val_accuracy: 0.5039 - val_loss: 1.0740
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1217
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0749 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0788
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0794
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0762
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0746
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0725
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0716
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0709
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0708
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Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.7188 - loss: 0.8893
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0436 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.0505
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0510
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0520
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0529
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0533
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.0536
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0537
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0536
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5247 - loss: 1.0536 - val_accuracy: 0.5066 - val_loss: 1.0702
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9650
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0356 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0287
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0274
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0281
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5447 - loss: 1.0295
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0309
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0320
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0325
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0332
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5419 - loss: 1.0333 - val_accuracy: 0.5036 - val_loss: 1.0741
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0492
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.0525 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0466
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0467
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0453
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0443
[1m230/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0438
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0440
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0436
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5229 - loss: 1.0432 - val_accuracy: 0.5115 - val_loss: 1.0677
Epoch 22/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0272 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0260
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0300
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[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0338
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0346
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0348
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0347
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5343 - loss: 1.0346 - val_accuracy: 0.5141 - val_loss: 1.0597
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.0766
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0373 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0296
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0312
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0302
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[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0290
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0288
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Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2960
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0451 
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0353
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0334
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0335
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0331
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0326
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5314 - loss: 1.0319 - val_accuracy: 0.5108 - val_loss: 1.0635
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0780
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0370 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0283
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0238
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0231
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0218
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0212
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0209
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0208
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0209
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5368 - loss: 1.0209 - val_accuracy: 0.5128 - val_loss: 1.0584
Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.8533
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 0.9814 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 0.9912
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 0.9900
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 0.9897
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 0.9911
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Epoch 27/29

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Epoch 28/29

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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 474ms/step2025-11-07 17:41:44.665144: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


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Saved model to disk.
F1-score capturado en la ejecución 23: 50.45 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

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[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 772us/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 25ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m59/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 865us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Global accuracy score (validation) = 51.08 [%]
Global F1 score (validation) = 49.49 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44364393 0.290597   0.10091549 0.16484362]
 [0.40832463 0.32214457 0.09329494 0.17623587]
 [0.18201667 0.37913334 0.01804996 0.42080003]
 ...
 [0.05828372 0.06943839 0.8306403  0.04163766]
 [0.03632359 0.03972375 0.9036148  0.02033785]
 [0.0246649  0.02886789 0.9345284  0.01193879]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.11 [%]
Global accuracy score (test) = 51.07 [%]
Global F1 score (train) = 56.58 [%]
Global F1 score (test) = 50.19 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.54      0.45       400
MODERATE-INTENSITY       0.46      0.30      0.36       400
         SEDENTARY       0.58      0.78      0.66       400
VIGOROUS-INTENSITY       0.72      0.42      0.53       345

          accuracy                           0.51      1545
         macro avg       0.54      0.51      0.50      1545
      weighted avg       0.53      0.51      0.50      1545


Accuracy capturado en la ejecución 24: 51.07 [%]
2025-11-07 17:41:57.208926: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:41:57.220125: 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 00:00:1762533717.233244 3505732 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533717.237559 3505732 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533717.247460 3505732 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533717.247479 3505732 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533717.247482 3505732 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533717.247484 3505732 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:41:57.250723: 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 00:00:1762533719.529928 3505732 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533721.152028 3505863 service.cc:152] XLA service 0x7cf7a001f870 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533721.152057 3505863 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:42:01.182657: 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 00:00:1762533721.364275 3505863 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533723.021773 3505863 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:46[0m 3s/step - accuracy: 0.2812 - loss: 1.7211
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2497 - loss: 1.6460  
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2619 - loss: 1.6202
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2668 - loss: 1.6049
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 1.5923
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2755 - loss: 1.5808
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2795 - loss: 1.5712
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2833 - loss: 1.5627
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2866 - loss: 1.5555
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2895 - loss: 1.54922025-11-07 17:42:05.718790: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:42:07.015168: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 14ms/step - accuracy: 0.2896 - loss: 1.5490 - val_accuracy: 0.3985 - val_loss: 1.3122
Epoch 2/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4193 - loss: 1.3436 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3966 - loss: 1.3562
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3881 - loss: 1.3648
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3849 - loss: 1.3668
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3835 - loss: 1.3666
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3831 - loss: 1.3655
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3830 - loss: 1.3649
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Epoch 3/29

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Epoch 4/29

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Epoch 5/29

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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.2223
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Epoch 6/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4248 - loss: 1.2271 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4335 - loss: 1.2187
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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4386 - loss: 1.2108
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[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4417 - loss: 1.2051
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.2033
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Epoch 7/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1789 
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[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1741
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4698 - loss: 1.1740 - val_accuracy: 0.4524 - val_loss: 1.1587
Epoch 8/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1712 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1701
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1779
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4549 - loss: 1.1775
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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1751
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1734
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Epoch 9/29

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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1388
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1477
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1523
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1545
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1542
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1532
[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1522
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1515
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Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1700
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1178 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1224
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.1230
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1253
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4811 - loss: 1.1276
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1285
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1291
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1297
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4834 - loss: 1.1300 - val_accuracy: 0.4632 - val_loss: 1.1402
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0772
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1557 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1535
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1486
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1431
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1394
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1358
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1327
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1301
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1282
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4944 - loss: 1.1280 - val_accuracy: 0.4691 - val_loss: 1.1315
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2398
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1050 
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[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1122
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Epoch 13/29

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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1083
[1m318/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4936 - loss: 1.1081
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Epoch 14/29

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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.0962
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.0953
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.0953
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0268
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0412 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5086 - loss: 1.0625
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0724
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0779
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.0807
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.0821
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.0830
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.0836
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.0838
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5033 - loss: 1.0839 - val_accuracy: 0.4777 - val_loss: 1.1094
Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0816
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5236 - loss: 1.0594 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0712
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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.0809
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0806
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Epoch 17/29

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[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5330 - loss: 1.0641
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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0730
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0723
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Epoch 18/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.0689 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0693
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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0708
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[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0692
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0679
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0666
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5170 - loss: 1.0654 - val_accuracy: 0.4957 - val_loss: 1.0951
Epoch 19/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0753 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0682
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0607
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0600
[1m230/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0597
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.0591
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0582
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5238 - loss: 1.0574 - val_accuracy: 0.4977 - val_loss: 1.0949
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0524
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0075 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0189
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0261
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0284
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0306
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0323
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0338
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0351
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Epoch 21/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5528 - loss: 0.9771 
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0082
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0132
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0173
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[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0225
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Epoch 22/29

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[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0502
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0378
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0378
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0383
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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0369
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Epoch 23/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5657 - loss: 1.0254 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 1.0238
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0196
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0209
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0226
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0241
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0250
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5380 - loss: 1.0254 - val_accuracy: 0.5013 - val_loss: 1.0847
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1505
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0623 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0535
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0363
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0324
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0303
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0290
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0280
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5353 - loss: 1.0274 - val_accuracy: 0.5059 - val_loss: 1.0782
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.6562 - loss: 0.8552
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5761 - loss: 0.9660 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5701 - loss: 0.9734
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5627 - loss: 0.9878
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5600 - loss: 0.9918
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5581 - loss: 0.9947
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 0.9965
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 0.9982
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5540 - loss: 0.9995
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Epoch 26/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9764
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Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0869
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Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9763
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 1.0174
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5467 - loss: 1.0137 - val_accuracy: 0.5115 - val_loss: 1.0697
Epoch 29/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5943 - loss: 0.9624 
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 440ms/step2025-11-07 17:42:26.582085: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


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Saved model to disk.
F1-score capturado en la ejecución 24: 50.19 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:16[0m 1s/step
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[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 799us/step
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 780us/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 25ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m62/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 821us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 50.92 [%]
Global F1 score (validation) = 49.49 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.41870895 0.34113497 0.09959413 0.14056197]
 [0.42174026 0.30653992 0.13007224 0.14164752]
 [0.24780661 0.34865376 0.08771487 0.31582478]
 ...
 [0.01525628 0.00815624 0.9732223  0.00336519]
 [0.11779434 0.10459045 0.7386579  0.03895735]
 [0.11511518 0.11217628 0.73156863 0.04113991]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.98 [%]
Global accuracy score (test) = 50.42 [%]
Global F1 score (train) = 57.68 [%]
Global F1 score (test) = 48.78 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.34      0.35       400
MODERATE-INTENSITY       0.43      0.64      0.52       400
         SEDENTARY       0.63      0.74      0.68       400
VIGOROUS-INTENSITY       0.82      0.27      0.40       345

          accuracy                           0.50      1545
         macro avg       0.56      0.50      0.49      1545
      weighted avg       0.55      0.50      0.49      1545


Accuracy capturado en la ejecución 25: 50.42 [%]
2025-11-07 17:42:39.241705: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:42:39.252930: 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 00:00:1762533759.265900 3509546 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533759.270181 3509546 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533759.280144 3509546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533759.280162 3509546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533759.280164 3509546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533759.280166 3509546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:42:39.283391: 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 00:00:1762533761.514606 3509546 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533763.107732 3509677 service.cc:152] XLA service 0x7cd478010120 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533763.107774 3509677 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:42:43.148229: 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 00:00:1762533763.323394 3509677 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533765.009417 3509677 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:54[0m 3s/step - accuracy: 0.2500 - loss: 1.9226
[1m 28/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2408 - loss: 1.8266  
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2577 - loss: 1.7898
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2656 - loss: 1.7706
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2700 - loss: 1.7561
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2735 - loss: 1.7455
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 1.7354
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2797 - loss: 1.7260
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2824 - loss: 1.7171
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2025-11-07 17:42:49.061223: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3328 - loss: 1.5289 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3397 - loss: 1.5169
[1m103/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3446 - loss: 1.5070
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3478 - loss: 1.4997
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3508 - loss: 1.4925
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3532 - loss: 1.4863
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3553 - loss: 1.4806
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Epoch 3/29

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Epoch 4/29

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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4169 - loss: 1.2931
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[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4186 - loss: 1.2913
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Epoch 5/29

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[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4702 - loss: 1.2217
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[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.2289
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4578 - loss: 1.2294
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4574 - loss: 1.2294
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4572 - loss: 1.2293 - val_accuracy: 0.4599 - val_loss: 1.1787
Epoch 6/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2812 - loss: 1.3270
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4299 - loss: 1.2590 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4342 - loss: 1.2549
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4370 - loss: 1.2488
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[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4397 - loss: 1.2401
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4409 - loss: 1.2359
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Epoch 7/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1650 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1784
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[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1845
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1867
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1881
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4726 - loss: 1.1885
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1886
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Epoch 8/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4546 - loss: 1.1724 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1768
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4687 - loss: 1.1711
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1691
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4726 - loss: 1.1668
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1663
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4735 - loss: 1.1660 - val_accuracy: 0.4793 - val_loss: 1.1442
Epoch 9/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3425
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.1702 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1592
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1570
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1543
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4805 - loss: 1.1517
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1505
[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1495
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1485
[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4834 - loss: 1.1476
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4836 - loss: 1.1474 - val_accuracy: 0.4836 - val_loss: 1.1313
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8962
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1010 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1182
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1218
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.1228
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1236
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1241
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1248
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.1259
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4958 - loss: 1.1271 - val_accuracy: 0.4855 - val_loss: 1.1291
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0628
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0763 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.0952
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.1026
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.1055
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1082
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1107
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1119
[1m307/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1127
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5011 - loss: 1.1128 - val_accuracy: 0.4915 - val_loss: 1.1206
Epoch 12/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0978 
[1m 79/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.1005
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[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.1051
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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5058 - loss: 1.1051
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Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0462
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0669 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0747
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0838
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[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0932
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Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0381
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.1225 
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[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.1013
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[1m230/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.0984
[1m269/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.0973
[1m307/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.0964
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5080 - loss: 1.0958 - val_accuracy: 0.4931 - val_loss: 1.1028
Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0563
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1196 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4987 - loss: 1.1138
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.1115
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.1104
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5055 - loss: 1.1071
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.1045
[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5066 - loss: 1.1023
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5073 - loss: 1.1004
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5079 - loss: 1.0986 - val_accuracy: 0.4957 - val_loss: 1.0962
Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.6562 - loss: 0.9227
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0429 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0591
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0648
[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0686
[1m191/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0706
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0716
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0719
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0723
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5171 - loss: 1.0723 - val_accuracy: 0.4984 - val_loss: 1.0903
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4062 - loss: 1.1250
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[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0931
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[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0853
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5196 - loss: 1.0835
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Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2189
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0587
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0565
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0555
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0548
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0546
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0547
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Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9817
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0400 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0443
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0462
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0467
[1m172/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0469
[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0473
[1m246/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0471
[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5344 - loss: 1.0471
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0472
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5338 - loss: 1.0473 - val_accuracy: 0.4990 - val_loss: 1.0687
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1693
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5552 - loss: 1.0466 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0439
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0419
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0412
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5399 - loss: 1.0402
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0401
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0408
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0413
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5362 - loss: 1.0416
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5362 - loss: 1.0417 - val_accuracy: 0.4967 - val_loss: 1.0665
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9417
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0168 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0240
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[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5346 - loss: 1.0270
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[1m266/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0305
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0314
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5337 - loss: 1.0316 - val_accuracy: 0.4993 - val_loss: 1.0698
Epoch 22/29

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[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0320
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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0277
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0283
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Epoch 23/29

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[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0250
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[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0262
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0253
[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0252
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0255
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0254
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5328 - loss: 1.0254 - val_accuracy: 0.4993 - val_loss: 1.0610
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0769
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0901 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0751
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0624
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0559
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0508
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0462
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0428
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0407
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0387
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5390 - loss: 1.0386 - val_accuracy: 0.5026 - val_loss: 1.0617
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5625 - loss: 1.0857
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5593 - loss: 1.0377 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5607 - loss: 1.0218
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5589 - loss: 1.0190
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5576 - loss: 1.0172
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 1.0161
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5557 - loss: 1.0149
[1m248/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5550 - loss: 1.0145
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 1.0144
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5534 - loss: 1.0146
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Epoch 26/29

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Epoch 27/29

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Epoch 28/29

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[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5514 - loss: 1.0089
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Epoch 29/29

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
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Saved model to disk.
F1-score capturado en la ejecución 25: 48.78 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:46[0m 1s/step
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 24ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 25ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m61/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 842us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 51.02 [%]
Global F1 score (validation) = 49.54 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.47125146 0.29862723 0.09719282 0.13292843]
 [0.4216391  0.2862863  0.03750138 0.25457314]
 [0.4488182  0.26984233 0.0752909  0.20604858]
 ...
 [0.00646835 0.00761803 0.98278993 0.00312371]
 [0.06651473 0.07754456 0.8215195  0.03442119]
 [0.00656201 0.0077487  0.9825001  0.00318925]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.7 [%]
Global accuracy score (test) = 50.16 [%]
Global F1 score (train) = 56.53 [%]
Global F1 score (test) = 49.35 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.57      0.46       400
MODERATE-INTENSITY       0.47      0.30      0.37       400
         SEDENTARY       0.59      0.73      0.65       400
VIGOROUS-INTENSITY       0.69      0.39      0.49       345

          accuracy                           0.50      1545
         macro avg       0.53      0.50      0.49      1545
      weighted avg       0.53      0.50      0.49      1545


Accuracy capturado en la ejecución 26: 50.16 [%]
2025-11-07 17:43:21.337185: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:43:21.348534: 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 00:00:1762533801.361946 3513383 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533801.366335 3513383 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533801.376283 3513383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533801.376301 3513383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533801.376303 3513383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533801.376304 3513383 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:43:21.379526: 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 00:00:1762533803.638592 3513383 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13758 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533805.248894 3513489 service.cc:152] XLA service 0x793d18110a20 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533805.248955 3513489 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:43:25.294216: 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 00:00:1762533805.477789 3513489 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533807.121867 3513489 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:50[0m 3s/step - accuracy: 0.3125 - loss: 1.5705
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2250 - loss: 1.7926  
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2334 - loss: 1.7888
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2388 - loss: 1.7810
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2433 - loss: 1.7721
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2475 - loss: 1.7619
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2519 - loss: 1.7498
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2557 - loss: 1.7398
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2593 - loss: 1.7308
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2632 - loss: 1.7216
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2025-11-07 17:43:31.230321: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3660 - loss: 1.4760 
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Epoch 3/29

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[1m271/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4000 - loss: 1.3520
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Epoch 4/29

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[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4207 - loss: 1.2822
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4203 - loss: 1.2817
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4203 - loss: 1.2805
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Epoch 5/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4472 - loss: 1.2378 
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4355 - loss: 1.2408
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[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4331 - loss: 1.2398
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.2372
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4335 - loss: 1.2358
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Epoch 6/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4375 - loss: 1.4104
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4479 - loss: 1.2520 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4461 - loss: 1.2381
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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2293
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4431 - loss: 1.2276
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.2258
[1m253/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.2238
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4448 - loss: 1.2216
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Epoch 7/29

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[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.2020
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1995
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4615 - loss: 1.1945
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Epoch 8/29

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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4655 - loss: 1.1622
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Epoch 9/29

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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1579
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[1m229/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1564
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1555
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1541
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1530 - val_accuracy: 0.4832 - val_loss: 1.1269
Epoch 10/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1028 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1120
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1232
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1247
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1258
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1266
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1270
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1270
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4848 - loss: 1.1269 - val_accuracy: 0.4898 - val_loss: 1.1181
Epoch 11/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1047 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1003
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[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1067
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[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1073
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1074
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1079
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4929 - loss: 1.1087 - val_accuracy: 0.4970 - val_loss: 1.1152
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0463
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1056
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Epoch 13/29

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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1078
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1059
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1044
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1030
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1010
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1000
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Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0045
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.0699 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.0799
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.0864
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.0859
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.0855
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.0850
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.0848
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.0846
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4967 - loss: 1.0846 - val_accuracy: 0.5095 - val_loss: 1.1034
Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0760
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0581 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0639
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0665
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0688
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0700
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0714
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0719
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0723
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0729
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Epoch 16/29

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[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4990 - loss: 1.0996 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.0792
[1m103/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0723
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5131 - loss: 1.0696
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[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0699
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5160 - loss: 1.0703
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0698
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5169 - loss: 1.0697 - val_accuracy: 0.5076 - val_loss: 1.0952
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4688 - loss: 1.0895
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0022 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0124
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0251
[1m153/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0316
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[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0391
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0420
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5242 - loss: 1.0464 - val_accuracy: 0.5112 - val_loss: 1.0984
Epoch 18/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0114 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0150
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0245
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[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0322
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0348
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0370
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5274 - loss: 1.0387 - val_accuracy: 0.5036 - val_loss: 1.0939
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5938 - loss: 0.9919
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0563 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0603
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0637
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5122 - loss: 1.0631
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0608
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0595
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0590
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0588
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.0587
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5139 - loss: 1.0586 - val_accuracy: 0.5076 - val_loss: 1.0954
Epoch 20/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5144 - loss: 1.0817 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0646
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[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0546
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[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0524
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0522
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5259 - loss: 1.0519
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Epoch 21/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0377 
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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5330 - loss: 1.0376
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[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5322 - loss: 1.0392
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0388
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Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0050
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5529 - loss: 1.0349 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0456
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0427
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 1.0413
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5372 - loss: 1.0408
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0407
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0404
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5359 - loss: 1.0399
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Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9592
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0241 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0314
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0331
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0323
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0318
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0308
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0301
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0296
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5374 - loss: 1.0295 - val_accuracy: 0.5072 - val_loss: 1.0794
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9987
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5609 - loss: 1.0352 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5416 - loss: 1.0331
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0309
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0318
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0323
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0318
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0312
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0305
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0301
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Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 0.8627
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[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0173
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Epoch 26/29

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Epoch 27/29

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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 0.9996
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0004
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Epoch 28/29

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[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5722 - loss: 0.9603 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5652 - loss: 0.9753
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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5582 - loss: 0.9901
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5572 - loss: 0.9909
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5566 - loss: 0.9914
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[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5557 - loss: 0.9921
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Epoch 29/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2200
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.0434 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0135
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Saved model to disk.
F1-score capturado en la ejecución 26: 49.35 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:41[0m 1s/step
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[1m64/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 804us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
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Global accuracy score (validation) = 51.94 [%]
Global F1 score (validation) = 51.89 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3788457  0.42909786 0.05200221 0.14005429]
 [0.39115223 0.3198209  0.12849756 0.1605293 ]
 [0.23118557 0.26922417 0.13268314 0.36690718]
 ...
 [0.05419157 0.07555957 0.828304   0.04194493]
 [0.11347272 0.11538786 0.69934916 0.07179032]
 [0.00867945 0.01396653 0.97242963 0.00492429]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.79 [%]
Global accuracy score (test) = 50.1 [%]
Global F1 score (train) = 58.73 [%]
Global F1 score (test) = 50.07 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.37      0.36       400
MODERATE-INTENSITY       0.42      0.50      0.46       400
         SEDENTARY       0.63      0.72      0.67       400
VIGOROUS-INTENSITY       0.69      0.40      0.51       345

          accuracy                           0.50      1545
         macro avg       0.53      0.50      0.50      1545
      weighted avg       0.52      0.50      0.50      1545


Accuracy capturado en la ejecución 27: 50.1 [%]
F1-score capturado en la ejecución 27: 50.07 [%]
2025-11-07 17:44:03.634513: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:44:03.645820: 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 00:00:1762533843.659019 3517201 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533843.663213 3517201 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533843.673038 3517201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533843.673057 3517201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533843.673060 3517201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533843.673061 3517201 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:44:03.676212: 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 00:00:1762533845.917257 3517201 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533847.526413 3517330 service.cc:152] XLA service 0x76fc2c020d10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533847.526445 3517330 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:44:07.556861: 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 00:00:1762533847.731208 3517330 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533849.401406 3517330 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15:50[0m 3s/step - accuracy: 0.2500 - loss: 1.7441
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[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step - accuracy: 0.2930 - loss: 1.67852025-11-07 17:44:12.160402: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 96 bytes spill stores, 96 bytes spill loads

2025-11-07 17:44:13.382481: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads


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Epoch 2/29

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[1m323/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3589 - loss: 1.4566
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Epoch 3/29

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[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3997 - loss: 1.3531
[1m213/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4012 - loss: 1.3507
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4026 - loss: 1.3482
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4035 - loss: 1.3464
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4043 - loss: 1.3449
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4046 - loss: 1.3443 - val_accuracy: 0.4540 - val_loss: 1.1903
Epoch 4/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3750 - loss: 1.3676
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3967 - loss: 1.3299 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4024 - loss: 1.3230
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4073 - loss: 1.3131
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4092 - loss: 1.3081
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.3043
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4116 - loss: 1.3021
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4122 - loss: 1.3006
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4128 - loss: 1.2990
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4128 - loss: 1.2989 - val_accuracy: 0.4625 - val_loss: 1.1733
Epoch 5/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.1732
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.2322 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.2337
[1m106/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4344 - loss: 1.2383
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4333 - loss: 1.2408
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4331 - loss: 1.2411
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4333 - loss: 1.2412
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4334 - loss: 1.2410
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4340 - loss: 1.2401
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4351 - loss: 1.2386
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Epoch 6/29

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[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.2071
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Epoch 7/29

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[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4638 - loss: 1.1865
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Epoch 8/29

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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1411
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1436
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1453
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1468
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1483
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Epoch 9/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0627
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1351 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4816 - loss: 1.1381
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.1420
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4776 - loss: 1.1444
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4765 - loss: 1.1462
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1487
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1500
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1508
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1509
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Epoch 10/29

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[1m 31/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4775 - loss: 1.1315 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4817 - loss: 1.1257
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[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1281
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1289
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1291
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1296
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1301
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1303
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4870 - loss: 1.1303 - val_accuracy: 0.4957 - val_loss: 1.1318
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9651
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4695 - loss: 1.1520 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4728 - loss: 1.1494
[1m104/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1431
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1379
[1m176/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1336
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1300
[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1287
[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1274
[1m319/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1260
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4850 - loss: 1.1256 - val_accuracy: 0.4928 - val_loss: 1.1256
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0532
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.0829 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5063 - loss: 1.0988
[1m107/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1062
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1077
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.1085
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.1094
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1096
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1095
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.1091
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4973 - loss: 1.1091 - val_accuracy: 0.4951 - val_loss: 1.1201
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1016
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4846 - loss: 1.0982 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.0977
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.0941
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.0929
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.0922
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5024 - loss: 1.0915
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.0913
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.0912
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.0912
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5047 - loss: 1.0912 - val_accuracy: 0.4924 - val_loss: 1.1100
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0670
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.1321 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.1113
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.1019
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.0957
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0923
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0900
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0885
[1m290/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5131 - loss: 1.0873
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0864
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Epoch 15/29

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[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0723
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Epoch 16/29

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[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0692
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[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0708
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Epoch 17/29

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[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0681
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[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0688
[1m327/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0686
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Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.6875 - loss: 0.7504
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0500 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0607
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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0629
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0636
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0641
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0640
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0637
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0634
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Epoch 19/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0855 
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[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.0637
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0628
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0616
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5157 - loss: 1.0616 - val_accuracy: 0.5043 - val_loss: 1.0850
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2761
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0557 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0396
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0333
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[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0344
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0355
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0362
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0365
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Epoch 21/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0479 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0574
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0527
[1m189/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0498
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0479
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0469
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0464
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5310 - loss: 1.0459 - val_accuracy: 0.5059 - val_loss: 1.0740
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3125 - loss: 1.1103
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5191 - loss: 1.0350 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0278
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0249
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5316 - loss: 1.0248
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0258
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0272
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0279
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0282
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0283
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5333 - loss: 1.0283 - val_accuracy: 0.5131 - val_loss: 1.0680
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0935
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0466 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5227 - loss: 1.0418
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0350
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0340
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0339
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0340
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0334
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0327
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0320
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Epoch 24/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5372 - loss: 1.0414 
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[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0266
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Epoch 25/29

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Epoch 26/29

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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0092
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5490 - loss: 1.0088
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Epoch 27/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5676 - loss: 1.0018 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5651 - loss: 0.9950
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[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5555 - loss: 0.9973
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 0.9979
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Epoch 28/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 1.0196 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 1.0081
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Epoch 29/29

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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 426ms/step2025-11-07 17:44:33.221115: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 28ms/step
Saved model to disk.
2025-11-07 17:44:45.864507: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:44:45.876562: 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 00:00:1762533885.890948 3521030 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533885.895338 3521030 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533885.906128 3521030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533885.906152 3521030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533885.906155 3521030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533885.906157 3521030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:44:45.909384: 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 00:00:1762533888.152622 3521030 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533889.756557 3521160 service.cc:152] XLA service 0x75f7fc11f600 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533889.756598 3521160 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:44:49.786803: 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 00:00:1762533889.970274 3521160 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533891.644614 3521160 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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[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2684 - loss: 1.7266
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2723 - loss: 1.7170
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2025-11-07 17:44:55.684434: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads

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Epoch 2/29

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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3622 - loss: 1.4480
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Epoch 3/29

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Epoch 4/29

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[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4275 - loss: 1.2695
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Epoch 5/29

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[1m279/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4375 - loss: 1.2398
[1m314/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4383 - loss: 1.2374
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Epoch 6/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1197
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1712 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1795
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[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1843
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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1862
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Epoch 7/29

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[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.2046
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.2041
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4616 - loss: 1.2027
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[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4618 - loss: 1.1999
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4623 - loss: 1.1983
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Epoch 8/29

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[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1519
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Epoch 9/29

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[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4759 - loss: 1.1390
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[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4770 - loss: 1.1409
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1406
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1403
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1405
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1406
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4787 - loss: 1.1404 - val_accuracy: 0.4704 - val_loss: 1.1428
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0202
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.1231 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1299
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1340
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1332
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[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1317
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1314
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.1311
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4935 - loss: 1.1311 - val_accuracy: 0.4691 - val_loss: 1.1327
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2500 - loss: 1.4520
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4568 - loss: 1.1765 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4663 - loss: 1.1624
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[1m139/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1490
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[1m241/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1377
[1m277/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1353
[1m312/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1336
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Epoch 12/29

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[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1199 
[1m 78/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1163
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[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1117
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4932 - loss: 1.1102
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1092
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4948 - loss: 1.1082
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1070
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4963 - loss: 1.1063 - val_accuracy: 0.4754 - val_loss: 1.1178
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1188
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.0842 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.0843
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0871
[1m176/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.0874
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0873
[1m250/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.0875
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0882
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.0890
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5091 - loss: 1.0892 - val_accuracy: 0.4727 - val_loss: 1.1119
Epoch 14/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0410
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5442 - loss: 1.0699 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0754
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0787
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0801
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0800
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0794
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0788
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0785
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0784
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5116 - loss: 1.0783 - val_accuracy: 0.4708 - val_loss: 1.1079
Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0452
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5225 - loss: 1.0508 
[1m 70/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0673
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0714
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0723
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0725
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0728
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0729
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0726
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0724
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5184 - loss: 1.0723 - val_accuracy: 0.4754 - val_loss: 1.1007
Epoch 16/29

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[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4958 - loss: 1.0987 
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[1m171/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0626
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[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0601
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Epoch 17/29

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[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0628
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0629
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Epoch 18/29

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[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0503
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0489
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0477
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0470
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0466
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Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5312 - loss: 1.2494
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0495 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0532
[1m108/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0535
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0522
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0509
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0496
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0480
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0470
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0458
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0455 - val_accuracy: 0.4846 - val_loss: 1.0846
Epoch 20/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 0.9142
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5097 - loss: 1.0238 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.0474
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[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0557
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0546
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.0533
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0519
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0504
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5151 - loss: 1.0502 - val_accuracy: 0.4816 - val_loss: 1.0794
Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.7722
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5747 - loss: 0.9691 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 0.9932
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5508 - loss: 1.0019
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0087
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[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0157
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0177
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0189
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5380 - loss: 1.0197 - val_accuracy: 0.4816 - val_loss: 1.0773
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5938 - loss: 1.1311
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5683 - loss: 1.0340 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5661 - loss: 1.0190
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5602 - loss: 1.0192
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 1.0183
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5536 - loss: 1.0192
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 1.0205
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 1.0213
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0220
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5462 - loss: 1.0226 - val_accuracy: 0.4865 - val_loss: 1.0777
Epoch 23/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.5938 - loss: 0.9544
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0096 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0076
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0092
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0125
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0146
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0155
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0169
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5371 - loss: 1.0176
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5367 - loss: 1.0181 - val_accuracy: 0.4911 - val_loss: 1.0707
Epoch 24/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 0.9754
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5500 - loss: 1.0100 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0162
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0152
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5416 - loss: 1.0148
[1m190/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0156
[1m227/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0160
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0158
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0159
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5395 - loss: 1.0160 - val_accuracy: 0.4852 - val_loss: 1.0708
Epoch 25/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8702
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5753 - loss: 0.9823 
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 0.9964
[1m181/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5510 - loss: 0.9979
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5505 - loss: 0.9992
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Epoch 26/29

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Epoch 27/29

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[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 0.9885
[1m293/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5539 - loss: 0.9886
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5537 - loss: 0.9890 - val_accuracy: 0.4964 - val_loss: 1.0611
Epoch 28/29

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[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5710 - loss: 0.9688
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[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5627 - loss: 0.9800
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[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 0.9832
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5579 - loss: 0.9841
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5571 - loss: 0.9850
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Epoch 29/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 0.9917 
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[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5502 - loss: 0.9912
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5502 - loss: 0.9912 - val_accuracy: 0.4974 - val_loss: 1.0617

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 473ms/step2025-11-07 17:45:15.558266: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 28ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 28ms/step
Saved model to disk.

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:57[0m 1s/step
[1m 62/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 827us/step
[1m133/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 766us/step
[1m200/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 760us/step
[1m272/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 743us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 23ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 24ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m62/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 823us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 52.37 [%]
Global F1 score (validation) = 51.94 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.40344307 0.2908144  0.15645766 0.14928482]
 [0.40344307 0.2908144  0.15645766 0.14928482]
 [0.305468   0.2598818  0.13615003 0.29850012]
 ...
 [0.00843708 0.00643722 0.9813768  0.00374894]
 [0.04235236 0.03673245 0.89765775 0.02325738]
 [0.03813921 0.03581604 0.90399736 0.0220475 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.08 [%]
Global accuracy score (test) = 51.84 [%]
Global F1 score (train) = 58.96 [%]
Global F1 score (test) = 51.35 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.47      0.43       400
MODERATE-INTENSITY       0.45      0.51      0.48       400
         SEDENTARY       0.64      0.73      0.68       400
VIGOROUS-INTENSITY       0.70      0.34      0.46       345

          accuracy                           0.52      1545
         macro avg       0.55      0.51      0.51      1545
      weighted avg       0.54      0.52      0.52      1545


Accuracy capturado en la ejecución 28: 51.84 [%]
F1-score capturado en la ejecución 28: 51.35 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:48[0m 1s/step
[1m 62/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 824us/step
[1m133/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 760us/step
[1m200/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 757us/step
[1m267/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 754us/step
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 760us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 4ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 23ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 23ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 785us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
Global accuracy score (validation) = 50.79 [%]
Global F1 score (validation) = 49.09 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.45872134 0.30264312 0.08177596 0.15685953]
 [0.32766208 0.46968365 0.01498464 0.18766968]
 [0.20912321 0.39825955 0.01812808 0.37448913]
 ...
 [0.0019599  0.00460851 0.99101806 0.00241349]
 [0.02673171 0.0508497  0.9064306  0.01598804]
 [0.00191871 0.00460254 0.9911308  0.00234797]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.83 [%]
Global accuracy score (test) = 50.55 [%]
Global F1 score (train) = 56.55 [%]
Global F1 score (test) = 49.39 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.56      0.46       400
MODERATE-INTENSITY       0.39      0.30      0.34       400
         SEDENTARY       0.66      0.79      0.72       400
VIGOROUS-INTENSITY       0.64      0.35      0.45       345

          accuracy                           0.51      1545
         macro avg       0.52      0.50      0.49      1545
      weighted avg       0.52      0.51      0.50      1545


Accuracy capturado en la ejecución 29: 50.55 [%]
2025-11-07 17:45:28.229183: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 17:45:28.240903: 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 00:00:1762533928.254627 3524873 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762533928.258762 3524873 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762533928.269124 3524873 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533928.269150 3524873 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533928.269152 3524873 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762533928.269154 3524873 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 17:45:28.272423: 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 00:00:1762533930.515316 3524873 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13760 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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Epoch 1/29
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762533932.123884 3524979 service.cc:152] XLA service 0x72bb1c00ccd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762533932.123946 3524979 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-07 17:45:32.162451: 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 00:00:1762533932.351943 3524979 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762533934.024106 3524979 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16:01[0m 3s/step - accuracy: 0.4062 - loss: 1.7157
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[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2754 - loss: 1.7634
[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2781 - loss: 1.7533
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2025-11-07 17:45:38.076824: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_92', 108 bytes spill stores, 108 bytes spill loads

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Epoch 2/29

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[1m 99/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3654 - loss: 1.4990
[1m133/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.3657 - loss: 1.4973
[1m171/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3657 - loss: 1.4968
[1m208/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3659 - loss: 1.4958
[1m244/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3660 - loss: 1.4950
[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3662 - loss: 1.4934
[1m318/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3669 - loss: 1.4909
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Epoch 3/29

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[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3813 - loss: 1.4210
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.4140
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[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3885 - loss: 1.4000
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3893 - loss: 1.3965
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Epoch 4/29

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[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4062 - loss: 1.3178
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Epoch 5/29

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Epoch 6/29

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[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4407 - loss: 1.2446
[1m298/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.2435
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Epoch 7/29

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[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4466 - loss: 1.2266
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4474 - loss: 1.2244
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4480 - loss: 1.2229
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Epoch 8/29

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[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.1816 
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1712
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4703 - loss: 1.1748
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Epoch 9/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1081 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1199
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4902 - loss: 1.1304
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1377
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4847 - loss: 1.1407
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1432
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1448
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1458
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4800 - loss: 1.1467 - val_accuracy: 0.4668 - val_loss: 1.1604
Epoch 10/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4062 - loss: 1.1663
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1528 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4758 - loss: 1.1497
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1476
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1464
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4822 - loss: 1.1466
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1468
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4822 - loss: 1.1472
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1472
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4820 - loss: 1.1468 - val_accuracy: 0.4786 - val_loss: 1.1495
Epoch 11/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.5312 - loss: 0.9658
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.1250 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.1242
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1299
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1311
[1m179/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1305
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4890 - loss: 1.1294
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1285
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1278
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1274
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4883 - loss: 1.1274 - val_accuracy: 0.4721 - val_loss: 1.1392
Epoch 12/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.2812 - loss: 1.4102
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4472 - loss: 1.1765 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4593 - loss: 1.1544
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4666 - loss: 1.1430
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1361
[1m186/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1311
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1281
[1m263/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1262
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1250
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4805 - loss: 1.1244 - val_accuracy: 0.4763 - val_loss: 1.1316
Epoch 13/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9257
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1124 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1098
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4836 - loss: 1.1077
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1070
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1064
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1069
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1073
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1073
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.4885 - loss: 1.1071 - val_accuracy: 0.4832 - val_loss: 1.1219
Epoch 14/29

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[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0576 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0514
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[1m314/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0865
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Epoch 15/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0526
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5093 - loss: 1.0621 
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[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.0727
[1m173/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.0767
[1m207/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.0795
[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.0811
[1m285/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.0820
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.0822
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Epoch 16/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9723
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1269 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.1010
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[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0878
[1m185/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0863
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0847
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0833
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0822
[1m325/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0814
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5161 - loss: 1.0812 - val_accuracy: 0.5023 - val_loss: 1.1028
Epoch 17/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2597
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.0738 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.0737
[1m104/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5067 - loss: 1.0743
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0731
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0713
[1m210/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.0698
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0693
[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0690
[1m317/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0685
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5127 - loss: 1.0683 - val_accuracy: 0.5053 - val_loss: 1.0974
Epoch 18/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9418
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0565 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0569
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[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0564
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[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0558
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0562
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0565
[1m326/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0569
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5230 - loss: 1.0570 - val_accuracy: 0.5030 - val_loss: 1.0941
Epoch 19/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.5000 - loss: 1.0997
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1212 
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1015
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[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0795
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[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0722
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0699
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Epoch 20/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0357 
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[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0348
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0351
[1m184/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0353
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5427 - loss: 1.0356
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0363
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0372
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0381
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Epoch 21/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 0.8558
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5152 - loss: 1.0481 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0449
[1m109/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0438
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0434
[1m183/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0439
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0436
[1m255/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0432
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0431
[1m328/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0429
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5271 - loss: 1.0429 - val_accuracy: 0.5085 - val_loss: 1.0817
Epoch 22/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1357
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0508 
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5316 - loss: 1.0465
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0449
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0434
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0418
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0407
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0401
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0395
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5350 - loss: 1.0387 - val_accuracy: 0.5102 - val_loss: 1.0766
Epoch 23/29

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[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5566 - loss: 1.0188 
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[1m230/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0206
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0211
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0215
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Epoch 24/29

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[1m306/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0144
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Epoch 25/29

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[1m258/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0239
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5305 - loss: 1.0234
[1m330/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0227
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5319 - loss: 1.0226 - val_accuracy: 0.5220 - val_loss: 1.0653
Epoch 26/29

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[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0587 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0342
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[1m152/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0211
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0187
[1m231/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5464 - loss: 1.0171
[1m267/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5468 - loss: 1.0162
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5471 - loss: 1.0153
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5473 - loss: 1.0146 - val_accuracy: 0.5246 - val_loss: 1.0596
Epoch 27/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0083
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0250 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0229
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[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0131
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[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0100
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0102
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0102
[1m329/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0104
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step - accuracy: 0.5395 - loss: 1.0104 - val_accuracy: 0.5223 - val_loss: 1.0599
Epoch 28/29

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5938 - loss: 0.8845
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5556 - loss: 0.9709 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5514 - loss: 0.9816
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 0.9850
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5521 - loss: 0.9875
[1m187/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5514 - loss: 0.9900
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5507 - loss: 0.9925
[1m259/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5495 - loss: 0.9947
[1m295/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5488 - loss: 0.9959
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Epoch 29/29

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[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5523 - loss: 1.0072
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[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 427ms/step2025-11-07 17:45:57.708126: I external/local_xla/xla/stream_executor/cuda/subprocess_compilation.cc:346] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_70', 136 bytes spill stores, 140 bytes spill loads


[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 27ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 27ms/step
Saved model to disk.
F1-score capturado en la ejecución 29: 49.39 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['LIGHT-INTENSITY' 'MODERATE-INTENSITY' 'SEDENTARY' 'VIGOROUS-INTENSITY']
4
Mapeo de etiquetas: {'LIGHT-INTENSITY': 0, 'MODERATE-INTENSITY': 1, 'SEDENTARY': 2, 'VIGOROUS-INTENSITY': 3}
This activity can't be balanced (in a downsampling way)
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Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7:55[0m 1s/step
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[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 26ms/step
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[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m53/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 962us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step
Global accuracy score (validation) = 51.91 [%]
Global F1 score (validation) = 51.94 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.39528728 0.3364958  0.08220097 0.18601601]
 [0.27878034 0.28570455 0.12448902 0.31102607]
 [0.40804774 0.31441605 0.08801635 0.18951981]
 ...
 [0.00189095 0.00301404 0.99324024 0.00185478]
 [0.00201525 0.00317119 0.99288917 0.00192438]
 [0.01091347 0.0173983  0.9598659  0.01182225]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.07 [%]
Global accuracy score (test) = 51.84 [%]
Global F1 score (train) = 58.86 [%]
Global F1 score (test) = 51.87 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.42      0.39       400
MODERATE-INTENSITY       0.48      0.47      0.48       400
         SEDENTARY       0.62      0.73      0.67       400
VIGOROUS-INTENSITY       0.69      0.44      0.53       345

          accuracy                           0.52      1545
         macro avg       0.54      0.52      0.52      1545
      weighted avg       0.53      0.52      0.52      1545


Accuracy capturado en la ejecución 30: 51.84 [%]
F1-score capturado en la ejecución 30: 51.87 [%]

=== RESUMEN FINAL ===
Accuracies: [50.87, 53.01, 51.26, 48.22, 50.81, 48.87, 50.87, 49.9, 50.03, 52.62, 50.61, 52.49, 52.1, 52.43, 50.49, 50.74, 52.1, 50.36, 52.75, 50.68, 48.41, 49.97, 51.13, 51.07, 50.42, 50.16, 50.1, 51.84, 50.55, 51.84]
F1-scores: [50.22, 52.51, 51.18, 46.67, 50.12, 48.05, 51.11, 49.29, 49.02, 51.29, 49.44, 52.7, 51.92, 51.2, 50.71, 50.95, 50.94, 49.65, 52.53, 49.36, 47.42, 49.09, 50.45, 50.19, 48.78, 49.35, 50.07, 51.35, 49.39, 51.87]
Accuracy mean: 50.8900 | std: 1.1951
F1 mean: 50.2273 | std: 1.4462

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_CAPTURE24_acc_superclasses_CPA_METs/metrics_test.npz
