2025-11-05 11:10:11.340602: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:10:11.352390: 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 00:00:1762337411.366363 3116668 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762337411.370694 3116668 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762337411.381211 3116668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337411.381234 3116668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337411.381236 3116668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337411.381238 3116668 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:10:11.384377: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-05 11:10:14,355	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-05 11:10:15,038	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-05 11:10:15,109	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_2186 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,111	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_44be because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,113	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_308f because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,115	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_9cf3 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,117	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_d403 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,119	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_1f3e because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,121	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_f3b8 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,124	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_ad32 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,127	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_c7b5 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,129	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_c2b3 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,132	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_fcb5 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,135	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_896e because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,138	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_9812 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,141	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_23af because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,144	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_4ad4 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,148	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_c068 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,151	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_bce0 because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,156	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_1acb because trial dirname 'dir_9f7c8' already exists.
2025-11-05 11:10:15,161	INFO trial.py:182 -- Creating a new dirname dir_9f7c8_572b because trial dirname 'dir_9f7c8' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
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)
Se lanza la búsqueda de hiperparámetros óptimos del modelo
╭─────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     ESANN_hyperparameters_tuning   │
├─────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator          │
│ Scheduler                        AsyncHyperBandScheduler        │
│ Number of trials                 20                             │
╰─────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_ESANN_acc_superclasses_CPA_METs/ESANN_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-05_11-10-13_643939_3116668/artifacts/2025-11-05_11-10-15/ESANN_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-05 11:10:15. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    PENDING            4   adam            relu                                   64                128                  5          0.00168541         137 │
│ trial_9f7c8    PENDING            2   adam            tanh                                   32                 32                  5          0.00357251         149 │
│ trial_9f7c8    PENDING            2   adam            relu                                   32                128                  3          1.05278e-05         72 │
│ trial_9f7c8    PENDING            4   rmsprop         tanh                                  128                 32                  3          0.000165933        107 │
│ trial_9f7c8    PENDING            3   rmsprop         relu                                   32                 64                  3          0.000159733         73 │
│ trial_9f7c8    PENDING            2   rmsprop         tanh                                   32                128                  5          0.000146441        116 │
│ trial_9f7c8    PENDING            4   adam            relu                                   64                 64                  3          0.000386417        121 │
│ trial_9f7c8    PENDING            2   adam            relu                                   64                 32                  5          1.56689e-05        116 │
│ trial_9f7c8    PENDING            3   rmsprop         tanh                                   32                128                  3          0.00149202          59 │
│ trial_9f7c8    PENDING            4   adam            tanh                                   64                 32                  5          0.00247751         130 │
│ trial_9f7c8    PENDING            2   rmsprop         relu                                   64                 32                  3          0.000917269        145 │
│ trial_9f7c8    PENDING            4   adam            tanh                                   32                128                  3          6.9214e-05          65 │
│ trial_9f7c8    PENDING            3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111 │
│ trial_9f7c8    PENDING            2   adam            relu                                   64                 32                  5          0.000113503        140 │
│ trial_9f7c8    PENDING            2   rmsprop         relu                                  128                128                  5          7.16419e-05         66 │
│ trial_9f7c8    PENDING            2   rmsprop         relu                                   64                 32                  5          0.0020272           93 │
│ trial_9f7c8    PENDING            2   rmsprop         tanh                                   32                128                  3          0.00213202          64 │
│ trial_9f7c8    PENDING            2   rmsprop         tanh                                   64                 32                  3          0.000203535         58 │
│ trial_9f7c8    PENDING            3   adam            tanh                                  128                128                  3          0.000931045         72 │
│ trial_9f7c8    PENDING            4   rmsprop         relu                                  128                128                  3          0.00205798         108 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            59 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00149 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            58 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje              0.0002 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            73 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00016 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            66 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            72 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00001 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           116 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00015 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           149 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00357 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           137 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00169 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           140 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00011 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           145 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00092 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                            65 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00007 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                           116 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00002 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                           111 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00004 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
[36m(train_cnn_ray_tune pid=3118311)[0m 2025-11-05 11:10:18.318794: 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=3118311)[0m 2025-11-05 11:10:18.340203: 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=3118311)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_cnn_ray_tune pid=3118311)[0m E0000 00:00:1762337418.368329 3119440 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=3118311)[0m E0000 00:00:1762337418.376988 3119440 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=3118311)[0m W0000 00:00:1762337418.397804 3119440 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=3118311)[0m W0000 00:00:1762337418.397852 3119440 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=3118311)[0m W0000 00:00:1762337418.397854 3119440 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=3118311)[0m W0000 00:00:1762337418.397856 3119440 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=3118311)[0m 2025-11-05 11:10:18.403884: 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=3118311)[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=3118311)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
[36m(train_cnn_ray_tune pid=3118311)[0m   warnings.warn(
[36m(train_cnn_ray_tune pid=3118311)[0m 2025-11-05 11:10:21.557033: 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=3118311)[0m 2025-11-05 11:10:21.557085: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:167] env: CUDA_VISIBLE_DEVICES=""
[36m(train_cnn_ray_tune pid=3118311)[0m 2025-11-05 11:10:21.557094: 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=3118311)[0m 2025-11-05 11:10:21.557100: 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=3118311)[0m 2025-11-05 11:10:21.557105: 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=3118311)[0m 2025-11-05 11:10:21.557109: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:190] hostname: simur-MS-7B94
[36m(train_cnn_ray_tune pid=3118311)[0m 2025-11-05 11:10:21.557336: 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=3118311)[0m 2025-11-05 11:10:21.557368: 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=3118311)[0m 2025-11-05 11:10:21.557373: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:291] kernel version seems to match DSO: 570.133.7
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           107 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00017 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           130 │
│ funcion_activacion              tanh │
│ numero_filtros                    32 │
│ optimizador                     adam │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00248 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            93 │
│ funcion_activacion              relu │
│ numero_filtros                    32 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     5 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00203 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           121 │
│ funcion_activacion              relu │
│ numero_filtros                    64 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  64 │
│ tasa_aprendizaje             0.00039 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            2 │
│ epochs                            64 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                  32 │
│ tasa_aprendizaje             0.00213 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            3 │
│ epochs                            72 │
│ funcion_activacion              tanh │
│ numero_filtros                   128 │
│ optimizador                     adam │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00093 │
╰──────────────────────────────────────╯
Trial trial_9f7c8 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 config             │
├──────────────────────────────────────┤
│ N_capas                            4 │
│ epochs                           108 │
│ funcion_activacion              relu │
│ numero_filtros                   128 │
│ optimizador                  rmsprop │
│ tamanho_filtro                     3 │
│ tamanho_minilote                 128 │
│ tasa_aprendizaje             0.00206 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118311)[0m Model: "sequential"
[36m(train_cnn_ray_tune pid=3118311)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
[36m(train_cnn_ray_tune pid=3118311)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
[36m(train_cnn_ray_tune pid=3118311)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
[36m(train_cnn_ray_tune pid=3118311)[0m │ conv1d (Conv1D)                 │ (None, 3, 128)         │        96,128 │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ layer_normalization             │ (None, 3, 128)         │           256 │
[36m(train_cnn_ray_tune pid=3118311)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ dropout (Dropout)               │ (None, 3, 128)         │             0 │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ conv1d_1 (Conv1D)               │ (None, 3, 128)         │        49,280 │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ layer_normalization_1           │ (None, 3, 128)         │           256 │
[36m(train_cnn_ray_tune pid=3118311)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ dropout_1 (Dropout)             │ (None, 3, 128)         │             0 │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ conv1d_2 (Conv1D)               │ (None, 3, 128)         │        49,280 │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ layer_normalization_2           │ (None, 3, 128)         │           256 │
[36m(train_cnn_ray_tune pid=3118311)[0m │ (LayerNormalization)            │                        │               │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ dropout_2 (Dropout)             │ (None, 3, 128)         │             0 │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ global_average_pooling1d        │ (None, 128)            │             0 │
[36m(train_cnn_ray_tune pid=3118311)[0m │ (GlobalAveragePooling1D)        │                        │               │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ dropout_3 (Dropout)             │ (None, 128)            │             0 │
[36m(train_cnn_ray_tune pid=3118311)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤
[36m(train_cnn_ray_tune pid=3118311)[0m │ dense (Dense)                   │ (None, 4)              │           516 │
[36m(train_cnn_ray_tune pid=3118311)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘
[36m(train_cnn_ray_tune pid=3118311)[0m  Total params: 195,972 (765.52 KB)
[36m(train_cnn_ray_tune pid=3118311)[0m  Trainable params: 195,972 (765.52 KB)
[36m(train_cnn_ray_tune pid=3118311)[0m  Non-trainable params: 0 (0.00 B)
[36m(train_cnn_ray_tune pid=3118311)[0m Epoch 1/59
[36m(train_cnn_ray_tune pid=3118307)[0m  Total params: 407,812 (1.56 MB)
[36m(train_cnn_ray_tune pid=3118307)[0m  Trainable params: 407,812 (1.56 MB)
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:07[0m 2s/step - accuracy: 0.2266 - loss: 3.1608
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.2535 - loss: 3.0632
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.2615 - loss: 2.9926
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3542 - loss: 2.2274 
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2497 - loss: 2.6697
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.2600 - loss: 2.6177
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 7/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 29ms/step - accuracy: 0.2627 - loss: 2.9493
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:25[0m 2s/step - accuracy: 0.2656 - loss: 2.1605
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.2413 - loss: 2.4046
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 9/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 30ms/step - accuracy: 0.2642 - loss: 2.9077
[1m11/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.2654 - loss: 2.8778
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.2496 - loss: 2.3946
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m13/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.2660 - loss: 2.8561
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m15/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.2665 - loss: 2.8371
[1m17/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.2669 - loss: 2.8220
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m19/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.2668 - loss: 2.8111
[1m21/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.2667 - loss: 2.8015
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m23/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 33ms/step - accuracy: 0.2668 - loss: 2.7923
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m25/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 33ms/step - accuracy: 0.2670 - loss: 2.7843
[1m26/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2670 - loss: 2.7808
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m28/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2672 - loss: 2.7734
[1m30/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2675 - loss: 2.7655
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m32/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2679 - loss: 2.7581
[1m34/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2683 - loss: 2.7511
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m36/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2687 - loss: 2.7443
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2692 - loss: 2.7380
[1m40/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2696 - loss: 2.7320
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m42/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.2699 - loss: 2.7259
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m44/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 35ms/step - accuracy: 0.2702 - loss: 2.7199
[1m45/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 35ms/step - accuracy: 0.2704 - loss: 2.7170
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.3043 - loss: 2.4785
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.3020 - loss: 2.4905
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m47/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 36ms/step - accuracy: 0.2708 - loss: 2.7114
[1m48/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 36ms/step - accuracy: 0.2709 - loss: 2.7087
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3006 - loss: 2.5015
[1m 12/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.2972 - loss: 2.5165
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m49/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 36ms/step - accuracy: 0.2711 - loss: 2.7062
[1m50/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 37ms/step - accuracy: 0.2713 - loss: 2.7038
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.2949 - loss: 2.5294
[1m 16/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.2923 - loss: 2.5422
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m52/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 37ms/step - accuracy: 0.2716 - loss: 2.6991
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.2914 - loss: 2.5477
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.2897 - loss: 2.5559
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m54/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 37ms/step - accuracy: 0.2720 - loss: 2.6944
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m55/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 38ms/step - accuracy: 0.2721 - loss: 2.6923
[1m57/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 37ms/step - accuracy: 0.2725 - loss: 2.6880
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.2889 - loss: 2.5618
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.2885 - loss: 2.5627
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m59/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 38ms/step - accuracy: 0.2729 - loss: 2.6840
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m61/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 38ms/step - accuracy: 0.2733 - loss: 2.6801
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.2866 - loss: 2.5611
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.2860 - loss: 2.5612
[36m(train_cnn_ray_tune pid=3118313)[0m Model: "sequential"[32m [repeated 19x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)[0m
[36m(train_cnn_ray_tune pid=3118313)[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m ┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m │ global_average_pooling1d        │ (None, 128)            │             0 │[32m [repeated 106x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m ├─────────────────────────────────┼────────────────────────┼───────────────┤[32m [repeated 197x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m │ layer_normalization             │ (None, 3, 128)         │           256 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m │ (LayerNormalization)            │                        │               │[32m [repeated 53x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m │ dropout (Dropout)               │ (None, 3, 128)         │             0 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m │ dropout_4 (Dropout)             │ (None, 128)            │             0 │[32m [repeated 53x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m │ (GlobalAveragePooling1D)        │                        │               │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m │ dense (Dense)                   │ (None, 4)              │           516 │[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m └─────────────────────────────────┴────────────────────────┴───────────────┘[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m  Total params: 245,508 (959.02 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m  Trainable params: 245,508 (959.02 KB)[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m  Non-trainable params: 0 (0.00 B)[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 33ms/step - accuracy: 0.2852 - loss: 2.5619 
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.2847 - loss: 2.5603
[36m(train_cnn_ray_tune pid=3118313)[0m Epoch 1/108[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m68/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 39ms/step - accuracy: 0.2748 - loss: 2.6669
[1m70/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 39ms/step - accuracy: 0.2752 - loss: 2.6633
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 20ms/step - accuracy: 0.2633 - loss: 2.4082
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 20ms/step - accuracy: 0.2634 - loss: 2.4072
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 20ms/step - accuracy: 0.2636 - loss: 2.4062
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step - accuracy: 0.2655 - loss: 2.3926
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 21ms/step - accuracy: 0.2657 - loss: 2.3917
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step - accuracy: 0.2658 - loss: 2.3907
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 51ms/step - accuracy: 0.2778 - loss: 2.6410 - val_accuracy: 0.3985 - val_loss: 1.3904
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 28ms/step - accuracy: 0.2973 - loss: 2.2512 - val_accuracy: 0.3696 - val_loss: 1.2952
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 37ms/step - accuracy: 0.2951 - loss: 1.6865  
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 130ms/step - accuracy: 0.2969 - loss: 1.6348[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m31/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 73ms/step - accuracy: 0.2827 - loss: 2.2444[32m [repeated 50x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 68ms/step - accuracy: 0.2727 - loss: 2.6172[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 12/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3300 - loss: 1.6728
[1m 15/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 26ms/step - accuracy: 0.3334 - loss: 1.6637[32m [repeated 281x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 105ms/step - accuracy: 0.2969 - loss: 2.3875
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 48ms/step - accuracy: 0.3220 - loss: 2.3496 [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m25/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 65ms/step - accuracy: 0.2795 - loss: 2.5437
[1m26/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 65ms/step - accuracy: 0.2801 - loss: 2.5398[32m [repeated 31x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m6s[0m 31ms/step - accuracy: 0.2348 - loss: 2.8516[32m [repeated 182x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.2965 - loss: 2.0328  
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.2903 - loss: 2.0613
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 31/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 75ms/step - accuracy: 0.2739 - loss: 2.5569
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 75ms/step - accuracy: 0.2739 - loss: 2.5542[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 74ms/step - accuracy: 0.2740 - loss: 2.5518 
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 75ms/step - accuracy: 0.2741 - loss: 2.5492[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 2/116[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m231/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.2922 - loss: 1.9515
[1m233/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.2924 - loss: 1.9486
[1m235/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.2925 - loss: 1.9458
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 57ms/step - accuracy: 0.3137 - loss: 2.3776 - val_accuracy: 0.3853 - val_loss: 1.3882[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m268/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 30ms/step - accuracy: 0.2790 - loss: 2.5484
[1m270/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 30ms/step - accuracy: 0.2790 - loss: 2.5480
[1m272/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 30ms/step - accuracy: 0.2791 - loss: 2.5476
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 40ms/step - accuracy: 0.2474 - loss: 2.6512 - val_accuracy: 0.2178 - val_loss: 1.6124[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 51ms/step - accuracy: 0.2578 - loss: 2.4216  
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 84ms/step - accuracy: 0.2812 - loss: 1.4638[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m23/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 43ms/step - accuracy: 0.3431 - loss: 2.2140[32m [repeated 83x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3034 - loss: 1.4254 [32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 58/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 33ms/step - accuracy: 0.2432 - loss: 2.5951
[1m 60/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 33ms/step - accuracy: 0.2432 - loss: 2.5935[32m [repeated 321x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 87ms/step - accuracy: 0.4375 - loss: 2.1217
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 44ms/step - accuracy: 0.4028 - loss: 2.1565[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m25/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 42ms/step - accuracy: 0.3420 - loss: 2.2124
[1m27/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 42ms/step - accuracy: 0.3410 - loss: 2.2115[32m [repeated 64x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 32ms/step - accuracy: 0.2375 - loss: 2.8175[32m [repeated 275x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m137/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m10s[0m 53ms/step - accuracy: 0.2707 - loss: 2.7232
[1m138/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m10s[0m 53ms/step - accuracy: 0.2707 - loss: 2.7226[32m [repeated 22x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2650 - loss: 2.0963 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2714 - loss: 2.0372[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m Epoch 2/64[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m49/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 35ms/step - accuracy: 0.2854 - loss: 2.3225
[1m50/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 36ms/step - accuracy: 0.2852 - loss: 2.3221
[1m51/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 36ms/step - accuracy: 0.2850 - loss: 2.3217
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3438 - loss: 2.0290 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3409 - loss: 2.0358
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 35ms/step - accuracy: 0.3186 - loss: 2.0218  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 34ms/step - accuracy: 0.3160 - loss: 2.0026
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.2865 - loss: 2.5691  
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 50ms/step - accuracy: 0.3332 - loss: 2.1944 - val_accuracy: 0.3817 - val_loss: 1.3363[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 26ms/step - accuracy: 0.2803 - loss: 2.0088 - val_accuracy: 0.3771 - val_loss: 1.3243[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 87ms/step - accuracy: 0.2969 - loss: 2.1567[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 51ms/step - accuracy: 0.3244 - loss: 2.1018[32m [repeated 76x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 36ms/step - accuracy: 0.2619 - loss: 2.4837[32m [repeated 26x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3400 - loss: 1.3079
[1m 10/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3397 - loss: 1.3057[32m [repeated 285x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 68ms/step - accuracy: 0.2969 - loss: 1.3188
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3311 - loss: 1.3177 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m27/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 61ms/step - accuracy: 0.2965 - loss: 1.8962
[1m28/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 62ms/step - accuracy: 0.2966 - loss: 1.8946[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 24ms/step - accuracy: 0.2699 - loss: 2.4225[32m [repeated 238x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.3485 - loss: 1.3695
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 37ms/step - accuracy: 0.3484 - loss: 1.3694[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 44ms/step - accuracy: 0.2921 - loss: 2.0392
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 44ms/step - accuracy: 0.2920 - loss: 2.0391
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m4s[0m 44ms/step - accuracy: 0.2919 - loss: 2.0391
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3096 - loss: 1.7443 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.2956 - loss: 1.7788[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m Epoch 3/108[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 46ms/step - accuracy: 0.3112 - loss: 2.0360  
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 48ms/step - accuracy: 0.3047 - loss: 2.0375
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2361 - loss: 2.3485  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.2423 - loss: 2.3615
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 83ms/step - accuracy: 0.4141 - loss: 1.3974 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 57ms/step - accuracy: 0.3272 - loss: 2.0815 - val_accuracy: 0.4044 - val_loss: 1.2857[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 29ms/step - accuracy: 0.2894 - loss: 1.8476 - val_accuracy: 0.3890 - val_loss: 1.3139[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 96ms/step - accuracy: 0.2812 - loss: 1.3814[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 8/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 66ms/step - accuracy: 0.3135 - loss: 1.6103[32m [repeated 77x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 81/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 40ms/step - accuracy: 0.3511 - loss: 1.3645 [32m [repeated 27x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m202/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m4s[0m 34ms/step - accuracy: 0.2696 - loss: 2.4952
[1m204/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m4s[0m 34ms/step - accuracy: 0.2697 - loss: 2.4954[32m [repeated 336x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 120ms/step - accuracy: 0.2734 - loss: 2.4924
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 61ms/step - accuracy: 0.2500 - loss: 2.4549 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 44ms/step - accuracy: 0.2489 - loss: 2.4121
[1m 6/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 40ms/step - accuracy: 0.2507 - loss: 2.3955[32m [repeated 64x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m242/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.3576 - loss: 1.3280[32m [repeated 297x across cluster][0m

Trial status: 20 RUNNING
Current time: 2025-11-05 11:10:45. Total running time: 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING            4   adam            relu                                   64                128                  5          0.00168541         137 │
│ trial_9f7c8    RUNNING            2   adam            tanh                                   32                 32                  5          0.00357251         149 │
│ trial_9f7c8    RUNNING            2   adam            relu                                   32                128                  3          1.05278e-05         72 │
│ trial_9f7c8    RUNNING            4   rmsprop         tanh                                  128                 32                  3          0.000165933        107 │
│ trial_9f7c8    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000159733         73 │
│ trial_9f7c8    RUNNING            2   rmsprop         tanh                                   32                128                  5          0.000146441        116 │
│ trial_9f7c8    RUNNING            4   adam            relu                                   64                 64                  3          0.000386417        121 │
│ trial_9f7c8    RUNNING            2   adam            relu                                   64                 32                  5          1.56689e-05        116 │
│ trial_9f7c8    RUNNING            3   rmsprop         tanh                                   32                128                  3          0.00149202          59 │
│ trial_9f7c8    RUNNING            4   adam            tanh                                   64                 32                  5          0.00247751         130 │
│ trial_9f7c8    RUNNING            2   rmsprop         relu                                   64                 32                  3          0.000917269        145 │
│ trial_9f7c8    RUNNING            4   adam            tanh                                   32                128                  3          6.9214e-05          65 │
│ trial_9f7c8    RUNNING            3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111 │
│ trial_9f7c8    RUNNING            2   adam            relu                                   64                 32                  5          0.000113503        140 │
│ trial_9f7c8    RUNNING            2   rmsprop         relu                                  128                128                  5          7.16419e-05         66 │
│ trial_9f7c8    RUNNING            2   rmsprop         relu                                   64                 32                  5          0.0020272           93 │
│ trial_9f7c8    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00213202          64 │
│ trial_9f7c8    RUNNING            2   rmsprop         tanh                                   64                 32                  3          0.000203535         58 │
│ trial_9f7c8    RUNNING            3   adam            tanh                                  128                128                  3          0.000931045         72 │
│ trial_9f7c8    RUNNING            4   rmsprop         relu                                  128                128                  3          0.00205798         108 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 25/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 70ms/step - accuracy: 0.3494 - loss: 1.4266
[1m 26/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 70ms/step - accuracy: 0.3489 - loss: 1.4265 [32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 55/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.3661 - loss: 1.3256
[1m 57/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.3661 - loss: 1.3254
[1m 59/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.3662 - loss: 1.3252[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 51ms/step - accuracy: 0.3008 - loss: 1.9389  
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 45ms/step - accuracy: 0.2985 - loss: 1.8863
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3160 - loss: 1.3589 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3395 - loss: 1.3368
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 4/116[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3854 - loss: 1.3375  
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 83ms/step - accuracy: 0.3502 - loss: 1.3284 - val_accuracy: 0.3844 - val_loss: 1.2590[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 71ms/step - accuracy: 0.3770 - loss: 1.2951  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 64ms/step - accuracy: 0.3659 - loss: 1.2966
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 43ms/step - accuracy: 0.2661 - loss: 2.0218 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 46ms/step - accuracy: 0.2660 - loss: 2.0282
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 30ms/step - accuracy: 0.3040 - loss: 1.6936 - val_accuracy: 0.3965 - val_loss: 1.2973[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 107ms/step - accuracy: 0.2422 - loss: 2.1072[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 66ms/step - accuracy: 0.3183 - loss: 1.5632[32m [repeated 95x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 49ms/step - accuracy: 0.2723 - loss: 2.3948[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 66ms/step - accuracy: 0.3342 - loss: 1.4065
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 66ms/step - accuracy: 0.3343 - loss: 1.4063[32m [repeated 323x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 83ms/step - accuracy: 0.2500 - loss: 1.4950
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 33ms/step - accuracy: 0.3368 - loss: 1.4432[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m13/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m4s[0m 66ms/step - accuracy: 0.3568 - loss: 1.3015
[1m14/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m4s[0m 67ms/step - accuracy: 0.3568 - loss: 1.3006[32m [repeated 69x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m140/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.2913 - loss: 1.7398[32m [repeated 299x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.2865 - loss: 2.3614  
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 51/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m12s[0m 44ms/step - accuracy: 0.2757 - loss: 2.4339
[1m 53/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m12s[0m 44ms/step - accuracy: 0.2758 - loss: 2.4360[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 44ms/step - accuracy: 0.3557 - loss: 1.3362
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 44ms/step - accuracy: 0.3554 - loss: 1.3365
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 44ms/step - accuracy: 0.3552 - loss: 1.3368
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2497 - loss: 2.2024  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.2513 - loss: 2.2039
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 5/116[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 45ms/step - accuracy: 0.2794 - loss: 2.4584 
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 45ms/step - accuracy: 0.2795 - loss: 2.4588
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 48ms/step - accuracy: 0.2696 - loss: 2.0775 - val_accuracy: 0.3660 - val_loss: 1.4478[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 23ms/step - accuracy: 0.3002 - loss: 1.6508
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 23ms/step - accuracy: 0.3002 - loss: 1.6505
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 23ms/step - accuracy: 0.3002 - loss: 1.6502
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 28ms/step - accuracy: 0.3775 - loss: 1.2961 - val_accuracy: 0.3827 - val_loss: 1.2412[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 87ms/step - accuracy: 0.4062 - loss: 1.3120[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 6/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 76ms/step - accuracy: 0.2964 - loss: 1.4782[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 45ms/step - accuracy: 0.2794 - loss: 2.4579[32m [repeated 44x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 23ms/step - accuracy: 0.3001 - loss: 1.6483
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 23ms/step - accuracy: 0.3000 - loss: 1.6482[32m [repeated 328x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:40[0m 4s/step - accuracy: 0.3125 - loss: 1.4406
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 65ms/step - accuracy: 0.3086 - loss: 1.4500[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 75ms/step - accuracy: 0.2986 - loss: 1.4681
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 72ms/step - accuracy: 0.2967 - loss: 1.4764[32m [repeated 58x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m 56/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 36ms/step - accuracy: 0.3589 - loss: 1.3316[32m [repeated 227x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 55ms/step - accuracy: 0.2804 - loss: 1.7060  
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 58/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3414 - loss: 1.3033
[1m 60/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3414 - loss: 1.3038[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.2708 - loss: 1.6958  
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 6/116[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m29/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 79ms/step - accuracy: 0.4065 - loss: 1.2343
[1m30/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 78ms/step - accuracy: 0.4065 - loss: 1.2340
[1m31/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 78ms/step - accuracy: 0.4065 - loss: 1.2337
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3802 - loss: 1.2817 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3865 - loss: 1.2696[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 50ms/step - accuracy: 0.2775 - loss: 1.9886 - val_accuracy: 0.3729 - val_loss: 1.4367[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 22/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 35ms/step - accuracy: 0.2534 - loss: 2.1830
[1m 23/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 36ms/step - accuracy: 0.2537 - loss: 2.1822
[1m 25/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 35ms/step - accuracy: 0.2544 - loss: 2.1819[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 31ms/step - accuracy: 0.3191 - loss: 1.5473 - val_accuracy: 0.3949 - val_loss: 1.2941[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 112ms/step - accuracy: 0.2344 - loss: 2.1177[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m55/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 78ms/step - accuracy: 0.4051 - loss: 1.2303[32m [repeated 103x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.3415 - loss: 1.3050[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m4s[0m 34ms/step - accuracy: 0.2580 - loss: 2.1822
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m4s[0m 34ms/step - accuracy: 0.2584 - loss: 2.1821[32m [repeated 321x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 85ms/step - accuracy: 0.3281 - loss: 1.6307
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 18ms/step - accuracy: 0.3044 - loss: 1.6198 [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m79/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 67ms/step - accuracy: 0.3214 - loss: 1.4427
[1m80/84[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 67ms/step - accuracy: 0.3215 - loss: 1.4423[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 33ms/step - accuracy: 0.2970 - loss: 2.4172[32m [repeated 381x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.3416 - loss: 1.3055
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 38ms/step - accuracy: 0.3417 - loss: 1.3060 
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 43ms/step - accuracy: 0.4375 - loss: 1.2765  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 57ms/step - accuracy: 0.3613 - loss: 1.4769  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 53ms/step - accuracy: 0.3572 - loss: 1.5110
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 52ms/step - accuracy: 0.3548 - loss: 1.5327
[36m(train_cnn_ray_tune pid=3118316)[0m Epoch 9/107[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 68ms/step - accuracy: 0.4473 - loss: 1.1792  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 73ms/step - accuracy: 0.4475 - loss: 1.1784
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 24ms/step - accuracy: 0.4655 - loss: 1.2420 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4527 - loss: 1.2513[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 87ms/step - accuracy: 0.4048 - loss: 1.2268 - val_accuracy: 0.4258 - val_loss: 1.1699[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m55/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 61ms/step - accuracy: 0.3521 - loss: 1.3651
[1m56/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 60ms/step - accuracy: 0.3521 - loss: 1.3650
[1m57/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 60ms/step - accuracy: 0.3521 - loss: 1.3649
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m135/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 27ms/step - accuracy: 0.3112 - loss: 1.5704
[1m137/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 27ms/step - accuracy: 0.3110 - loss: 1.5704
[1m139/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 27ms/step - accuracy: 0.3109 - loss: 1.5704
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 29ms/step - accuracy: 0.3579 - loss: 1.3156 - val_accuracy: 0.3689 - val_loss: 1.2830[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 86ms/step - accuracy: 0.1875 - loss: 2.0336[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m71/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 59ms/step - accuracy: 0.3517 - loss: 1.3636[32m [repeated 84x across cluster][0m
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.2733 - loss: 1.7831[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.2903 - loss: 2.4445
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.2896 - loss: 2.4396[32m [repeated 297x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 90ms/step - accuracy: 0.3750 - loss: 1.2747
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 24ms/step - accuracy: 0.3685 - loss: 1.2699 [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m30/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 66ms/step - accuracy: 0.4281 - loss: 1.1976
[1m31/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 66ms/step - accuracy: 0.4274 - loss: 1.1980[32m [repeated 73x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 38ms/step - accuracy: 0.3496 - loss: 1.3138[32m [repeated 293x across cluster][0m
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3007 - loss: 1.7515
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3066 - loss: 1.7504
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3100 - loss: 1.7567
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3128 - loss: 1.7579
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3154 - loss: 1.7580
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3168 - loss: 1.7571
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3186 - loss: 1.7573
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3201 - loss: 1.7574
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 24/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3210 - loss: 1.7578
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3231 - loss: 1.7570
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.2561 - loss: 1.8036 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 33ms/step - accuracy: 0.2562 - loss: 1.8137
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.2951 - loss: 2.4909 
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 66ms/step - accuracy: 0.3711 - loss: 1.2947 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 55ms/step - accuracy: 0.3542 - loss: 1.3154 
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 66/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.4116 - loss: 1.2498
[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.4111 - loss: 1.2500
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.4108 - loss: 1.2502
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 8/116[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.2717 - loss: 2.1225  
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.3478 - loss: 1.3151 
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.3482 - loss: 1.3130[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 67ms/step - accuracy: 0.3515 - loss: 1.3621 - val_accuracy: 0.3821 - val_loss: 1.2835[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3200 - loss: 1.4268   
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3204 - loss: 1.4206
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 26ms/step - accuracy: 0.4028 - loss: 1.2489 - val_accuracy: 0.4090 - val_loss: 1.1813[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 84ms/step - accuracy: 0.2500 - loss: 1.7903[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m12/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 60ms/step - accuracy: 0.4375 - loss: 1.1748[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - accuracy: 0.3315 - loss: 2.2962[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m206/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 28ms/step - accuracy: 0.3031 - loss: 2.3505
[1m208/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 28ms/step - accuracy: 0.3031 - loss: 2.3504[32m [repeated 321x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 143ms/step - accuracy: 0.4219 - loss: 1.2425
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 81ms/step - accuracy: 0.4277 - loss: 1.2116  [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m64/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 55ms/step - accuracy: 0.3509 - loss: 1.3295
[1m65/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 55ms/step - accuracy: 0.3510 - loss: 1.3295[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 55/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 33ms/step - accuracy: 0.3563 - loss: 1.3060[32m [repeated 227x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 47ms/step - accuracy: 0.3301 - loss: 2.2951
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 47ms/step - accuracy: 0.3286 - loss: 2.2947[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.2865 - loss: 1.5414  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2814 - loss: 1.5410
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.4465 - loss: 1.2422
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4376 - loss: 1.2414
[1m 10/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4292 - loss: 1.2443[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 9/116[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 51ms/step - accuracy: 0.3711 - loss: 1.4372  
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 67ms/step - accuracy: 0.3521 - loss: 1.3291 - val_accuracy: 0.3702 - val_loss: 1.2849[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.2700 - loss: 1.6663  
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.2705 - loss: 1.6654
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 30ms/step - accuracy: 0.4070 - loss: 1.2284 - val_accuracy: 0.4281 - val_loss: 1.1702[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 136ms/step - accuracy: 0.2812 - loss: 1.6500[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m77/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 72ms/step - accuracy: 0.4267 - loss: 1.1764[32m [repeated 72x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 49ms/step - accuracy: 0.2945 - loss: 2.3455[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m139/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 26ms/step - accuracy: 0.3003 - loss: 1.5007
[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 26ms/step - accuracy: 0.3004 - loss: 1.5004[32m [repeated 309x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 92ms/step - accuracy: 0.4219 - loss: 1.1778
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3976 - loss: 1.1891 [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m46/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.3679 - loss: 1.3175
[1m47/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.3679 - loss: 1.3175[32m [repeated 83x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m140/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 63ms/step - accuracy: 0.3669 - loss: 1.3157[32m [repeated 306x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m120/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m10s[0m 49ms/step - accuracy: 0.2941 - loss: 2.3460
[1m122/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m10s[0m 49ms/step - accuracy: 0.2940 - loss: 2.3462[32m [repeated 21x across cluster][0m
Trial status: 20 RUNNING
Current time: 2025-11-05 11:11:15. Total running time: 1min 0s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING            4   adam            relu                                   64                128                  5          0.00168541         137 │
│ trial_9f7c8    RUNNING            2   adam            tanh                                   32                 32                  5          0.00357251         149 │
│ trial_9f7c8    RUNNING            2   adam            relu                                   32                128                  3          1.05278e-05         72 │
│ trial_9f7c8    RUNNING            4   rmsprop         tanh                                  128                 32                  3          0.000165933        107 │
│ trial_9f7c8    RUNNING            3   rmsprop         relu                                   32                 64                  3          0.000159733         73 │
│ trial_9f7c8    RUNNING            2   rmsprop         tanh                                   32                128                  5          0.000146441        116 │
│ trial_9f7c8    RUNNING            4   adam            relu                                   64                 64                  3          0.000386417        121 │
│ trial_9f7c8    RUNNING            2   adam            relu                                   64                 32                  5          1.56689e-05        116 │
│ trial_9f7c8    RUNNING            3   rmsprop         tanh                                   32                128                  3          0.00149202          59 │
│ trial_9f7c8    RUNNING            4   adam            tanh                                   64                 32                  5          0.00247751         130 │
│ trial_9f7c8    RUNNING            2   rmsprop         relu                                   64                 32                  3          0.000917269        145 │
│ trial_9f7c8    RUNNING            4   adam            tanh                                   32                128                  3          6.9214e-05          65 │
│ trial_9f7c8    RUNNING            3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111 │
│ trial_9f7c8    RUNNING            2   adam            relu                                   64                 32                  5          0.000113503        140 │
│ trial_9f7c8    RUNNING            2   rmsprop         relu                                  128                128                  5          7.16419e-05         66 │
│ trial_9f7c8    RUNNING            2   rmsprop         relu                                   64                 32                  5          0.0020272           93 │
│ trial_9f7c8    RUNNING            2   rmsprop         tanh                                   32                128                  3          0.00213202          64 │
│ trial_9f7c8    RUNNING            2   rmsprop         tanh                                   64                 32                  3          0.000203535         58 │
│ trial_9f7c8    RUNNING            3   adam            tanh                                  128                128                  3          0.000931045         72 │
│ trial_9f7c8    RUNNING            4   rmsprop         relu                                  128                128                  3          0.00205798         108 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.2865 - loss: 2.3573 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3028 - loss: 2.2797
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3932 - loss: 1.2480 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3873 - loss: 1.2400
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 27ms/step - accuracy: 0.2975 - loss: 1.3525  
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.3141 - loss: 1.3452[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 11/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.3860 - loss: 1.2331
[1m 13/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.3873 - loss: 1.2309
[1m 15/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.3879 - loss: 1.2302[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m Epoch 9/111[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 71ms/step - accuracy: 0.3671 - loss: 1.3170 - val_accuracy: 0.3670 - val_loss: 1.2816[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 37ms/step - accuracy: 0.3203 - loss: 1.3745  
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 42ms/step - accuracy: 0.3213 - loss: 1.3714[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 50ms/step - accuracy: 0.3276 - loss: 1.4194 - val_accuracy: 0.3755 - val_loss: 1.2868[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 117ms/step - accuracy: 0.3125 - loss: 1.3780[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 63ms/step - accuracy: 0.3455 - loss: 1.4215[32m [repeated 100x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 22/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 67ms/step - accuracy: 0.3560 - loss: 1.3082 [32m [repeated 27x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 30ms/step - accuracy: 0.3199 - loss: 2.2406
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 30ms/step - accuracy: 0.3198 - loss: 2.2407[32m [repeated 294x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 119ms/step - accuracy: 0.3672 - loss: 1.3771
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 71ms/step - accuracy: 0.3516 - loss: 1.4109 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m58/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 67ms/step - accuracy: 0.4276 - loss: 1.1606
[1m59/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 66ms/step - accuracy: 0.4276 - loss: 1.1607[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.2930 - loss: 2.1273[32m [repeated 300x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 13/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 71ms/step - accuracy: 0.3588 - loss: 1.3110
[1m 14/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 70ms/step - accuracy: 0.3587 - loss: 1.3102[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 74ms/step - accuracy: 0.3750 - loss: 1.2985 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 72ms/step - accuracy: 0.3646 - loss: 1.3147
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5140 - loss: 1.1517 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4995 - loss: 1.1627
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3655 - loss: 1.2441  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3811 - loss: 1.2481[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m224/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m5s[0m 48ms/step - accuracy: 0.2907 - loss: 2.3446
[1m225/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m5s[0m 48ms/step - accuracy: 0.2907 - loss: 2.3445
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m5s[0m 48ms/step - accuracy: 0.2907 - loss: 2.3444[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 194ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m 8/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step  
[1m15/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[1m19/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m27/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 8ms/step
[1m31/49[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 55ms/step - accuracy: 0.3008 - loss: 1.6541 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 43ms/step - accuracy: 0.2977 - loss: 1.6276
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m36/49[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 10ms/step
[1m40/49[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118300)[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=3118300)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118313)[0m 2025-11-05 11:10:19.012402: 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=3118313)[0m 2025-11-05 11:10:19.033958: 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=3118313)[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=3118313)[0m E0000 00:00:1762337419.066694 3119573 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=3118313)[0m E0000 00:00:1762337419.074669 3119573 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=3118313)[0m W0000 00:00:1762337419.094843 3119573 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=3118313)[0m 2025-11-05 11:10:19.100876: 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=3118313)[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=3118313)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m   warnings.warn([32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 2025-11-05 11:10:22.447936: 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=3118313)[0m 2025-11-05 11:10:22.448005: 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=3118313)[0m 2025-11-05 11:10:22.448021: 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=3118313)[0m 2025-11-05 11:10:22.448026: 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=3118313)[0m 2025-11-05 11:10:22.448031: 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=3118313)[0m 2025-11-05 11:10:22.448035: 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=3118313)[0m 2025-11-05 11:10:22.448361: 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=3118313)[0m 2025-11-05 11:10:22.448421: 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=3118313)[0m 2025-11-05 11:10:22.448426: 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=3118300)[0m 
[1m44/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 11ms/step
[1m48/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118311)[0m Epoch 5/59[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 53ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m 6/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m11/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m16/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m20/96[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m25/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m31/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m36/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m42/96[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m45/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m49/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 42ms/step - accuracy: 0.2144 - loss: 2.2431  
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m63/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 11ms/step
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m73/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m78/96[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 11ms/step
[1m82/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m87/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 11ms/step
[1m94/96[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 11ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:11:23. Total running time: 1min 8s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             65.9468 │
│ time_total_s                 65.9468 │
│ training_iteration                 1 │
│ val_accuracy                 0.38371 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:11:24. Total running time: 1min 8s
[36m(train_cnn_ray_tune pid=3118300)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 78ms/step - accuracy: 0.4292 - loss: 1.1611 - val_accuracy: 0.4557 - val_loss: 1.1161[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m56/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 43ms/step - accuracy: 0.2880 - loss: 1.6062
[1m57/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 43ms/step - accuracy: 0.2880 - loss: 1.6061
[1m58/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 43ms/step - accuracy: 0.2881 - loss: 1.6060
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 30ms/step - accuracy: 0.4229 - loss: 1.1985 - val_accuracy: 0.4698 - val_loss: 1.1354[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 105ms/step - accuracy: 0.3125 - loss: 2.2408[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m68/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 44ms/step - accuracy: 0.2886 - loss: 1.6054[32m [repeated 90x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 54/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3769 - loss: 1.2864[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m 53/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.3680 - loss: 1.3488
[1m 57/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.3667 - loss: 1.3497[32m [repeated 309x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 111ms/step - accuracy: 0.3281 - loss: 1.4371
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 56ms/step - accuracy: 0.3398 - loss: 1.4339 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m11/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 66ms/step - accuracy: 0.3415 - loss: 1.3184
[1m12/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 66ms/step - accuracy: 0.3431 - loss: 1.3184[32m [repeated 65x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m308/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 30ms/step - accuracy: 0.3160 - loss: 2.2521[32m [repeated 268x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 63/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3747 - loss: 1.2861
[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 37ms/step - accuracy: 0.3743 - loss: 1.2860 [32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3268 - loss: 2.0556  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3207 - loss: 2.0580
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.3573 - loss: 1.3560
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.3572 - loss: 1.3561
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.3569 - loss: 1.3563[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 29ms/step - accuracy: 0.2695 - loss: 1.5638 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 30ms/step - accuracy: 0.2704 - loss: 1.5842
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 4/65[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 56ms/step - accuracy: 0.3867 - loss: 1.2816  
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 62ms/step - accuracy: 0.3741 - loss: 1.3083
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 67ms/step - accuracy: 0.2500 - loss: 2.2882 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 74ms/step - accuracy: 0.2535 - loss: 2.2278
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 53ms/step - accuracy: 0.2892 - loss: 1.6042 - val_accuracy: 0.3702 - val_loss: 1.3573[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m69/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 67ms/step - accuracy: 0.3595 - loss: 1.3118
[1m70/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 67ms/step - accuracy: 0.3596 - loss: 1.3119
[1m71/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 67ms/step - accuracy: 0.3597 - loss: 1.3119
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 38ms/step - accuracy: 0.2764 - loss: 2.0697 - val_accuracy: 0.3837 - val_loss: 1.3141[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 78ms/step - accuracy: 0.2656 - loss: 2.1804[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m17/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 45ms/step - accuracy: 0.3537 - loss: 1.4146[32m [repeated 89x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 58/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m12s[0m 46ms/step - accuracy: 0.2812 - loss: 2.2833[32m [repeated 22x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 81/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m7s[0m 30ms/step - accuracy: 0.3179 - loss: 2.3299
[1m 83/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m7s[0m 30ms/step - accuracy: 0.3178 - loss: 2.3292[32m [repeated 306x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 140ms/step - accuracy: 0.3672 - loss: 1.1798
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 64ms/step - accuracy: 0.3906 - loss: 1.1609  [32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.4351 - loss: 1.1389
[1m 6/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 62ms/step - accuracy: 0.4387 - loss: 1.1376[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 22/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4474 - loss: 1.1775[32m [repeated 274x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 52/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m13s[0m 47ms/step - accuracy: 0.2796 - loss: 2.2845
[1m 54/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m12s[0m 46ms/step - accuracy: 0.2801 - loss: 2.2843[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 66ms/step - accuracy: 0.3535 - loss: 1.3347 
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 43ms/step - accuracy: 0.2995 - loss: 1.4689 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 43ms/step - accuracy: 0.3023 - loss: 1.4818
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 69ms/step - accuracy: 0.3398 - loss: 1.2531 
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3750 - loss: 1.2020 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.4022 - loss: 1.1796
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m235/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m3s[0m 38ms/step - accuracy: 0.3667 - loss: 1.2840
[1m237/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m3s[0m 37ms/step - accuracy: 0.3666 - loss: 1.2840
[1m238/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m3s[0m 37ms/step - accuracy: 0.3666 - loss: 1.2840[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m Epoch 8/149[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3242 - loss: 2.0425  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3170 - loss: 2.0445
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 50ms/step - accuracy: 0.3501 - loss: 1.4232 - val_accuracy: 0.3850 - val_loss: 1.2214[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 36ms/step - accuracy: 0.3155 - loss: 1.6506 - val_accuracy: 0.3791 - val_loss: 1.2814[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 99ms/step - accuracy: 0.4688 - loss: 1.1201[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m35/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 46ms/step - accuracy: 0.3623 - loss: 1.3846[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3941 - loss: 1.3041 [32m [repeated 22x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m 59/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.3692 - loss: 1.3249
[1m 61/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.3689 - loss: 1.3251[32m [repeated 284x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 127ms/step - accuracy: 0.3359 - loss: 1.4364
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 35ms/step - accuracy: 0.3247 - loss: 1.4552  [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m72/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 68ms/step - accuracy: 0.3709 - loss: 1.3033
[1m73/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 67ms/step - accuracy: 0.3709 - loss: 1.3033[32m [repeated 84x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m239/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.3164 - loss: 2.3155[32m [repeated 307x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m112/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 46ms/step - accuracy: 0.2884 - loss: 2.2683
[1m114/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 46ms/step - accuracy: 0.2885 - loss: 2.2679[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 53ms/step - accuracy: 0.3559 - loss: 1.3022  
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.4097 - loss: 1.3350 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.3877 - loss: 1.4025
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 59/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.4246 - loss: 1.1944
[1m 62/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.4249 - loss: 1.1942
[1m 65/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.4251 - loss: 1.1940
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m178/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 32ms/step - accuracy: 0.3569 - loss: 1.5461
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 32ms/step - accuracy: 0.3567 - loss: 1.5461
[1m182/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 32ms/step - accuracy: 0.3566 - loss: 1.5460
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 57ms/step - accuracy: 0.3750 - loss: 1.2832 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 48ms/step - accuracy: 0.3919 - loss: 1.2650
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3785 - loss: 1.1598 
[36m(train_cnn_ray_tune pid=3118309)[0m Epoch 7/64[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.3661 - loss: 1.2329 
[1m 24/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3667 - loss: 1.2353
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m 26/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.3672 - loss: 1.2378 
[1m 28/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.3680 - loss: 1.2394
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 59ms/step - accuracy: 0.4023 - loss: 1.2976 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 57ms/step - accuracy: 0.4036 - loss: 1.2933
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 48ms/step - accuracy: 0.3005 - loss: 1.5247 - val_accuracy: 0.3709 - val_loss: 1.3465[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 36ms/step - accuracy: 0.3173 - loss: 2.3047 - val_accuracy: 0.3784 - val_loss: 1.3379[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 93ms/step - accuracy: 0.1562 - loss: 2.6295[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m44/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 49ms/step - accuracy: 0.3846 - loss: 1.3413[32m [repeated 95x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3067 - loss: 2.3313[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.3706 - loss: 1.3259
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.3704 - loss: 1.3261[32m [repeated 317x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 81ms/step - accuracy: 0.3281 - loss: 2.1167
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3320 - loss: 2.1010 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m57/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 64ms/step - accuracy: 0.3536 - loss: 1.3063
[1m59/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 63ms/step - accuracy: 0.3538 - loss: 1.3062[32m [repeated 65x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.3701 - loss: 1.3263[32m [repeated 289x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3092 - loss: 2.3178
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3108 - loss: 2.3076[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 50/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.4532 - loss: 1.1696
[1m 53/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.4530 - loss: 1.1694
[1m 56/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.4528 - loss: 1.1692[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 32ms/step - accuracy: 0.4193 - loss: 1.2349  
[36m(train_cnn_ray_tune pid=3118307)[0m Epoch 7/137[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.2526 - loss: 1.9810 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.2594 - loss: 1.9970[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 89ms/step - accuracy: 0.4579 - loss: 1.1293 
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 40ms/step - accuracy: 0.3542 - loss: 1.3115  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 37ms/step - accuracy: 0.3522 - loss: 1.3164
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 80ms/step - accuracy: 0.4770 - loss: 1.1170 - val_accuracy: 0.4409 - val_loss: 1.1285[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 51ms/step - accuracy: 0.2986 - loss: 2.3723 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 48ms/step - accuracy: 0.2845 - loss: 2.3686
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 51ms/step - accuracy: 0.2899 - loss: 2.2580 - val_accuracy: 0.3729 - val_loss: 1.8242[32m [repeated 11x across cluster][0m

Trial status: 19 RUNNING | 1 TERMINATED
Current time: 2025-11-05 11:11:45. Total running time: 1min 30s
Logical resource usage: 19.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            tanh                                   32                 32                  5          0.00357251         149                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         tanh                                  128                 32                  3          0.000165933        107                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  5          0.000146441        116                                              │
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                 64                  3          0.000386417        121                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   64                 32                  5          1.56689e-05        116                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.00149202          59                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   64                 32                  5          0.00247751         130                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  3          0.000917269        145                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                  128                128                  5          7.16419e-05         66                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.0020272           93                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00213202          64                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   64                 32                  3          0.000203535         58                                              │
│ trial_9f7c8    RUNNING              3   adam            tanh                                  128                128                  3          0.000931045         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         relu                                  128                128                  3          0.00205798         108                                              │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 86ms/step - accuracy: 0.3906 - loss: 1.2413[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m31/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 71ms/step - accuracy: 0.4706 - loss: 1.1264[32m [repeated 77x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.4219 - loss: 1.2111 [32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4451 - loss: 1.1829
[1m 11/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.4485 - loss: 1.1798[32m [repeated 320x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 77ms/step - accuracy: 0.3125 - loss: 1.7734
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.3070 - loss: 1.9852 [32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m49/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 57ms/step - accuracy: 0.3592 - loss: 1.3115
[1m50/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 57ms/step - accuracy: 0.3594 - loss: 1.3113[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m4s[0m 31ms/step - accuracy: 0.3206 - loss: 2.2017[32m [repeated 229x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - accuracy: 0.2800 - loss: 2.3493
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 47ms/step - accuracy: 0.2798 - loss: 2.3469[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 57ms/step - accuracy: 0.3281 - loss: 1.4250 
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 24/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.3205 - loss: 1.4577
[1m 26/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 29ms/step - accuracy: 0.3202 - loss: 1.4586
[1m 28/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 29ms/step - accuracy: 0.3204 - loss: 1.4588[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m Epoch 19/66[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4297 - loss: 1.2292 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4203 - loss: 1.2334[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 64ms/step - accuracy: 0.3633 - loss: 1.2979  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 64ms/step - accuracy: 0.3628 - loss: 1.2970
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 215ms/step
[1m 6/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step  
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m13/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step 
[1m18/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m76/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 74ms/step - accuracy: 0.4722 - loss: 1.1201
[1m77/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 74ms/step - accuracy: 0.4722 - loss: 1.1200
[1m78/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 74ms/step - accuracy: 0.4722 - loss: 1.1199
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m22/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m27/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m33/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m39/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 10ms/step
[1m46/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 68ms/step - accuracy: 0.3624 - loss: 1.3068 - val_accuracy: 0.3683 - val_loss: 1.2701[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 48ms/step
[1m 9/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step 
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m15/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[1m21/96[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m26/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[1m31/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m36/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118301)[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=3118301)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 36ms/step - accuracy: 0.3714 - loss: 1.2641 - val_accuracy: 0.3752 - val_loss: 1.2371[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m42/96[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m49/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m55/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m62/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 9ms/step 
[1m74/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m80/96[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 9ms/step
[1m85/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step
[1m90/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118301)[0m 
[1m95/96[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 10ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 143ms/step - accuracy: 0.4531 - loss: 1.1959[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 71ms/step - accuracy: 0.4455 - loss: 1.1899[32m [repeated 96x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m110/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 47ms/step - accuracy: 0.2998 - loss: 2.2484[32m [repeated 27x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4538 - loss: 1.1163
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4558 - loss: 1.1204[32m [repeated 290x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 95ms/step - accuracy: 0.4375 - loss: 1.1159
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4492 - loss: 1.1186 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m37/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 57ms/step - accuracy: 0.3625 - loss: 1.3043
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 56ms/step - accuracy: 0.3625 - loss: 1.3043[32m [repeated 64x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m131/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 46ms/step - accuracy: 0.3004 - loss: 2.2414[32m [repeated 281x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m115/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m10s[0m 46ms/step - accuracy: 0.3001 - loss: 2.2463
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m9s[0m 46ms/step - accuracy: 0.3001 - loss: 2.2456 [32m [repeated 21x across cluster][0m

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:11:50. Total running time: 1min 35s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             92.6448 │
│ time_total_s                 92.6448 │
│ training_iteration                 1 │
│ val_accuracy                 0.37451 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:11:50. Total running time: 1min 35s
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 57ms/step - accuracy: 0.3594 - loss: 1.2758 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 55ms/step - accuracy: 0.3707 - loss: 1.2700
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.4536 - loss: 1.1478
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.4534 - loss: 1.1481
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.4533 - loss: 1.1484
[36m(train_cnn_ray_tune pid=3118315)[0m Epoch 8/72[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.4427 - loss: 1.2660 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.4247 - loss: 1.2847[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m44/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 71ms/step - accuracy: 0.4715 - loss: 1.1213
[1m45/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 71ms/step - accuracy: 0.4718 - loss: 1.1209
[1m46/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 71ms/step - accuracy: 0.4721 - loss: 1.1204
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 68ms/step - accuracy: 0.3630 - loss: 1.3028 - val_accuracy: 0.3712 - val_loss: 1.2675[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 46ms/step - accuracy: 0.3828 - loss: 1.2462  
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 49ms/step - accuracy: 0.3848 - loss: 1.2417
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 28ms/step - accuracy: 0.4491 - loss: 1.1552 - val_accuracy: 0.4708 - val_loss: 1.1228[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 88ms/step - accuracy: 0.3750 - loss: 1.3165[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 45ms/step - accuracy: 0.3661 - loss: 1.3232[32m [repeated 83x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 29/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3397 - loss: 2.1480[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m134/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.3390 - loss: 2.1447
[1m136/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 29ms/step - accuracy: 0.3389 - loss: 2.1444[32m [repeated 314x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 72ms/step - accuracy: 0.4688 - loss: 1.1875
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.4788 - loss: 1.1429 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m81/84[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 66ms/step - accuracy: 0.4790 - loss: 1.1099
[1m82/84[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 66ms/step - accuracy: 0.4791 - loss: 1.1098[32m [repeated 65x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 27/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.4497 - loss: 1.1517[32m [repeated 212x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3423 - loss: 2.1471
[1m 41/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3425 - loss: 2.1470[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3440 - loss: 1.3981
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3472 - loss: 1.3900
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.3471 - loss: 1.3865 
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m16/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m4s[0m 59ms/step - accuracy: 0.5017 - loss: 1.1012
[1m17/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 59ms/step - accuracy: 0.5011 - loss: 1.1007
[1m18/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 59ms/step - accuracy: 0.5006 - loss: 1.1002
[36m(train_cnn_ray_tune pid=3118322)[0m Epoch 20/145[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.3455 - loss: 1.4812 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.3414 - loss: 1.4359[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 36ms/step - accuracy: 0.3069 - loss: 1.4324 
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 40ms/step - accuracy: 0.3250 - loss: 1.4154 - val_accuracy: 0.3699 - val_loss: 1.3193[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.4427 - loss: 1.1839  
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.4512 - loss: 1.2069
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 26ms/step - accuracy: 0.3738 - loss: 1.2838 - val_accuracy: 0.3712 - val_loss: 1.2541[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 85ms/step - accuracy: 0.4375 - loss: 1.2163[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m60/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 37ms/step - accuracy: 0.3310 - loss: 1.3989[32m [repeated 79x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 54/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.4710 - loss: 1.1243
[1m 57/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step - accuracy: 0.4708 - loss: 1.1248[32m [repeated 328x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11:51[0m 4s/step - accuracy: 0.3906 - loss: 1.8068
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3628 - loss: 1.7711 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m33/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 61ms/step - accuracy: 0.3533 - loss: 1.3022
[1m34/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 61ms/step - accuracy: 0.3536 - loss: 1.3023[32m [repeated 62x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 28ms/step - accuracy: 0.3320 - loss: 2.1535[32m [repeated 223x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3563 - loss: 1.3017
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3562 - loss: 1.3018
[1m 42/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3564 - loss: 1.3017
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 60ms/step - accuracy: 0.3203 - loss: 1.7840 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 45ms/step - accuracy: 0.2936 - loss: 1.9166
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 44ms/step - accuracy: 0.2905 - loss: 1.9658
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - accuracy: 0.2905 - loss: 1.9774
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  8/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 49ms/step - accuracy: 0.2884 - loss: 1.9899
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 51ms/step - accuracy: 0.2880 - loss: 2.0001
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 52ms/step - accuracy: 0.2879 - loss: 2.0083
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 52ms/step - accuracy: 0.2889 - loss: 2.0136
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 51ms/step - accuracy: 0.2881 - loss: 2.0298
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 50ms/step - accuracy: 0.2882 - loss: 2.0430
[1m 16/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 51ms/step - accuracy: 0.2881 - loss: 2.0467
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 49ms/step - accuracy: 0.2875 - loss: 2.0534
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 47ms/step - accuracy: 0.2871 - loss: 2.0608
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 48ms/step - accuracy: 0.2871 - loss: 2.0645
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 47ms/step - accuracy: 0.2874 - loss: 2.0706
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 25/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 45ms/step - accuracy: 0.2879 - loss: 2.0746
[1m 26/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 45ms/step - accuracy: 0.2881 - loss: 2.0758
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 28/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 45ms/step - accuracy: 0.2885 - loss: 2.0778
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 44ms/step - accuracy: 0.2886 - loss: 2.0796
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 44ms/step - accuracy: 0.2890 - loss: 2.0812
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 44ms/step - accuracy: 0.2893 - loss: 2.0830
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 44ms/step - accuracy: 0.2895 - loss: 2.0841
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 39ms/step - accuracy: 0.3121 - loss: 1.4199 
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m59/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 61ms/step - accuracy: 0.3584 - loss: 1.3030
[1m60/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 61ms/step - accuracy: 0.3585 - loss: 1.3030
[1m61/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 61ms/step - accuracy: 0.3587 - loss: 1.3029[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 44ms/step - accuracy: 0.2898 - loss: 2.0860
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 43ms/step - accuracy: 0.2903 - loss: 2.0872
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3481 - loss: 1.3273  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3623 - loss: 1.3107
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 41/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 43ms/step - accuracy: 0.2907 - loss: 2.0887
[1m 43/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 43ms/step - accuracy: 0.2911 - loss: 2.0901
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 47/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 43ms/step - accuracy: 0.2915 - loss: 2.0932
[1m 49/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 43ms/step - accuracy: 0.2917 - loss: 2.0946
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 19/116[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.3867 - loss: 1.2755 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.3688 - loss: 1.2968[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 51/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m11s[0m 42ms/step - accuracy: 0.2919 - loss: 2.0954
[1m 53/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m11s[0m 42ms/step - accuracy: 0.2922 - loss: 2.0960
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 62/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m11s[0m 43ms/step - accuracy: 0.2929 - loss: 2.0964
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m11s[0m 43ms/step - accuracy: 0.2931 - loss: 2.0967
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m11s[0m 42ms/step - accuracy: 0.2934 - loss: 2.0966
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m11s[0m 42ms/step - accuracy: 0.2936 - loss: 2.0962
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 42ms/step - accuracy: 0.2938 - loss: 2.0960
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 42ms/step - accuracy: 0.2940 - loss: 2.0956
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 68ms/step - accuracy: 0.3609 - loss: 1.3017 - val_accuracy: 0.3890 - val_loss: 1.2657[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 36ms/step - accuracy: 0.2903 - loss: 1.8634 - val_accuracy: 0.3844 - val_loss: 1.2737[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 60ms/step - accuracy: 0.4453 - loss: 1.1576  
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 127ms/step - accuracy: 0.4688 - loss: 1.1357[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m22/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 63ms/step - accuracy: 0.3686 - loss: 1.2997[32m [repeated 95x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 22ms/step - accuracy: 0.4517 - loss: 1.1466
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4517 - loss: 1.1466[32m [repeated 303x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 116ms/step - accuracy: 0.3438 - loss: 1.2696
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 36ms/step - accuracy: 0.3485 - loss: 1.2919 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m32/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 47ms/step - accuracy: 0.3810 - loss: 1.2966
[1m34/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 47ms/step - accuracy: 0.3818 - loss: 1.2964[32m [repeated 65x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m6s[0m 29ms/step - accuracy: 0.3068 - loss: 2.1455[32m [repeated 218x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 18/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4801 - loss: 1.1218
[1m 21/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4748 - loss: 1.1249
[1m 24/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.4705 - loss: 1.1273[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 65ms/step - accuracy: 0.4024 - loss: 1.2028
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 64ms/step - accuracy: 0.3985 - loss: 1.2074[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 53/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 36ms/step - accuracy: 0.3863 - loss: 1.2660 [32m [repeated 31x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m47/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.3649 - loss: 1.2985
[1m48/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.3648 - loss: 1.2984
[1m49/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 62ms/step - accuracy: 0.3646 - loss: 1.2984
[36m(train_cnn_ray_tune pid=3118318)[0m Epoch 9/73[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4544 - loss: 1.1974 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4635 - loss: 1.1820[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m61/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 61ms/step - accuracy: 0.3638 - loss: 1.2975
[1m62/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 61ms/step - accuracy: 0.3637 - loss: 1.2975
[1m63/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 61ms/step - accuracy: 0.3637 - loss: 1.2974
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.2956 - loss: 1.4125  
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.3066 - loss: 1.4212
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 107ms/step - accuracy: 0.4453 - loss: 1.2868
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 50ms/step - accuracy: 0.4199 - loss: 1.3085 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 50ms/step - accuracy: 0.4093 - loss: 1.3124
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 74ms/step - accuracy: 0.5105 - loss: 1.0681 - val_accuracy: 0.4573 - val_loss: 1.0836[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 43ms/step - accuracy: 0.3654 - loss: 1.2955 - val_accuracy: 0.3735 - val_loss: 1.2707[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3945 - loss: 1.4518  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 126ms/step - accuracy: 0.5469 - loss: 0.9697[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m24/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 71ms/step - accuracy: 0.5217 - loss: 1.0380[32m [repeated 62x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 29ms/step - accuracy: 0.3147 - loss: 2.1389
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m1s[0m 29ms/step - accuracy: 0.3147 - loss: 2.1387[32m [repeated 315x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 104ms/step - accuracy: 0.2344 - loss: 1.8552
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.2517 - loss: 1.8190  [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 9/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.3826 - loss: 1.2877
[1m10/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.3810 - loss: 1.2886[32m [repeated 79x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 38ms/step - accuracy: 0.3705 - loss: 1.2945[32m [repeated 236x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 25ms/step - accuracy: 0.4736 - loss: 1.1321
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 25ms/step - accuracy: 0.4735 - loss: 1.1323
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 25ms/step - accuracy: 0.4733 - loss: 1.1325[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 39ms/step - accuracy: 0.4121 - loss: 1.2094
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.4034 - loss: 1.2205
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  8/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 37ms/step - accuracy: 0.3964 - loss: 1.2260
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3926 - loss: 1.2304
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m 12/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3894 - loss: 1.2354
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3885 - loss: 1.2373
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.4219 - loss: 1.2015[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m 16/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3880 - loss: 1.2389
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3869 - loss: 1.2406
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 21/116[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.4977 - loss: 1.0705 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4863 - loss: 1.0809[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 32ms/step - accuracy: 0.3194 - loss: 1.8872  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3224 - loss: 1.8807
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 37ms/step - accuracy: 0.3811 - loss: 1.2982 
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 55ms/step - accuracy: 0.4974 - loss: 1.2691  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 53ms/step - accuracy: 0.4669 - loss: 1.2845
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 44ms/step - accuracy: 0.3391 - loss: 1.3749 - val_accuracy: 0.3817 - val_loss: 1.3127[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m8s[0m 45ms/step - accuracy: 0.3710 - loss: 1.2949 - val_accuracy: 0.3857 - val_loss: 1.2693[32m [repeated 7x across cluster][0m

Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-05 11:12:15. Total running time: 2min 0s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            tanh                                   32                 32                  5          0.00357251         149                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         tanh                                  128                 32                  3          0.000165933        107                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  5          0.000146441        116                                              │
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                 64                  3          0.000386417        121                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   64                 32                  5          1.56689e-05        116                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.00149202          59                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   64                 32                  5          0.00247751         130                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  3          0.000917269        145                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                  128                128                  5          7.16419e-05         66                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.0020272           93                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00213202          64                                              │
│ trial_9f7c8    RUNNING              3   adam            tanh                                  128                128                  3          0.000931045         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         relu                                  128                128                  3          0.00205798         108                                              │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 111ms/step - accuracy: 0.5469 - loss: 1.2488[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m55/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 45ms/step - accuracy: 0.3799 - loss: 1.3103[32m [repeated 86x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m7s[0m 29ms/step - accuracy: 0.3596 - loss: 2.0132
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m7s[0m 28ms/step - accuracy: 0.3588 - loss: 2.0145[32m [repeated 306x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 128ms/step - accuracy: 0.5625 - loss: 1.0216
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 68ms/step - accuracy: 0.5605 - loss: 1.0171  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 62ms/step - accuracy: 0.5586 - loss: 1.0051
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 59ms/step - accuracy: 0.5562 - loss: 1.0013[32m [repeated 75x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 25ms/step - accuracy: 0.3109 - loss: 1.9071[32m [repeated 293x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3111 - loss: 1.9082
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3111 - loss: 1.9081
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.3111 - loss: 1.9079[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3854 - loss: 2.0130
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.3866 - loss: 2.0026 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m18/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 56ms/step - accuracy: 0.5377 - loss: 1.0302
[1m19/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 55ms/step - accuracy: 0.5371 - loss: 1.0306
[1m20/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 55ms/step - accuracy: 0.5368 - loss: 1.0310
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 37ms/step - accuracy: 0.3698 - loss: 2.1212[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m Epoch 24/93[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.2535 - loss: 2.3334 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.2711 - loss: 2.2498[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 36ms/step - accuracy: 0.3568 - loss: 1.1928  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 40ms/step - accuracy: 0.3675 - loss: 1.1967
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 48ms/step - accuracy: 0.3810 - loss: 1.3047 - val_accuracy: 0.3837 - val_loss: 1.1961[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 35ms/step - accuracy: 0.3510 - loss: 1.4075 - val_accuracy: 0.3867 - val_loss: 1.2564[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 82ms/step - accuracy: 0.4688 - loss: 1.1568[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 8/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 53ms/step - accuracy: 0.3755 - loss: 1.3124[32m [repeated 63x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 22ms/step - accuracy: 0.4800 - loss: 1.1141
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 22ms/step - accuracy: 0.4801 - loss: 1.1141[32m [repeated 291x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6:20[0m 5s/step - accuracy: 0.3516 - loss: 1.3707
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 68ms/step - accuracy: 0.3789 - loss: 1.3413[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.3802 - loss: 1.3273
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 53ms/step - accuracy: 0.3795 - loss: 1.3179[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 27ms/step - accuracy: 0.3377 - loss: 2.0767[32m [repeated 175x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4787 - loss: 1.1137
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4787 - loss: 1.1139
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4787 - loss: 1.1140[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 63/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 40ms/step - accuracy: 0.2967 - loss: 2.0371
[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 40ms/step - accuracy: 0.2969 - loss: 2.0352[32m [repeated 18x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 52ms/step - accuracy: 0.4258 - loss: 1.2072 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 58ms/step - accuracy: 0.4149 - loss: 1.2246
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.3672 - loss: 1.3914 [32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3906 - loss: 1.2924 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3849 - loss: 1.2967 
[36m(train_cnn_ray_tune pid=3118312)[0m Epoch 25/93[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.2529 - loss: 2.0285 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2697 - loss: 1.9800[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 40ms/step - accuracy: 0.4141 - loss: 1.3293  
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 68ms/step - accuracy: 0.5311 - loss: 1.0307 - val_accuracy: 0.4589 - val_loss: 1.0611[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 28ms/step - accuracy: 0.4860 - loss: 1.1119 - val_accuracy: 0.5358 - val_loss: 1.0084[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 124ms/step - accuracy: 0.3359 - loss: 1.2675[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m69/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 65ms/step - accuracy: 0.5247 - loss: 1.0381[32m [repeated 96x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 45/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 28ms/step - accuracy: 0.3412 - loss: 1.9459
[1m 47/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 28ms/step - accuracy: 0.3411 - loss: 1.9486[32m [repeated 336x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 107ms/step - accuracy: 0.3906 - loss: 1.3038
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.3650 - loss: 1.3240 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m23/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.3552 - loss: 1.3389
[1m25/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.3554 - loss: 1.3390[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 28/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.4655 - loss: 1.1567[32m [repeated 243x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 62ms/step - accuracy: 0.3555 - loss: 1.2692  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 65ms/step - accuracy: 0.3655 - loss: 1.2700
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m150/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 24ms/step - accuracy: 0.3126 - loss: 1.8560
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 24ms/step - accuracy: 0.3125 - loss: 1.8561
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 24ms/step - accuracy: 0.3125 - loss: 1.8562[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 49ms/step - accuracy: 0.4015 - loss: 1.2597 
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3412 - loss: 1.9023
[1m 24/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.3412 - loss: 1.9090 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4339 - loss: 1.2105 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 58ms/step - accuracy: 0.3906 - loss: 1.1564  
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 66ms/step - accuracy: 0.3698 - loss: 1.1748
[36m(train_cnn_ray_tune pid=3118313)[0m Epoch 19/108[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.3472 - loss: 1.8563 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.3382 - loss: 1.8903
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 36ms/step - accuracy: 0.4236 - loss: 1.3048  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 27ms/step - accuracy: 0.5156 - loss: 1.2503 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.4625 - loss: 1.2886
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.4347 - loss: 1.3084
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 44ms/step - accuracy: 0.3540 - loss: 1.3443 - val_accuracy: 0.3693 - val_loss: 1.3074[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 27ms/step - accuracy: 0.4744 - loss: 1.1317 - val_accuracy: 0.5401 - val_loss: 1.0115[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 57ms/step - accuracy: 0.3724 - loss: 1.2641 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 55ms/step - accuracy: 0.3760 - loss: 1.2668
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 81ms/step - accuracy: 0.2812 - loss: 1.8542[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m50/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 62ms/step - accuracy: 0.5262 - loss: 1.0299[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 30ms/step - accuracy: 0.3261 - loss: 2.0297
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 30ms/step - accuracy: 0.3260 - loss: 2.0299[32m [repeated 295x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m25s[0m 78ms/step - accuracy: 0.3750 - loss: 1.2569
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3620 - loss: 1.2689 
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m54/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 38ms/step - accuracy: 0.3394 - loss: 1.3426
[1m56/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 38ms/step - accuracy: 0.3397 - loss: 1.3426[32m [repeated 76x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 29ms/step - accuracy: 0.3074 - loss: 1.7000[32m [repeated 293x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 43ms/step - accuracy: 0.3572 - loss: 1.3225  
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 38ms/step - accuracy: 0.3498 - loss: 1.3282
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 98ms/step - accuracy: 0.4219 - loss: 1.3250
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 32ms/step - accuracy: 0.4323 - loss: 1.2917 
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 29ms/step - accuracy: 0.3082 - loss: 1.6994
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 29ms/step - accuracy: 0.3081 - loss: 1.6995
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 29ms/step - accuracy: 0.3080 - loss: 1.6996[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 63ms/step - accuracy: 0.3645 - loss: 1.1790
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 63ms/step - accuracy: 0.3644 - loss: 1.1800[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 33ms/step - accuracy: 0.3438 - loss: 1.3059[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3290 - loss: 1.6141  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3265 - loss: 1.6163
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.3525 - loss: 1.3725 
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.3539 - loss: 1.3706
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m27/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 57ms/step - accuracy: 0.3670 - loss: 1.2815
[1m28/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 57ms/step - accuracy: 0.3672 - loss: 1.2812
[1m29/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 57ms/step - accuracy: 0.3673 - loss: 1.2810
[36m(train_cnn_ray_tune pid=3118309)[0m Epoch 12/64[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 50ms/step - accuracy: 0.3472 - loss: 2.0862 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 46ms/step - accuracy: 0.3383 - loss: 2.0738
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 52ms/step - accuracy: 0.3933 - loss: 1.2607 - val_accuracy: 0.3811 - val_loss: 1.1895[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 26ms/step - accuracy: 0.3523 - loss: 1.2826 - val_accuracy: 0.3719 - val_loss: 1.2409[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 84ms/step - accuracy: 0.3125 - loss: 1.7342[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m79/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 57ms/step - accuracy: 0.3686 - loss: 1.2824[32m [repeated 67x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 29ms/step - accuracy: 0.2839 - loss: 1.7154
[1m 92/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 30ms/step - accuracy: 0.2840 - loss: 1.7154[32m [repeated 287x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m30/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 62ms/step - accuracy: 0.5536 - loss: 0.9707
[1m31/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m3s[0m 62ms/step - accuracy: 0.5539 - loss: 0.9708[32m [repeated 86x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.3010 - loss: 1.8488[32m [repeated 236x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 159ms/step - accuracy: 0.5000 - loss: 1.0020
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 71ms/step - accuracy: 0.5215 - loss: 0.9899  [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.5084 - loss: 1.0901
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.5083 - loss: 1.0901
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.5082 - loss: 1.0902
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 39ms/step - accuracy: 0.3142 - loss: 1.3348  
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 49/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 43ms/step - accuracy: 0.3064 - loss: 1.9378
[1m 51/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m12s[0m 43ms/step - accuracy: 0.3064 - loss: 1.9348[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m104/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4869 - loss: 1.1257
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4871 - loss: 1.1254
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.4873 - loss: 1.1252
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m11s[0m 43ms/step - accuracy: 0.3066 - loss: 1.9152[32m [repeated 32x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 55ms/step - accuracy: 0.3594 - loss: 1.3336 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 55ms/step - accuracy: 0.3568 - loss: 1.3282
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2663 - loss: 1.9619 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2863 - loss: 1.9051[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m Epoch 18/130[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m24/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 64ms/step - accuracy: 0.3691 - loss: 1.2894
[1m25/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 64ms/step - accuracy: 0.3693 - loss: 1.2888
[1m26/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 64ms/step - accuracy: 0.3695 - loss: 1.2882
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m37/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.3710 - loss: 1.2842
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.3711 - loss: 1.2840
[1m39/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.3712 - loss: 1.2838
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 49ms/step - accuracy: 0.4026 - loss: 1.2541 - val_accuracy: 0.3788 - val_loss: 1.1857[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 35ms/step - accuracy: 0.2880 - loss: 1.7090 - val_accuracy: 0.3755 - val_loss: 1.2644[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 59ms/step - accuracy: 0.5371 - loss: 1.0142  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 59ms/step - accuracy: 0.5499 - loss: 1.0014
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m29s[0m 89ms/step - accuracy: 0.4688 - loss: 1.2378[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m11/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step - accuracy: 0.3789 - loss: 1.3334[32m [repeated 63x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m273/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.3764 - loss: 1.2708
[1m276/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.3763 - loss: 1.2709[32m [repeated 308x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m13/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step - accuracy: 0.3770 - loss: 1.3335
[1m15/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step - accuracy: 0.3740 - loss: 1.3343[32m [repeated 74x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m277/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 30ms/step - accuracy: 0.3928 - loss: 1.2384[32m [repeated 269x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 99ms/step - accuracy: 0.2969 - loss: 1.9313
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 32ms/step - accuracy: 0.3264 - loss: 1.8557 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 34ms/step - accuracy: 0.3594 - loss: 1.3281  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 67ms/step - accuracy: 0.4225 - loss: 1.1366
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 64ms/step - accuracy: 0.4168 - loss: 1.1428[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m157/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m7s[0m 43ms/step - accuracy: 0.3093 - loss: 1.8928
[1m158/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m7s[0m 43ms/step - accuracy: 0.3093 - loss: 1.8928
[1m159/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m7s[0m 43ms/step - accuracy: 0.3093 - loss: 1.8928[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.5425 - loss: 1.2543   
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.2646 - loss: 1.7983 [32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 56ms/step - accuracy: 0.3418 - loss: 1.3374 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 46ms/step - accuracy: 0.3586 - loss: 1.3114
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.4740 - loss: 1.2187 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.4659 - loss: 1.2180
[36m(train_cnn_ray_tune pid=3118310)[0m Epoch 21/72[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 52ms/step - accuracy: 0.3750 - loss: 1.2461 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 51ms/step - accuracy: 0.3707 - loss: 1.2470
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3229 - loss: 1.2878 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3325 - loss: 1.2901
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 44ms/step - accuracy: 0.3633 - loss: 1.3325 - val_accuracy: 0.3752 - val_loss: 1.3002[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 53ms/step - accuracy: 0.3984 - loss: 1.3566 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 43ms/step - accuracy: 0.3809 - loss: 1.3599
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 43ms/step - accuracy: 0.3744 - loss: 1.2921 - val_accuracy: 0.3893 - val_loss: 1.2567[32m [repeated 12x across cluster][0m
Trial status: 18 RUNNING | 2 TERMINATED
Current time: 2025-11-05 11:12:45. Total running time: 2min 30s
Logical resource usage: 18.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            tanh                                   32                 32                  5          0.00357251         149                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         tanh                                  128                 32                  3          0.000165933        107                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  5          0.000146441        116                                              │
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                 64                  3          0.000386417        121                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   64                 32                  5          1.56689e-05        116                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.00149202          59                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   64                 32                  5          0.00247751         130                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  3          0.000917269        145                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                  128                128                  5          7.16419e-05         66                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.0020272           93                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00213202          64                                              │
│ trial_9f7c8    RUNNING              3   adam            tanh                                  128                128                  3          0.000931045         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         relu                                  128                128                  3          0.00205798         108                                              │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 43ms/step - accuracy: 0.3798 - loss: 1.3351  
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 41ms/step - accuracy: 0.3775 - loss: 1.3373
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 129ms/step - accuracy: 0.3906 - loss: 1.2391[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m59/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 44ms/step - accuracy: 0.3946 - loss: 1.2463[32m [repeated 75x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m137/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 58ms/step - accuracy: 0.4353 - loss: 1.1724
[1m138/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 59ms/step - accuracy: 0.4352 - loss: 1.1725[32m [repeated 281x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m49/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 59ms/step - accuracy: 0.3828 - loss: 1.2633
[1m50/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 59ms/step - accuracy: 0.3827 - loss: 1.2634[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 52/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m4s[0m 41ms/step - accuracy: 0.3952 - loss: 1.2428[32m [repeated 267x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 94ms/step - accuracy: 0.2344 - loss: 1.9027
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.2321 - loss: 1.9277 
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 43ms/step - accuracy: 0.4054 - loss: 1.2464  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 74ms/step - accuracy: 0.5625 - loss: 0.9872
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.5560 - loss: 1.0086 
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 44/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3778 - loss: 1.2774
[1m 46/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3786 - loss: 1.2757[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 66ms/step - accuracy: 0.5898 - loss: 1.1233  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 62ms/step - accuracy: 0.5825 - loss: 1.1000
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 48/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 35ms/step - accuracy: 0.3793 - loss: 1.2742 [32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m Epoch 36/107[32m [repeated 17x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 58ms/step - accuracy: 0.4277 - loss: 1.1573 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 43ms/step - accuracy: 0.4202 - loss: 1.1903
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 37ms/step - accuracy: 0.3980 - loss: 1.3162 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.3914 - loss: 1.3166
[36m(train_cnn_ray_tune pid=3118320)[0m 
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 40ms/step - accuracy: 0.3904 - loss: 1.2486
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 29ms/step - accuracy: 0.2908 - loss: 1.6668
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 29ms/step - accuracy: 0.2909 - loss: 1.6666
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 29ms/step - accuracy: 0.2910 - loss: 1.6664
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3037 - loss: 1.6266 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.2993 - loss: 1.6523[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 67ms/step - accuracy: 0.3802 - loss: 1.2668 - val_accuracy: 0.4037 - val_loss: 1.2475[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m17s[0m 50ms/step - accuracy: 0.3073 - loss: 1.8876 - val_accuracy: 0.3709 - val_loss: 1.5297[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 36ms/step - accuracy: 0.3351 - loss: 1.5904  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 35ms/step - accuracy: 0.3254 - loss: 1.6084
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 101ms/step - accuracy: 0.3750 - loss: 1.4453[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m70/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 45ms/step - accuracy: 0.3984 - loss: 1.2407[32m [repeated 76x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m123/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m6s[0m 29ms/step - accuracy: 0.3281 - loss: 1.9957
[1m125/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m6s[0m 29ms/step - accuracy: 0.3284 - loss: 1.9954[32m [repeated 333x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m37/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 59ms/step - accuracy: 0.3651 - loss: 1.2815
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 60ms/step - accuracy: 0.3652 - loss: 1.2814[32m [repeated 80x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 37ms/step - accuracy: 0.3812 - loss: 1.2784[32m [repeated 222x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  2/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 54ms/step - accuracy: 0.3438 - loss: 1.7434 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 90ms/step - accuracy: 0.4219 - loss: 1.1738
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 29ms/step - accuracy: 0.4358 - loss: 1.1546 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - accuracy: 0.3216 - loss: 1.8472
[1m 24/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 46ms/step - accuracy: 0.3210 - loss: 1.8464[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.4160 - loss: 1.1458  
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 42ms/step - accuracy: 0.4175 - loss: 1.1744
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 45ms/step - accuracy: 0.3191 - loss: 1.8407[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 40ms/step - accuracy: 0.3672 - loss: 1.3441 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 37ms/step - accuracy: 0.3627 - loss: 1.3418
[36m(train_cnn_ray_tune pid=3118313)[0m Epoch 23/108[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 57ms/step - accuracy: 0.4583 - loss: 1.1304 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 56ms/step - accuracy: 0.4560 - loss: 1.1297
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 77ms/step - accuracy: 0.3984 - loss: 1.2371  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 71ms/step - accuracy: 0.3750 - loss: 1.2513
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 66ms/step - accuracy: 0.3688 - loss: 1.2753 - val_accuracy: 0.3945 - val_loss: 1.2364[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 45ms/step - accuracy: 0.3194 - loss: 1.8277 
[1m113/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m9s[0m 45ms/step - accuracy: 0.3195 - loss: 1.8276
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m36/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m3s[0m 63ms/step - accuracy: 0.5779 - loss: 0.9307
[1m37/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.5778 - loss: 0.9311
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 63ms/step - accuracy: 0.5778 - loss: 0.9315
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 29ms/step - accuracy: 0.3144 - loss: 1.7957 - val_accuracy: 0.3505 - val_loss: 1.2683[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.4392 - loss: 1.3129 
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5550 - loss: 1.0410  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5518 - loss: 1.0473
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m33s[0m 101ms/step - accuracy: 0.3438 - loss: 1.4767[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m51/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 64ms/step - accuracy: 0.5765 - loss: 0.9353[32m [repeated 86x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 29ms/step - accuracy: 0.3371 - loss: 1.9816
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 29ms/step - accuracy: 0.3372 - loss: 1.9815[32m [repeated 287x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m28/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 58ms/step - accuracy: 0.3835 - loss: 1.2639
[1m29/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 57ms/step - accuracy: 0.3838 - loss: 1.2638[32m [repeated 65x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m5s[0m 28ms/step - accuracy: 0.3902 - loss: 1.2889[32m [repeated 257x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.3056 - loss: 1.2987  [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m30s[0m 91ms/step - accuracy: 0.3750 - loss: 1.1502
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3646 - loss: 1.2177[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3409 - loss: 1.3591
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3407 - loss: 1.3566[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3139 - loss: 1.4894
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3122 - loss: 1.4983
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3068 - loss: 1.5111
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 25/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3439 - loss: 1.3500[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2937 - loss: 1.5876
[1m 47/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2939 - loss: 1.5885
[1m 49/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.2940 - loss: 1.5894
[36m(train_cnn_ray_tune pid=3118322)[0m Epoch 33/145[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 66ms/step - accuracy: 0.5547 - loss: 0.9953  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 68ms/step - accuracy: 0.5477 - loss: 0.9960
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 44ms/step - accuracy: 0.3742 - loss: 1.3229 - val_accuracy: 0.3745 - val_loss: 1.2955[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.2691 - loss: 2.2407 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.2902 - loss: 2.1732[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m72/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 66ms/step - accuracy: 0.5747 - loss: 0.9387
[1m73/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 66ms/step - accuracy: 0.5747 - loss: 0.9388
[1m74/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 66ms/step - accuracy: 0.5746 - loss: 0.9389[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m11s[0m 65ms/step - accuracy: 0.4413 - loss: 1.1538 - val_accuracy: 0.4346 - val_loss: 1.1301[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 44ms/step - accuracy: 0.3997 - loss: 1.1917 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.3316 - loss: 1.4013  
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.3240 - loss: 1.3900
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 108ms/step - accuracy: 0.3984 - loss: 1.1751[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m47/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 38ms/step - accuracy: 0.3712 - loss: 1.3183[32m [repeated 98x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.5202 - loss: 1.0565
[1m 18/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.5180 - loss: 1.0595[32m [repeated 274x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m18/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m4s[0m 62ms/step - accuracy: 0.3769 - loss: 1.2604
[1m19/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m4s[0m 62ms/step - accuracy: 0.3767 - loss: 1.2606[32m [repeated 61x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 42ms/step - accuracy: 0.3172 - loss: 1.8295[32m [repeated 271x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 129ms/step - accuracy: 0.5156 - loss: 1.0067
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.5278 - loss: 1.0257  [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 11/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 64ms/step - accuracy: 0.4616 - loss: 1.1501
[1m 12/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 63ms/step - accuracy: 0.4613 - loss: 1.1500 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.3967 - loss: 1.2032
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.3973 - loss: 1.2044
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.3987 - loss: 1.2040[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 32ms/step - accuracy: 0.3325 - loss: 1.3664 [32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3245 - loss: 1.4991  
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4329 - loss: 1.2225  
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4106 - loss: 1.2401
[36m(train_cnn_ray_tune pid=3118318)[0m Epoch 14/73[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 54ms/step - accuracy: 0.3906 - loss: 1.2931  
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 42ms/step - accuracy: 0.3703 - loss: 1.2917
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 47ms/step - accuracy: 0.3679 - loss: 1.3197 - val_accuracy: 0.3752 - val_loss: 1.2947[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m70/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 60ms/step - accuracy: 0.3769 - loss: 1.2615
[1m71/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 60ms/step - accuracy: 0.3769 - loss: 1.2615
[1m72/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 60ms/step - accuracy: 0.3769 - loss: 1.2615[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 27ms/step - accuracy: 0.5184 - loss: 1.0626 - val_accuracy: 0.5434 - val_loss: 0.9740[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 125ms/step - accuracy: 0.3359 - loss: 1.2833[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 9/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 53ms/step - accuracy: 0.3718 - loss: 1.2751[32m [repeated 73x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.5263 - loss: 1.0545
[1m 48/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.5260 - loss: 1.0543[32m [repeated 277x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 7/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 51ms/step - accuracy: 0.3720 - loss: 1.2760
[1m 8/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 51ms/step - accuracy: 0.3722 - loss: 1.2748[32m [repeated 74x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m222/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m3s[0m 29ms/step - accuracy: 0.3320 - loss: 1.9463[32m [repeated 308x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 137ms/step - accuracy: 0.5391 - loss: 1.2011
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 64ms/step - accuracy: 0.5312 - loss: 1.1681  [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 39ms/step - accuracy: 0.3035 - loss: 1.8028
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 37ms/step - accuracy: 0.3043 - loss: 1.8032[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.3195 - loss: 1.7375
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.3194 - loss: 1.7381
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.3192 - loss: 1.7387[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 33ms/step - accuracy: 0.4010 - loss: 1.3736 [32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3018 - loss: 1.5944 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2939 - loss: 1.5988
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.5130 - loss: 1.1302  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5099 - loss: 1.1042
[36m(train_cnn_ray_tune pid=3118317)[0m Epoch 27/111[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 248ms/step
[1m 6/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step  
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 57ms/step - accuracy: 0.3418 - loss: 1.2926  
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 50ms/step - accuracy: 0.3646 - loss: 1.2862
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m11/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m17/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m21/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m26/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m30/49[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m34/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 13ms/step
[1m40/49[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m45/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 42ms/step - accuracy: 0.4054 - loss: 1.2093 
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 54ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m 5/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m10/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[1m14/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m17/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
[1m22/96[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m27/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4821 - loss: 1.1180  
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m32/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m36/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m40/96[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m45/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 49ms/step - accuracy: 0.4149 - loss: 1.2230 - val_accuracy: 0.3876 - val_loss: 1.1652[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118308)[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=3118308)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m50/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m55/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m61/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m60/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 58ms/step - accuracy: 0.3783 - loss: 1.2613
[1m61/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 58ms/step - accuracy: 0.3783 - loss: 1.2613
[1m62/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 58ms/step - accuracy: 0.3783 - loss: 1.2612
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m74/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 12ms/step
[1m79/96[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m84/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step
[1m89/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118308)[0m 
[1m94/96[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 12ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 27ms/step - accuracy: 0.5218 - loss: 1.0577 - val_accuracy: 0.5690 - val_loss: 0.9872[32m [repeated 9x across cluster][0m

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:13:10. Total running time: 2min 55s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             172.777 │
│ time_total_s                 172.777 │
│ training_iteration                 1 │
│ val_accuracy                 0.38338 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:13:10. Total running time: 2min 55s
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 125ms/step - accuracy: 0.3438 - loss: 1.2191[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m51/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 39ms/step - accuracy: 0.4064 - loss: 1.2145[32m [repeated 71x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3394 - loss: 1.9204
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3377 - loss: 1.9215[32m [repeated 282x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m11/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 29ms/step - accuracy: 0.3608 - loss: 1.3352
[1m13/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 30ms/step - accuracy: 0.3593 - loss: 1.3354[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.5244 - loss: 1.0456[32m [repeated 224x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 77ms/step - accuracy: 0.5078 - loss: 1.1729  
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 67ms/step - accuracy: 0.5104 - loss: 1.1268
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 74ms/step - accuracy: 0.4375 - loss: 1.1527
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.4891 - loss: 1.1176 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 59/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m11s[0m 42ms/step - accuracy: 0.3006 - loss: 1.7901
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m11s[0m 42ms/step - accuracy: 0.3006 - loss: 1.7906[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 27/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.5098 - loss: 1.0972
[1m 30/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.5110 - loss: 1.0960
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.5128 - loss: 1.0942[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 88/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m10s[0m 41ms/step - accuracy: 0.2994 - loss: 1.7947[32m [repeated 30x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 30ms/step - accuracy: 0.3351 - loss: 1.3390 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.3270 - loss: 1.3311[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m Epoch 28/111[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.2726 - loss: 1.4799 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.2748 - loss: 1.4959
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.2783 - loss: 1.5138
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3255 - loss: 1.8246  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3277 - loss: 1.7967
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m43/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 62ms/step - accuracy: 0.5443 - loss: 0.9806
[1m44/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 61ms/step - accuracy: 0.5449 - loss: 0.9797
[1m45/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 61ms/step - accuracy: 0.5454 - loss: 0.9787
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.2977 - loss: 1.3607 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 33ms/step - accuracy: 0.3134 - loss: 1.3577
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 46ms/step - accuracy: 0.4184 - loss: 1.1381  
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 41ms/step - accuracy: 0.3585 - loss: 1.3242 - val_accuracy: 0.3784 - val_loss: 1.2899[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4844 - loss: 1.1109  

Trial status: 17 RUNNING | 3 TERMINATED
Current time: 2025-11-05 11:13:15. Total running time: 3min 0s
Logical resource usage: 17.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            tanh                                   32                 32                  5          0.00357251         149                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         tanh                                  128                 32                  3          0.000165933        107                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                 64                  3          0.000386417        121                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   64                 32                  5          1.56689e-05        116                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.00149202          59                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   64                 32                  5          0.00247751         130                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  3          0.000917269        145                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                  128                128                  5          7.16419e-05         66                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.0020272           93                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00213202          64                                              │
│ trial_9f7c8    RUNNING              3   adam            tanh                                  128                128                  3          0.000931045         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         relu                                  128                128                  3          0.00205798         108                                              │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 26ms/step - accuracy: 0.5201 - loss: 1.0759 - val_accuracy: 0.5854 - val_loss: 0.9294[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 100ms/step - accuracy: 0.5000 - loss: 1.1261[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m80/84[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 61ms/step - accuracy: 0.5580 - loss: 0.9602[32m [repeated 80x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m101/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.5232 - loss: 1.0348
[1m103/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.5232 - loss: 1.0350[32m [repeated 294x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m75/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 40ms/step - accuracy: 0.4090 - loss: 1.2057
[1m77/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 39ms/step - accuracy: 0.4089 - loss: 1.2059[32m [repeated 79x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m204/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m3s[0m 27ms/step - accuracy: 0.3307 - loss: 1.9343[32m [repeated 254x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 85ms/step - accuracy: 0.5625 - loss: 1.0331
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.5243 - loss: 1.0556 
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.4441 - loss: 1.1831
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 33ms/step - accuracy: 0.4411 - loss: 1.1867 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 62/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.3830 - loss: 1.2902
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.3831 - loss: 1.2899
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m7s[0m 27ms/step - accuracy: 0.3831 - loss: 1.2895[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 28/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.4471 - loss: 1.1794[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 86ms/step - accuracy: 0.3750 - loss: 1.2577
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.3819 - loss: 1.2249 
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 40ms/step - accuracy: 0.4714 - loss: 1.1524 
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.4965 - loss: 1.1707 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.4614 - loss: 1.2157[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 35/116[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 41ms/step - accuracy: 0.4141 - loss: 1.2604  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 42ms/step - accuracy: 0.4166 - loss: 1.2429
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3024 - loss: 1.7413   
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3040 - loss: 1.7678
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 32ms/step - accuracy: 0.2977 - loss: 1.4066  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2997 - loss: 1.4367
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m37/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 51ms/step - accuracy: 0.4030 - loss: 1.2325
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 51ms/step - accuracy: 0.4029 - loss: 1.2325
[1m39/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 51ms/step - accuracy: 0.4027 - loss: 1.2326[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 44ms/step - accuracy: 0.3567 - loss: 1.3180 - val_accuracy: 0.3781 - val_loss: 1.2877[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 370ms/step
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 30ms/step - accuracy: 0.3302 - loss: 1.9322 - val_accuracy: 0.3952 - val_loss: 1.2501[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m 7/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step   
[1m10/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m14/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m19/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m23/49[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m28/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m34/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.4332 - loss: 1.1798 
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m39/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 11ms/step
[1m44/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 110ms/step - accuracy: 0.4609 - loss: 1.1595[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m69/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 60ms/step - accuracy: 0.5873 - loss: 0.9200[32m [repeated 69x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3202 - loss: 1.9474
[1m 26/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 22ms/step - accuracy: 0.3185 - loss: 1.9511[32m [repeated 293x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 9/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 29ms/step - accuracy: 0.4018 - loss: 1.2143
[1m11/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 29ms/step - accuracy: 0.4004 - loss: 1.2156[32m [repeated 77x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m312/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 31ms/step - accuracy: 0.4023 - loss: 1.2231[32m [repeated 211x across cluster][0m
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 17ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 17ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 49ms/step - accuracy: 0.3845 - loss: 1.2177 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 53ms/step - accuracy: 0.3846 - loss: 1.2166
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 55ms/step
[1m 5/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 39ms/step - accuracy: 0.2629 - loss: 1.7940
[1m 12/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 40ms/step - accuracy: 0.2642 - loss: 1.7922
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m 9/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 14ms/step
[1m14/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.3034 - loss: 1.5448
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.3035 - loss: 1.5447
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 24ms/step - accuracy: 0.3036 - loss: 1.5445[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 42ms/step - accuracy: 0.2720 - loss: 1.7799[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 42ms/step - accuracy: 0.2750 - loss: 1.7766
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 41ms/step - accuracy: 0.2783 - loss: 1.7727
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m19/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m24/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m29/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m32/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m36/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 133ms/step - accuracy: 0.5469 - loss: 0.9466
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 63ms/step - accuracy: 0.5195 - loss: 1.0147 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 40ms/step - accuracy: 0.2842 - loss: 1.7656
[1m 29/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 39ms/step - accuracy: 0.2861 - loss: 1.7636
[36m(train_cnn_ray_tune pid=3118321)[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=3118321)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m40/96[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m44/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m48/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m52/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m57/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m60/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 13ms/step
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 39ms/step - accuracy: 0.2897 - loss: 1.7599
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 39ms/step - accuracy: 0.2905 - loss: 1.7594
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 13ms/step
[1m73/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 41/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 39ms/step - accuracy: 0.2913 - loss: 1.7587
[1m 43/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.2919 - loss: 1.7584
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m77/96[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step
[1m83/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m87/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 13ms/step
[1m91/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118321)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:13:23. Total running time: 3min 8s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             184.977 │
│ time_total_s                 184.977 │
│ training_iteration                 1 │
│ val_accuracy                 0.37122 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:13:23. Total running time: 3min 8s
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.3299 - loss: 1.7675 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3331 - loss: 1.6922[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 51/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.2933 - loss: 1.7576
[1m 53/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.2935 - loss: 1.7575
[36m(train_cnn_ray_tune pid=3118313)[0m Epoch 28/108[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.2947 - loss: 1.7585
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 38ms/step - accuracy: 0.2948 - loss: 1.7586 
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.2821 - loss: 1.6054  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2860 - loss: 1.5866[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 41ms/step - accuracy: 0.3693 - loss: 1.2995 - val_accuracy: 0.3739 - val_loss: 1.2851[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 30ms/step - accuracy: 0.3051 - loss: 1.5415 - val_accuracy: 0.3886 - val_loss: 1.2540[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 53ms/step - accuracy: 0.3770 - loss: 1.2101 
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 117ms/step - accuracy: 0.3906 - loss: 1.2128[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m44/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 39ms/step - accuracy: 0.4161 - loss: 1.2141[32m [repeated 65x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.3279 - loss: 1.9101
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 26ms/step - accuracy: 0.3279 - loss: 1.9098[32m [repeated 280x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m47/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 32ms/step - accuracy: 0.3620 - loss: 1.3055
[1m49/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 32ms/step - accuracy: 0.3623 - loss: 1.3053[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 88/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m7s[0m 32ms/step - accuracy: 0.4115 - loss: 1.2027[32m [repeated 202x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m63/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 59ms/step - accuracy: 0.5891 - loss: 0.8924
[1m64/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 59ms/step - accuracy: 0.5890 - loss: 0.8925
[1m65/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 59ms/step - accuracy: 0.5890 - loss: 0.8927
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.3014 - loss: 1.5506
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.3015 - loss: 1.5503
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.3018 - loss: 1.5499
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.2945 - loss: 1.7584[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.4332 - loss: 1.1723
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.4305 - loss: 1.1767
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.4219 - loss: 1.1701
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.4177 - loss: 1.1943 
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 67ms/step - accuracy: 0.3281 - loss: 1.8473
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.3232 - loss: 1.7168 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m167/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3784 - loss: 1.2492
[1m169/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3784 - loss: 1.2492
[1m171/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3785 - loss: 1.2492
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 16ms/step - accuracy: 0.3568 - loss: 1.2610 
[1m  8/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 16ms/step - accuracy: 0.3506 - loss: 1.2745[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.3455 - loss: 1.3153 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.3573 - loss: 1.3086
[36m(train_cnn_ray_tune pid=3118313)[0m Epoch 29/108[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 66ms/step - accuracy: 0.5877 - loss: 0.8951 - val_accuracy: 0.4451 - val_loss: 1.7410[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 24ms/step - accuracy: 0.5313 - loss: 1.0189 - val_accuracy: 0.5200 - val_loss: 1.0023[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 50ms/step - accuracy: 0.4319 - loss: 1.2058 
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  2/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 58ms/step - accuracy: 0.4883 - loss: 1.1122  
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 97ms/step - accuracy: 0.1875 - loss: 1.7093[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m45/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m2s[0m 58ms/step - accuracy: 0.5630 - loss: 0.9939[32m [repeated 82x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 34/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 23ms/step - accuracy: 0.3263 - loss: 1.8198
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3261 - loss: 1.8207[32m [repeated 281x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 7/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 53ms/step - accuracy: 0.4275 - loss: 1.1982
[1m 8/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 54ms/step - accuracy: 0.4263 - loss: 1.1992[32m [repeated 77x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 34ms/step - accuracy: 0.4108 - loss: 1.2131[32m [repeated 199x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2578 - loss: 1.7408 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 85ms/step - accuracy: 0.5625 - loss: 1.0912
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5384 - loss: 1.0608 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m161/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3936 - loss: 1.2284
[1m163/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3936 - loss: 1.2283
[1m165/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3937 - loss: 1.2282[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step - accuracy: 0.4839 - loss: 1.0945 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.5031 - loss: 1.0739
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 41ms/step - accuracy: 0.3724 - loss: 1.2802 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 39ms/step - accuracy: 0.3679 - loss: 1.2894[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m Epoch 44/66[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 58ms/step - accuracy: 0.6036 - loss: 0.8856
[1m 6/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 57ms/step - accuracy: 0.6015 - loss: 0.8912
[1m 7/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 56ms/step - accuracy: 0.5990 - loss: 0.8956
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 39ms/step - accuracy: 0.3647 - loss: 1.2986 - val_accuracy: 0.3732 - val_loss: 1.2820[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3682 - loss: 1.5550 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.3647 - loss: 1.5749
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3617 - loss: 1.5694
[1m  8/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 29ms/step - accuracy: 0.3498 - loss: 1.5867 
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 223ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m 7/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step  
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m12/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m17/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 12/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3371 - loss: 1.6112
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3363 - loss: 1.6151
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 55ms/step - accuracy: 0.4023 - loss: 1.2124 
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 23ms/step - accuracy: 0.5361 - loss: 1.0424 - val_accuracy: 0.5733 - val_loss: 0.9244[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m21/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m26/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3346 - loss: 1.6216
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.3328 - loss: 1.6286
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m32/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 11ms/step
[1m37/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m44/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 16ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 111ms/step - accuracy: 0.4141 - loss: 1.1951[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m14/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 48ms/step - accuracy: 0.3978 - loss: 1.2293[32m [repeated 76x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.3121 - loss: 1.5113
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.3120 - loss: 1.5110[32m [repeated 272x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m20/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step - accuracy: 0.4274 - loss: 1.2045
[1m21/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 40ms/step - accuracy: 0.4282 - loss: 1.2034[32m [repeated 75x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m119/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 22ms/step - accuracy: 0.3941 - loss: 1.2367[32m [repeated 186x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 58ms/step
[1m 7/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 35/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.3237 - loss: 1.6687
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.3235 - loss: 1.6705
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m13/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m19/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m23/96[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m29/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m34/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m38/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 45/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3224 - loss: 1.6762[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m44/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m49/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m53/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 11ms/step
[1m59/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 49/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.3218 - loss: 1.6786
[1m 50/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3217 - loss: 1.6792
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 85ms/step - accuracy: 0.7188 - loss: 1.1996
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.5521 - loss: 1.2620 [32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m65/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 11ms/step
[1m71/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m76/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m82/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 11ms/step
[1m87/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118314)[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=3118314)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118314)[0m 
[1m92/96[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 11ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 58/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3203 - loss: 1.6842
[1m 60/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3200 - loss: 1.6851

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:13:37. Total running time: 3min 22s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             199.893 │
│ time_total_s                 199.893 │
│ training_iteration                 1 │
│ val_accuracy                 0.36695 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:13:37. Total running time: 3min 22s
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 62/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3197 - loss: 1.6859
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3195 - loss: 1.6866
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3195 - loss: 1.6870
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3195 - loss: 1.6872
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 62/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.5262 - loss: 1.0248
[1m 65/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.5263 - loss: 1.0248
[1m 67/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.5262 - loss: 1.0248[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3195 - loss: 1.6875
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3195 - loss: 1.6879
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 75/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m10s[0m 39ms/step - accuracy: 0.3195 - loss: 1.6887
[1m 77/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 39ms/step - accuracy: 0.3194 - loss: 1.6890 
[36m(train_cnn_ray_tune pid=3118309)[0m Epoch 18/64[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.3889 - loss: 1.2880 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.3829 - loss: 1.2946
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m36/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 50ms/step - accuracy: 0.4049 - loss: 1.2220
[1m37/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 50ms/step - accuracy: 0.4052 - loss: 1.2216
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 50ms/step - accuracy: 0.4054 - loss: 1.2212
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 54ms/step - accuracy: 0.4107 - loss: 1.2136 - val_accuracy: 0.4254 - val_loss: 1.1714[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3346 - loss: 1.9043 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3576 - loss: 1.8224
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 40ms/step - accuracy: 0.5503 - loss: 1.0631  
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 46ms/step - accuracy: 0.4453 - loss: 1.1402  
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 49ms/step - accuracy: 0.4448 - loss: 1.1476
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 38ms/step - accuracy: 0.4272 - loss: 1.2200 - val_accuracy: 0.4451 - val_loss: 1.1688[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 111ms/step - accuracy: 0.2969 - loss: 1.3067[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m34/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 52ms/step - accuracy: 0.5764 - loss: 0.9195[32m [repeated 77x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 49/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.3563 - loss: 1.7944
[1m 51/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.3555 - loss: 1.7971[32m [repeated 256x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m21/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 49ms/step - accuracy: 0.4321 - loss: 1.2007
[1m23/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 48ms/step - accuracy: 0.4318 - loss: 1.2018[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 22/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 49ms/step - accuracy: 0.5036 - loss: 1.0905[32m [repeated 205x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 33ms/step - accuracy: 0.3377 - loss: 1.3127 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 33ms/step - accuracy: 0.3494 - loss: 1.3005
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.3522 - loss: 1.4483 [32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 101ms/step - accuracy: 0.5625 - loss: 0.9310
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 50ms/step - accuracy: 0.5762 - loss: 0.9300 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m65/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 53ms/step - accuracy: 0.5786 - loss: 0.9099
[1m66/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 53ms/step - accuracy: 0.5787 - loss: 0.9097
[1m67/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 53ms/step - accuracy: 0.5788 - loss: 0.9096
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5264 - loss: 1.0588 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5371 - loss: 1.0388
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 41/116[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 13/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2932 - loss: 1.7040
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2943 - loss: 1.7057
[1m 19/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2956 - loss: 1.7040
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 65/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.3086 - loss: 1.6869
[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.3091 - loss: 1.6866
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.3095 - loss: 1.6863
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.3733 - loss: 1.3027 
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 39ms/step - accuracy: 0.3675 - loss: 1.2932 - val_accuracy: 0.3748 - val_loss: 1.2767[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 37ms/step - accuracy: 0.3924 - loss: 1.2343  

Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-11-05 11:13:45. Total running time: 3min 30s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         tanh                                  128                 32                  3          0.000165933        107                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                 64                  3          0.000386417        121                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   64                 32                  5          1.56689e-05        116                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.00149202          59                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  3          0.000917269        145                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                  128                128                  5          7.16419e-05         66                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.0020272           93                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00213202          64                                              │
│ trial_9f7c8    RUNNING              3   adam            tanh                                  128                128                  3          0.000931045         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         relu                                  128                128                  3          0.00205798         108                                              │
│ trial_9f7c8    TERMINATED           2   adam            tanh                                   32                 32                  5          0.00357251         149        1           199.893          0.366951 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   64                 32                  5          0.00247751         130        1           184.977          0.371222 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 29ms/step - accuracy: 0.3260 - loss: 1.4733 - val_accuracy: 0.3949 - val_loss: 1.2527[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m26s[0m 79ms/step - accuracy: 0.4688 - loss: 1.1510[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m30/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 54ms/step - accuracy: 0.5926 - loss: 0.8781[32m [repeated 83x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m262/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.3397 - loss: 1.8341
[1m264/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.3396 - loss: 1.8342[32m [repeated 281x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m42/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 31ms/step - accuracy: 0.3813 - loss: 1.2937
[1m44/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 31ms/step - accuracy: 0.3811 - loss: 1.2935[32m [repeated 73x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 94/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.3140 - loss: 1.4652[32m [repeated 161x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4284 - loss: 1.1905 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 75ms/step - accuracy: 0.5312 - loss: 1.1551
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.5540 - loss: 1.0760 [32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3643 - loss: 1.6164 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.3569 - loss: 1.6089[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m Epoch 53/107[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 61ms/step - accuracy: 0.3750 - loss: 1.1745 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 50ms/step - accuracy: 0.3770 - loss: 1.2015
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3009 - loss: 1.5983
[1m 25/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3018 - loss: 1.6009
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 31/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3027 - loss: 1.6094
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3028 - loss: 1.6119
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3027 - loss: 1.6167
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3028 - loss: 1.6177
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 42ms/step - accuracy: 0.4533 - loss: 1.1579 - val_accuracy: 0.4290 - val_loss: 1.1171[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3029 - loss: 1.6196
[1m 42/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3031 - loss: 1.6213
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 44/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 35ms/step - accuracy: 0.3031 - loss: 1.6231
[1m 46/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 35ms/step - accuracy: 0.3030 - loss: 1.6248 
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 23ms/step - accuracy: 0.5482 - loss: 1.0112 - val_accuracy: 0.5595 - val_loss: 0.9445[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m23/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 55ms/step - accuracy: 0.5862 - loss: 0.9249
[1m24/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 55ms/step - accuracy: 0.5864 - loss: 0.9238
[1m25/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 55ms/step - accuracy: 0.5865 - loss: 0.9226
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 82ms/step - accuracy: 0.3281 - loss: 1.5063[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m47/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 46ms/step - accuracy: 0.4055 - loss: 1.1955[32m [repeated 112x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 23/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.5117 - loss: 1.0502
[1m 26/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 20ms/step - accuracy: 0.5142 - loss: 1.0470[32m [repeated 251x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m28/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 55ms/step - accuracy: 0.5868 - loss: 0.9194
[1m29/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 55ms/step - accuracy: 0.5869 - loss: 0.9184[32m [repeated 58x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 89/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m5s[0m 24ms/step - accuracy: 0.3535 - loss: 1.8152[32m [repeated 161x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3185 - loss: 1.4532
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3185 - loss: 1.4533
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3185 - loss: 1.4534
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.4766 - loss: 1.1112 [32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m39s[0m 120ms/step - accuracy: 0.2812 - loss: 1.2084
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.3507 - loss: 1.1603 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.3340 - loss: 1.5804 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3239 - loss: 1.6161[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m Epoch 28/121[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 45ms/step - accuracy: 0.3993 - loss: 1.2112 
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 49ms/step - accuracy: 0.4059 - loss: 1.2035[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3885 - loss: 1.1470
[1m  8/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.4022 - loss: 1.1411 
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 55ms/step - accuracy: 0.5957 - loss: 0.8555  
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 47ms/step - accuracy: 0.6006 - loss: 0.8608
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.4714 - loss: 1.1700  
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 62ms/step - accuracy: 0.5930 - loss: 0.8924 - val_accuracy: 0.5562 - val_loss: 0.9160[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 24ms/step - accuracy: 0.5264 - loss: 1.0377 - val_accuracy: 0.5897 - val_loss: 0.8996[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m37/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m2s[0m 47ms/step - accuracy: 0.4186 - loss: 1.1911
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 47ms/step - accuracy: 0.4188 - loss: 1.1910
[1m39/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 48ms/step - accuracy: 0.4189 - loss: 1.1909[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m35s[0m 106ms/step - accuracy: 0.4375 - loss: 1.1724[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m53/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 48ms/step - accuracy: 0.4207 - loss: 1.1896[32m [repeated 85x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 24ms/step - accuracy: 0.3446 - loss: 1.8089
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 24ms/step - accuracy: 0.3446 - loss: 1.8088[32m [repeated 257x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m24/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 58ms/step - accuracy: 0.5978 - loss: 0.8757
[1m25/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 58ms/step - accuracy: 0.5975 - loss: 0.8766[32m [repeated 74x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.3208 - loss: 1.4661[32m [repeated 231x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.3407 - loss: 1.5944
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.3403 - loss: 1.5946
[1m 47/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 21ms/step - accuracy: 0.3395 - loss: 1.5952
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5439 - loss: 1.0126
[1m 93/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5440 - loss: 1.0129
[1m 97/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 20ms/step - accuracy: 0.5442 - loss: 1.0136
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 42ms/step - accuracy: 0.4366 - loss: 1.2094 
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 88ms/step - accuracy: 0.4375 - loss: 1.5849
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3893 - loss: 1.5523 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 28ms/step - accuracy: 0.4115 - loss: 1.1903 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 27ms/step - accuracy: 0.4222 - loss: 1.1906[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m Epoch 37/111[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3108 - loss: 1.7088
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3295 - loss: 1.7160 
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3056 - loss: 1.4504  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 25ms/step - accuracy: 0.3125 - loss: 1.4491
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 45ms/step - accuracy: 0.6302 - loss: 0.8719 
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 38ms/step - accuracy: 0.4577 - loss: 1.1414 - val_accuracy: 0.4786 - val_loss: 1.0865[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 37ms/step - accuracy: 0.4167 - loss: 1.2049 - val_accuracy: 0.4432 - val_loss: 1.1626[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m24/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 52ms/step - accuracy: 0.6089 - loss: 0.8764
[1m25/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 52ms/step - accuracy: 0.6089 - loss: 0.8763
[1m26/84[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 52ms/step - accuracy: 0.6089 - loss: 0.8762
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m32s[0m 99ms/step - accuracy: 0.3750 - loss: 1.0516[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m49/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 32ms/step - accuracy: 0.3711 - loss: 1.2925[32m [repeated 80x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m130/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3341 - loss: 1.7868
[1m132/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3341 - loss: 1.7864[32m [repeated 264x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m67/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 45ms/step - accuracy: 0.4300 - loss: 1.1733
[1m68/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 45ms/step - accuracy: 0.4299 - loss: 1.1734[32m [repeated 67x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.4083 - loss: 1.1732[32m [repeated 183x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.4195 - loss: 1.1752
[1m 29/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.4207 - loss: 1.1760
[1m 31/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 26ms/step - accuracy: 0.4213 - loss: 1.1771[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2:37[0m 2s/step - accuracy: 0.3203 - loss: 1.3131
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 30ms/step - accuracy: 0.3524 - loss: 1.3112[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.2778 - loss: 1.5829
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 37ms/step - accuracy: 0.2748 - loss: 1.5795
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 38ms/step - accuracy: 0.2740 - loss: 1.5769
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.2736 - loss: 1.5772
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3659 - loss: 1.1504 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 25ms/step - accuracy: 0.3800 - loss: 1.1582
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.2759 - loss: 1.5826
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.2761 - loss: 1.5833
[36m(train_cnn_ray_tune pid=3118316)[0m Epoch 57/107[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 38ms/step - accuracy: 0.2753 - loss: 1.5830[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 39ms/step - accuracy: 0.2765 - loss: 1.5834
[1m 25/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.2773 - loss: 1.5835
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4193 - loss: 1.1951  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4106 - loss: 1.2070
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.2784 - loss: 1.5833
[1m 29/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.2793 - loss: 1.5837
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.2820 - loss: 1.5862
[1m 39/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.2827 - loss: 1.5871
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 41/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.2831 - loss: 1.5882
[1m 43/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.2836 - loss: 1.5893
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 47/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.2846 - loss: 1.5917
[1m 49/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.2852 - loss: 1.5925
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 38ms/step - accuracy: 0.2885 - loss: 1.5948
[1m 63/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.2889 - loss: 1.5952
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.2894 - loss: 1.5955
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m9s[0m 37ms/step - accuracy: 0.2899 - loss: 1.5956 
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 39ms/step - accuracy: 0.4609 - loss: 1.1660 
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 54ms/step - accuracy: 0.5781 - loss: 0.8896 
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 61ms/step - accuracy: 0.6080 - loss: 0.8688 - val_accuracy: 0.5342 - val_loss: 0.9533[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 27ms/step - accuracy: 0.4113 - loss: 1.2179 - val_accuracy: 0.4149 - val_loss: 1.1925[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m48/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 31ms/step - accuracy: 0.3703 - loss: 1.2805
[1m50/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 31ms/step - accuracy: 0.3704 - loss: 1.2807
[1m52/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 30ms/step - accuracy: 0.3705 - loss: 1.2808
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 25ms/step - accuracy: 0.3320 - loss: 1.3037 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.3312 - loss: 1.3059
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 105ms/step - accuracy: 0.3047 - loss: 1.3354[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m75/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 48ms/step - accuracy: 0.4323 - loss: 1.1677[32m [repeated 96x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m136/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 24ms/step - accuracy: 0.3220 - loss: 1.4269
[1m139/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 24ms/step - accuracy: 0.3221 - loss: 1.4268[32m [repeated 248x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 7/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.3383 - loss: 1.3040
[1m 9/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 27ms/step - accuracy: 0.3463 - loss: 1.3006[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m228/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.4238 - loss: 1.1907[32m [repeated 181x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step - accuracy: 0.4993 - loss: 0.9999 
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step - accuracy: 0.5203 - loss: 0.9973
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 24ms/step - accuracy: 0.3225 - loss: 1.4267
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 24ms/step - accuracy: 0.3226 - loss: 1.4267
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 24ms/step - accuracy: 0.3226 - loss: 1.4267[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 88ms/step - accuracy: 0.4688 - loss: 1.1170
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.4575 - loss: 1.1232[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.3394 - loss: 1.1566
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3481 - loss: 1.1625
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3561 - loss: 1.1672
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3598 - loss: 1.1732
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 32ms/step - accuracy: 0.2674 - loss: 1.4385  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2846 - loss: 1.4206
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.2895 - loss: 1.4127
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 34ms/step - accuracy: 0.3640 - loss: 1.1768
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3677 - loss: 1.1794
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 33ms/step - accuracy: 0.3707 - loss: 1.1810
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3745 - loss: 1.1818
[36m(train_cnn_ray_tune pid=3118317)[0m Epoch 39/111[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 33ms/step - accuracy: 0.5269 - loss: 1.0881 [32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3780 - loss: 1.1817
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.3807 - loss: 1.1817 
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 46ms/step - accuracy: 0.4462 - loss: 1.1727 
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 61ms/step - accuracy: 0.6047 - loss: 0.8811 - val_accuracy: 0.5848 - val_loss: 0.8796[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.4958 - loss: 1.1377 
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.5013 - loss: 1.1181
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.5017 - loss: 1.0755 
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 23ms/step - accuracy: 0.3240 - loss: 1.5978 - val_accuracy: 0.3597 - val_loss: 1.2573[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:50[0m 1s/step - accuracy: 0.4062 - loss: 1.2235[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m23/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m3s[0m 53ms/step - accuracy: 0.6071 - loss: 0.8811[32m [repeated 84x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m163/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3318 - loss: 1.7595
[1m166/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3319 - loss: 1.7596[32m [repeated 257x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m19/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 35ms/step - accuracy: 0.4817 - loss: 1.0950
[1m21/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 35ms/step - accuracy: 0.4807 - loss: 1.0966[32m [repeated 74x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m251/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m2s[0m 36ms/step - accuracy: 0.3014 - loss: 1.6032[32m [repeated 181x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.2861 - loss: 1.5532 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2914 - loss: 1.5637
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4436 - loss: 1.2058
[1m 20/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4402 - loss: 1.2071
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4372 - loss: 1.2085
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 60ms/step - accuracy: 0.4727 - loss: 1.1153 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 59ms/step - accuracy: 0.4609 - loss: 1.1186
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 85ms/step - accuracy: 0.5938 - loss: 0.9076
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5635 - loss: 0.9480 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 19ms/step - accuracy: 0.5438 - loss: 1.0147
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 19ms/step - accuracy: 0.5441 - loss: 1.0143
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 19ms/step - accuracy: 0.5443 - loss: 1.0139
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.2760 - loss: 1.4717 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.2944 - loss: 1.4556
[36m(train_cnn_ray_tune pid=3118309)[0m Epoch 22/64[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.6077 - loss: 0.9417 
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5404 - loss: 1.0747  
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.5417 - loss: 1.0626
[1m 10/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.5443 - loss: 1.0515
Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-11-05 11:14:15. Total running time: 4min 0s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         tanh                                  128                 32                  3          0.000165933        107                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                 64                  3          0.000386417        121                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   64                 32                  5          1.56689e-05        116                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.00149202          59                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  3          0.000917269        145                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                  128                128                  5          7.16419e-05         66                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.0020272           93                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00213202          64                                              │
│ trial_9f7c8    RUNNING              3   adam            tanh                                  128                128                  3          0.000931045         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         relu                                  128                128                  3          0.00205798         108                                              │
│ trial_9f7c8    TERMINATED           2   adam            tanh                                   32                 32                  5          0.00357251         149        1           199.893          0.366951 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   64                 32                  5          0.00247751         130        1           184.977          0.371222 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 41ms/step - accuracy: 0.4736 - loss: 1.1131 - val_accuracy: 0.5023 - val_loss: 1.0694[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 40ms/step - accuracy: 0.5347 - loss: 1.0832  
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.3902 - loss: 1.2512 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m13s[0m 40ms/step - accuracy: 0.3033 - loss: 1.6006 - val_accuracy: 0.3693 - val_loss: 1.3494[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 34ms/step - accuracy: 0.2760 - loss: 1.5935 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 35ms/step - accuracy: 0.2834 - loss: 1.5826
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3066 - loss: 1.5545
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3082 - loss: 1.5552
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 2/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 62ms/step - accuracy: 0.4512 - loss: 1.1428 
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 112ms/step - accuracy: 0.4531 - loss: 1.1683[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m69/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 33ms/step - accuracy: 0.4758 - loss: 1.1276[32m [repeated 71x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 17ms/step - accuracy: 0.5508 - loss: 1.0042
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 17ms/step - accuracy: 0.5509 - loss: 1.0038[32m [repeated 272x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m17/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 53ms/step - accuracy: 0.5827 - loss: 0.9278
[1m18/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m3s[0m 53ms/step - accuracy: 0.5832 - loss: 0.9259[32m [repeated 78x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m109/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.3067 - loss: 1.6147[32m [repeated 166x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3095 - loss: 1.5553
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3104 - loss: 1.5565
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 21/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 37ms/step - accuracy: 0.3112 - loss: 1.5579
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3120 - loss: 1.5591
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 36ms/step - accuracy: 0.3124 - loss: 1.5614
[1m 28/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 37ms/step - accuracy: 0.3123 - loss: 1.5620
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 30/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.3121 - loss: 1.5628
[1m 32/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.3119 - loss: 1.5632
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.3776 - loss: 1.2805 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 29ms/step - accuracy: 0.3744 - loss: 1.2743[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 36/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.3118 - loss: 1.5632
[1m 38/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 38ms/step - accuracy: 0.3118 - loss: 1.5637
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3116 - loss: 1.5642
[1m 42/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3117 - loss: 1.5646
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m27s[0m 84ms/step - accuracy: 0.5000 - loss: 1.0763
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.4184 - loss: 1.1755 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 44/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3119 - loss: 1.5648
[1m 45/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3120 - loss: 1.5649
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 47/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3123 - loss: 1.5651
[1m 49/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3124 - loss: 1.5654
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 53/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3126 - loss: 1.5667
[1m 55/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3128 - loss: 1.5671
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3216 - loss: 1.3943  
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3214 - loss: 1.4000
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m299/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 28ms/step - accuracy: 0.4013 - loss: 1.2015
[1m301/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 28ms/step - accuracy: 0.4013 - loss: 1.2016
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 28ms/step - accuracy: 0.4013 - loss: 1.2017[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 57/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m10s[0m 37ms/step - accuracy: 0.3129 - loss: 1.5676
[1m 59/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m9s[0m 36ms/step - accuracy: 0.3131 - loss: 1.5679 
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  3/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 26ms/step - accuracy: 0.3333 - loss: 1.7578 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.3238 - loss: 1.7609[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m Epoch 50/116[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.5576 - loss: 1.0508 [32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 38ms/step - accuracy: 0.3719 - loss: 1.2777 - val_accuracy: 0.3909 - val_loss: 1.2536[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m54/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 31ms/step - accuracy: 0.3704 - loss: 1.2731
[1m56/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 31ms/step - accuracy: 0.3704 - loss: 1.2730
[1m58/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 31ms/step - accuracy: 0.3704 - loss: 1.2729
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m10s[0m 27ms/step - accuracy: 0.4149 - loss: 1.1996 - val_accuracy: 0.4951 - val_loss: 1.1097[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 45ms/step - accuracy: 0.6133 - loss: 0.8749 
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 128ms/step - accuracy: 0.3984 - loss: 1.1535[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m11/84[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 35ms/step - accuracy: 0.4556 - loss: 1.1344[32m [repeated 59x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m209/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.3280 - loss: 1.7808
[1m212/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m2s[0m 24ms/step - accuracy: 0.3281 - loss: 1.7802[32m [repeated 243x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m76/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 31ms/step - accuracy: 0.3706 - loss: 1.2726
[1m78/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 31ms/step - accuracy: 0.3707 - loss: 1.2726[32m [repeated 87x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 25ms/step - accuracy: 0.3243 - loss: 1.4071[32m [repeated 206x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 29ms/step - accuracy: 0.3984 - loss: 1.2673 
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 81ms/step - accuracy: 0.5781 - loss: 1.0490
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.5618 - loss: 1.0201 [32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3082 - loss: 1.4060  
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3088 - loss: 1.4106
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3116 - loss: 1.4118
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 96/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3248 - loss: 1.4074
[1m 98/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3248 - loss: 1.4074
[1m100/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 25ms/step - accuracy: 0.3248 - loss: 1.4075[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3864 - loss: 1.4689 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3587 - loss: 1.5094
[36m(train_cnn_ray_tune pid=3118317)[0m Epoch 42/111[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3698 - loss: 1.2528 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  3/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.4462 - loss: 1.0795 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4316 - loss: 1.1061
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 42ms/step - accuracy: 0.4726 - loss: 1.1190 - val_accuracy: 0.5145 - val_loss: 1.0476[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 28ms/step - accuracy: 0.3880 - loss: 1.2513 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 34ms/step - accuracy: 0.3925 - loss: 1.2522
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 27ms/step - accuracy: 0.3310 - loss: 1.7648 - val_accuracy: 0.4011 - val_loss: 1.2200[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m18s[0m 389ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 6/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step  
[1m10/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m14/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m18/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m22/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 86ms/step - accuracy: 0.3906 - loss: 1.5090[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m21/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 45ms/step - accuracy: 0.3920 - loss: 1.1982[32m [repeated 96x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.3351 - loss: 1.6764
[1m 42/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 25ms/step - accuracy: 0.3356 - loss: 1.6780[32m [repeated 248x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m54/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 33ms/step - accuracy: 0.4886 - loss: 1.0967
[1m56/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 33ms/step - accuracy: 0.4886 - loss: 1.0969[32m [repeated 65x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 54/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m3s[0m 29ms/step - accuracy: 0.4352 - loss: 1.1694[32m [repeated 207x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m26/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m30/49[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m34/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 15ms/step
[1m38/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m42/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 15ms/step
[1m46/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3118313)[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=3118313)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 21ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 21ms/step
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 39ms/step - accuracy: 0.4844 - loss: 1.0548 
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 86ms/step - accuracy: 0.5469 - loss: 0.9477
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5417 - loss: 0.9636 
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 56ms/step
[1m 6/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m10/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m14/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m19/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m23/96[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m27/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m31/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[1m36/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 13/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.5628 - loss: 0.9704
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.5661 - loss: 0.9713
[1m 19/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.5690 - loss: 0.9707[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m41/96[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m45/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m49/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m54/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 12ms/step
[1m58/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m62/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 13ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m72/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m77/96[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step
[36m(train_cnn_ray_tune pid=3118297)[0m Epoch 60/66[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m81/96[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step
[1m87/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m91/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:14:29. Total running time: 4min 14s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             250.829 │
│ time_total_s                 250.829 │
│ training_iteration                 1 │
│ val_accuracy                 0.57654 │
╰──────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118313)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 13ms/step

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:14:29. Total running time: 4min 14s
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 18ms/step - accuracy: 0.4238 - loss: 1.1820 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m28s[0m 86ms/step - accuracy: 0.5000 - loss: 1.1455
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4557 - loss: 1.1822 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.2637 - loss: 1.6487
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.2810 - loss: 1.6331
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3026 - loss: 1.5875
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 32ms/step - accuracy: 0.3046 - loss: 1.5833 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 31ms/step - accuracy: 0.2985 - loss: 1.6014 
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3006 - loss: 1.5929[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 50ms/step - accuracy: 0.4178 - loss: 1.1811 - val_accuracy: 0.4573 - val_loss: 1.1197[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 36ms/step - accuracy: 0.4253 - loss: 1.1804 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 37ms/step - accuracy: 0.4266 - loss: 1.1805[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 26ms/step - accuracy: 0.3952 - loss: 1.2181 - val_accuracy: 0.4258 - val_loss: 1.1758[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 86ms/step - accuracy: 0.4375 - loss: 1.2685[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 40ms/step - accuracy: 0.4387 - loss: 1.1682[32m [repeated 54x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m104/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 17ms/step - accuracy: 0.5775 - loss: 0.9721
[1m108/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 17ms/step - accuracy: 0.5772 - loss: 0.9723[32m [repeated 265x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m44/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 39ms/step - accuracy: 0.4402 - loss: 1.1664
[1m45/84[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 40ms/step - accuracy: 0.4404 - loss: 1.1661[32m [repeated 63x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m272/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 22ms/step - accuracy: 0.3450 - loss: 1.7078[32m [repeated 175x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 17ms/step - accuracy: 0.5791 - loss: 0.9716
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 17ms/step - accuracy: 0.5789 - loss: 0.9716
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 17ms/step - accuracy: 0.5787 - loss: 0.9717
[36m(train_cnn_ray_tune pid=3118312)[0m Epoch 56/93[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 14ms/step - accuracy: 0.3134 - loss: 1.5515 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.3153 - loss: 1.5578
[1m 11/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.3121 - loss: 1.5684
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3848 - loss: 1.5820 
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 77ms/step - accuracy: 0.5625 - loss: 0.9517
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.5684 - loss: 0.9435 [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3311 - loss: 1.3988 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3327 - loss: 1.3878[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 34ms/step - accuracy: 0.3863 - loss: 1.2702 - val_accuracy: 0.4005 - val_loss: 1.2376[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 32ms/step - accuracy: 0.3811 - loss: 1.2240 
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.3723 - loss: 1.2455[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m9s[0m 26ms/step - accuracy: 0.3443 - loss: 1.7114 - val_accuracy: 0.4047 - val_loss: 1.2149[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[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=3118317)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 18ms/step - accuracy: 0.6208 - loss: 0.9260 
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 76ms/step - accuracy: 0.7031 - loss: 0.8173[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m64/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 42ms/step - accuracy: 0.4419 - loss: 1.1525[32m [repeated 68x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4093 - loss: 1.2233
[1m268/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m1s[0m 21ms/step - accuracy: 0.4094 - loss: 1.2232[32m [repeated 294x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m73/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 29ms/step - accuracy: 0.3913 - loss: 1.2507
[1m75/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 29ms/step - accuracy: 0.3912 - loss: 1.2509[32m [repeated 62x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 99/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m2s[0m 42ms/step - accuracy: 0.4962 - loss: 1.0865[32m [repeated 156x across cluster][0m
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 324ms/step
[1m 7/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step   
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m13/49[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m20/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m26/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 9ms/step
[1m33/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 9ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m37/49[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step
[1m42/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m48/49[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 14ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 45ms/step
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 241ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m 6/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[1m13/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step 
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 7/49[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step   
[1m15/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m19/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step
[1m25/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118317)[0m 
[1m31/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step 
[1m37/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:14:39. Total running time: 4min 24s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             260.985 │
│ time_total_s                 260.985 │
│ training_iteration                 1 │
│ val_accuracy                 0.39685 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:14:39. Total running time: 4min 24s
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 41ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m Epoch 45/72[32m [repeated 12x across cluster][0m

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:14:39. Total running time: 4min 24s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             261.789 │
│ time_total_s                 261.789 │
│ training_iteration                 1 │
│ val_accuracy                 0.36597 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:14:39. Total running time: 4min 24s
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 114ms/step - accuracy: 0.4453 - loss: 1.1025
[1m 3/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 39ms/step - accuracy: 0.4466 - loss: 1.1370 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4316 - loss: 1.2404 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.4288 - loss: 1.2270[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 31ms/step - accuracy: 0.5035 - loss: 1.0934 - val_accuracy: 0.5338 - val_loss: 1.0189[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m12s[0m 34ms/step - accuracy: 0.3154 - loss: 1.5362 - val_accuracy: 0.3702 - val_loss: 1.3283[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 20ms/step - accuracy: 0.3509 - loss: 1.5060 [32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 75ms/step - accuracy: 0.4375 - loss: 1.6192[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m17/84[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 30ms/step - accuracy: 0.4496 - loss: 1.1423[32m [repeated 46x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 18ms/step - accuracy: 0.4417 - loss: 1.1713
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 18ms/step - accuracy: 0.4417 - loss: 1.1713[32m [repeated 217x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m13/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.4503 - loss: 1.1419
[1m15/84[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step - accuracy: 0.4500 - loss: 1.1422[32m [repeated 67x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m151/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m2s[0m 15ms/step - accuracy: 0.4130 - loss: 1.2137[32m [repeated 124x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118299)[0m 
[1m 8/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step 
[1m16/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3118297)[0m Epoch 65/66[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m101/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 26ms/step - accuracy: 0.3238 - loss: 1.5277
[1m103/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 26ms/step - accuracy: 0.3239 - loss: 1.5276
[1m105/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m5s[0m 26ms/step - accuracy: 0.3240 - loss: 1.5274
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5:42[0m 2s/step - accuracy: 0.5625 - loss: 1.0070
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 13ms/step - accuracy: 0.5560 - loss: 1.0136[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 13ms/step - accuracy: 0.5257 - loss: 1.0162 
[1m 10/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step - accuracy: 0.5397 - loss: 0.9915[32m [repeated 5x across cluster][0m

Trial status: 12 RUNNING | 8 TERMINATED
Current time: 2025-11-05 11:14:45. Total running time: 4min 30s
Logical resource usage: 12.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              4   rmsprop         tanh                                  128                 32                  3          0.000165933        107                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                 64                  3          0.000386417        121                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.00149202          59                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  3          0.000917269        145                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                  128                128                  5          7.16419e-05         66                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         relu                                   64                 32                  5          0.0020272           93                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00213202          64                                              │
│ trial_9f7c8    RUNNING              3   adam            tanh                                  128                128                  3          0.000931045         72                                              │
│ trial_9f7c8    TERMINATED           2   adam            tanh                                   32                 32                  5          0.00357251         149        1           199.893          0.366951 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          1.56689e-05        116        1           261.789          0.365966 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   64                 32                  5          0.00247751         130        1           184.977          0.371222 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111        1           260.985          0.396846 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
│ trial_9f7c8    TERMINATED           4   rmsprop         relu                                  128                128                  3          0.00205798         108        1           250.829          0.576544 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 30ms/step - accuracy: 0.4962 - loss: 1.0908 - val_accuracy: 0.5388 - val_loss: 0.9931[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 225ms/step
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 9/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step   
[1m19/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 33ms/step
[1m10/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step 
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 27ms/step - accuracy: 0.4315 - loss: 1.1749 - val_accuracy: 0.4432 - val_loss: 1.1356[32m [repeated 8x across cluster][0m

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:14:47. Total running time: 4min 32s
[36m(train_cnn_ray_tune pid=3118312)[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=3118312)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             269.098 │
│ time_total_s                 269.098 │
│ training_iteration                 1 │
│ val_accuracy                 0.59363 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:14:47. Total running time: 4min 32s
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 63ms/step - accuracy: 0.4688 - loss: 1.1783[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m64/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 23ms/step - accuracy: 0.5011 - loss: 1.0784[32m [repeated 45x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m300/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 17ms/step - accuracy: 0.3424 - loss: 1.6867
[1m304/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 17ms/step - accuracy: 0.3423 - loss: 1.6867[32m [repeated 246x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m76/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 30ms/step - accuracy: 0.4487 - loss: 1.1488
[1m78/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 30ms/step - accuracy: 0.4488 - loss: 1.1486[32m [repeated 77x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m226/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m2s[0m 25ms/step - accuracy: 0.3249 - loss: 1.5151[32m [repeated 113x across cluster][0m
[36m(train_cnn_ray_tune pid=3118312)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 226ms/step
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 40ms/step
[1m 8/96[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step 
[36m(train_cnn_ray_tune pid=3118315)[0m Epoch 26/72[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m103/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.5487 - loss: 1.0127
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.5490 - loss: 1.0122
[1m111/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.5493 - loss: 1.0117[32m [repeated 2x across cluster][0m

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:14:49. Total running time: 4min 34s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              271.66 │
│ time_total_s                  271.66 │
│ training_iteration                 1 │
│ val_accuracy                 0.54008 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:14:49. Total running time: 4min 34s
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 76ms/step - accuracy: 0.4141 - loss: 1.2205
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step - accuracy: 0.4215 - loss: 1.2339[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 12/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.5817 - loss: 0.9463
[1m 17/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.5820 - loss: 0.9468
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.3132 - loss: 1.5343 
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 15ms/step - accuracy: 0.3691 - loss: 1.5748 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 15ms/step - accuracy: 0.3675 - loss: 1.6132[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 32ms/step - accuracy: 0.4467 - loss: 1.1485 - val_accuracy: 0.4359 - val_loss: 1.1171[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m 8/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 8ms/step   
[1m17/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m6s[0m 17ms/step - accuracy: 0.4098 - loss: 1.2105 - val_accuracy: 0.4244 - val_loss: 1.1632[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m60/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4713 - loss: 1.1074
[1m62/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4710 - loss: 1.1080
[1m64/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4708 - loss: 1.1085
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 76ms/step - accuracy: 0.6250 - loss: 0.9031[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m66/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 26ms/step - accuracy: 0.4705 - loss: 1.1089[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m275/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step - accuracy: 0.3447 - loss: 1.6547
[1m279/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 13ms/step - accuracy: 0.3446 - loss: 1.6546[32m [repeated 238x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m50/84[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 17ms/step - accuracy: 0.4113 - loss: 1.2270
[1m53/84[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 17ms/step - accuracy: 0.4111 - loss: 1.2269[32m [repeated 59x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 96/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m4s[0m 19ms/step - accuracy: 0.3292 - loss: 1.4878[32m [repeated 88x across cluster][0m
[36m(train_cnn_ray_tune pid=3118297)[0m 
[1m90/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 7ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m36/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.4558 - loss: 1.1160
[1m38/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 23ms/step - accuracy: 0.4559 - loss: 1.1160
[1m40/84[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 24ms/step - accuracy: 0.4560 - loss: 1.1161
[36m(train_cnn_ray_tune pid=3118311)[0m Epoch 23/59[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 29/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 14ms/step - accuracy: 0.4837 - loss: 1.1378
[1m 33/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 14ms/step - accuracy: 0.4779 - loss: 1.1423
[1m 37/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 14ms/step - accuracy: 0.4728 - loss: 1.1457
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 41/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.4273 - loss: 1.2024
[1m 44/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.4271 - loss: 1.2031
[1m 47/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 17ms/step - accuracy: 0.4266 - loss: 1.2040
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 62ms/step - accuracy: 0.5000 - loss: 1.0666
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 17ms/step - accuracy: 0.4766 - loss: 1.1045 [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step - accuracy: 0.5595 - loss: 0.9899 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  4/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4046 - loss: 1.1830 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4162 - loss: 1.1824[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 30ms/step - accuracy: 0.4576 - loss: 1.1186 - val_accuracy: 0.4566 - val_loss: 1.0958[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 16ms/step - accuracy: 0.4430 - loss: 1.1641 - val_accuracy: 0.4908 - val_loss: 1.0902[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 13/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5288 - loss: 0.9929
[1m 20/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5374 - loss: 0.9881
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 25/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5422 - loss: 0.9865
[1m 30/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5456 - loss: 0.9848
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5474 - loss: 0.9848
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.5484 - loss: 0.9857
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.5491 - loss: 0.9861
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 66ms/step - accuracy: 0.4062 - loss: 1.2089[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m62/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 24ms/step - accuracy: 0.4566 - loss: 1.1199[32m [repeated 25x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m261/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 14ms/step - accuracy: 0.3409 - loss: 1.6480
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 14ms/step - accuracy: 0.3409 - loss: 1.6480[32m [repeated 241x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m65/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 24ms/step - accuracy: 0.4565 - loss: 1.1199
[1m68/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 24ms/step - accuracy: 0.4566 - loss: 1.1199[32m [repeated 52x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 18ms/step - accuracy: 0.4339 - loss: 1.1832[32m [repeated 88x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m Epoch 28/72[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.4285 - loss: 1.1910
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.4290 - loss: 1.1901
[1m 49/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.4294 - loss: 1.1892[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 66ms/step - accuracy: 0.4062 - loss: 1.6510
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 13ms/step - accuracy: 0.3825 - loss: 1.6092 [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 19ms/step - accuracy: 0.4466 - loss: 1.2518 
[1m  8/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 17ms/step - accuracy: 0.4510 - loss: 1.2271[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 23ms/step - accuracy: 0.4128 - loss: 1.2178 - val_accuracy: 0.4547 - val_loss: 1.1899[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 24/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5710 - loss: 0.9398
[1m 30/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5716 - loss: 0.9413
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5719 - loss: 0.9432
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5717 - loss: 0.9470
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 47/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5718 - loss: 0.9495
[1m 53/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5718 - loss: 0.9517
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 22ms/step - accuracy: 0.4374 - loss: 1.1757 - val_accuracy: 0.4645 - val_loss: 1.1380[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 58/167[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5718 - loss: 0.9530
[1m 64/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5721 - loss: 0.9540
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.3935 - loss: 1.2188 
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5723 - loss: 0.9550
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5723 - loss: 0.9555
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5724 - loss: 0.9560
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5724 - loss: 0.9565
[1m 92/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 10ms/step - accuracy: 0.5724 - loss: 0.9570
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m20s[0m 63ms/step - accuracy: 0.4688 - loss: 1.3734[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m83/84[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 18ms/step - accuracy: 0.4166 - loss: 1.2197[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m252/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 13ms/step - accuracy: 0.3411 - loss: 1.6193
[1m256/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m1s[0m 13ms/step - accuracy: 0.3410 - loss: 1.6195[32m [repeated 227x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m68/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4686 - loss: 1.1089
[1m70/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4685 - loss: 1.1092[32m [repeated 59x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 19ms/step - accuracy: 0.4355 - loss: 1.1597[32m [repeated 89x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m72/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4683 - loss: 1.1096
[1m74/84[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4682 - loss: 1.1100
[1m76/84[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 25ms/step - accuracy: 0.4681 - loss: 1.1103
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m31/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 26ms/step - accuracy: 0.4649 - loss: 1.1210
[1m33/84[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 26ms/step - accuracy: 0.4653 - loss: 1.1211
[1m35/84[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 26ms/step - accuracy: 0.4658 - loss: 1.1210
[36m(train_cnn_ray_tune pid=3118322)[0m Epoch 69/145[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 14ms/step - accuracy: 0.4192 - loss: 1.2121
[1m192/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 14ms/step - accuracy: 0.4192 - loss: 1.2119
[1m196/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m1s[0m 14ms/step - accuracy: 0.4192 - loss: 1.2117[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 82ms/step - accuracy: 0.4297 - loss: 1.1827
[1m 4/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step - accuracy: 0.4256 - loss: 1.1940[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 24/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5767 - loss: 0.9780
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 29/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.5754 - loss: 0.9733
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.5741 - loss: 0.9715
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.4856 - loss: 1.1192 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.4833 - loss: 1.1316[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 30ms/step - accuracy: 0.4682 - loss: 1.1180 - val_accuracy: 0.4258 - val_loss: 1.1199[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m102/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5669 - loss: 0.9652
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5670 - loss: 0.9649
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 10ms/step - accuracy: 0.5670 - loss: 0.9647
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 10ms/step - accuracy: 0.5670 - loss: 0.9645
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 29ms/step - accuracy: 0.5465 - loss: 1.0205 - val_accuracy: 0.5013 - val_loss: 1.0438[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[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=3118310)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 14ms/step - accuracy: 0.3629 - loss: 1.4057 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 70ms/step - accuracy: 0.5078 - loss: 1.1895[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m63/84[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 24ms/step - accuracy: 0.4764 - loss: 1.1166[32m [repeated 27x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m245/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 13ms/step - accuracy: 0.3375 - loss: 1.6177
[1m249/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m1s[0m 13ms/step - accuracy: 0.3373 - loss: 1.6179[32m [repeated 230x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m69/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4762 - loss: 1.1164
[1m71/84[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 25ms/step - accuracy: 0.4761 - loss: 1.1164[32m [repeated 50x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 26/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 28ms/step - accuracy: 0.5629 - loss: 0.9997[32m [repeated 100x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m13s[0m 274ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 9/49[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step   
[1m18/49[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m27/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 6ms/step
[1m36/49[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m45/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 38ms/step
[36m(train_cnn_ray_tune pid=3118320)[0m Epoch 43/121[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m10/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step 
[1m19/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m28/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[1m36/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m44/96[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 6ms/step
[1m52/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m60/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 17/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.4365 - loss: 1.1329
[1m 20/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.4357 - loss: 1.1380
[1m 23/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 17ms/step - accuracy: 0.4346 - loss: 1.1423[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 6ms/step
[1m76/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m84/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 7ms/step
[1m92/96[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step
[36m(train_cnn_ray_tune pid=3118310)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 7ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:15:10. Total running time: 4min 55s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             291.745 │
│ time_total_s                 291.745 │
│ training_iteration                 1 │
│ val_accuracy                 0.43298 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:15:10. Total running time: 4min 55s
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/84[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 71ms/step - accuracy: 0.4375 - loss: 1.1699
[1m 5/84[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 17ms/step - accuracy: 0.4152 - loss: 1.1814[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 13ms/step - accuracy: 0.4100 - loss: 1.3908 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 14ms/step - accuracy: 0.3903 - loss: 1.4351[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 20ms/step - accuracy: 0.4198 - loss: 1.2120 - val_accuracy: 0.4543 - val_loss: 1.1908[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5603 - loss: 0.9854 
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5603 - loss: 0.9850
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m141/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5604 - loss: 0.9846
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 9ms/step - accuracy: 0.5604 - loss: 0.9842
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 9ms/step - accuracy: 0.5605 - loss: 0.9838
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step - accuracy: 0.5606 - loss: 0.9835
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step - accuracy: 0.5609 - loss: 0.9829 - val_accuracy: 0.6041 - val_loss: 0.8667
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 14ms/step - accuracy: 0.4184 - loss: 1.2065 - val_accuracy: 0.4169 - val_loss: 1.1639[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.5378 - loss: 0.9893 
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 198ms/step
[1m17/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step  
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 15ms/step - accuracy: 0.4090 - loss: 1.2081 
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 38ms/step
[1m13/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m21s[0m 66ms/step - accuracy: 0.2812 - loss: 1.3935[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m22/84[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 13ms/step - accuracy: 0.4176 - loss: 1.2160[32m [repeated 16x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 20/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.4064 - loss: 1.1938
[1m 23/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 16ms/step - accuracy: 0.4089 - loss: 1.1911[32m [repeated 217x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m57/84[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 14ms/step - accuracy: 0.4229 - loss: 1.2125
[1m60/84[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 14ms/step - accuracy: 0.4234 - loss: 1.2121[32m [repeated 27x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m282/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 11ms/step - accuracy: 0.3489 - loss: 1.5710[32m [repeated 101x across cluster][0m

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:15:13. Total running time: 4min 58s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             295.091 │
│ time_total_s                 295.091 │
│ training_iteration                 1 │
│ val_accuracy                 0.60414 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:15:13. Total running time: 4min 58s
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 346ms/step
[36m(train_cnn_ray_tune pid=3118322)[0m 
[1m87/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 4ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m11/49[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step   
[1m22/49[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 5ms/step
[1m32/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=3118315)[0m Epoch 31/72[32m [repeated 11x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[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=3118316)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 41ms/step
[1m11/96[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 5ms/step 
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m154/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.4525 - loss: 1.1394
[1m159/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.4523 - loss: 1.1397
[1m165/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.4519 - loss: 1.1401[32m [repeated 4x across cluster][0m

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:15:15. Total running time: 5min 0s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             296.992 │
│ time_total_s                 296.992 │
│ training_iteration                 1 │
│ val_accuracy                 0.45729 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:15:15. Total running time: 5min 0s
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 59ms/step - accuracy: 0.4062 - loss: 1.2984
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 11ms/step - accuracy: 0.3911 - loss: 1.5132 [32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m180/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.3387 - loss: 1.6120

Trial status: 7 RUNNING | 13 TERMINATED
Current time: 2025-11-05 11:15:15. Total running time: 5min 0s
Logical resource usage: 7.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                 64                  3          0.000386417        121                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         tanh                                   32                128                  3          0.00149202          59                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    RUNNING              2   rmsprop         tanh                                   32                128                  3          0.00213202          64                                              │
│ trial_9f7c8    TERMINATED           2   adam            tanh                                   32                 32                  5          0.00357251         149        1           199.893          0.366951 │
│ trial_9f7c8    TERMINATED           4   rmsprop         tanh                                  128                 32                  3          0.000165933        107        1           296.992          0.457293 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          1.56689e-05        116        1           261.789          0.365966 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   64                 32                  5          0.00247751         130        1           184.977          0.371222 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  3          0.000917269        145        1           295.091          0.604139 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111        1           260.985          0.396846 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                  128                128                  5          7.16419e-05         66        1           271.66           0.540079 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.0020272           93        1           269.098          0.593627 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
│ trial_9f7c8    TERMINATED           3   adam            tanh                                  128                128                  3          0.000931045         72        1           291.746          0.432983 │
│ trial_9f7c8    TERMINATED           4   rmsprop         relu                                  128                128                  3          0.00205798         108        1           250.829          0.576544 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  4/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 18ms/step - accuracy: 0.3229 - loss: 1.4040 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 18ms/step - accuracy: 0.3244 - loss: 1.4169[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m9s[0m 203ms/step
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m84/84[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 18ms/step - accuracy: 0.4251 - loss: 1.2110 - val_accuracy: 0.4573 - val_loss: 1.1834[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m156/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.3385 - loss: 1.6140 
[1m162/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.3386 - loss: 1.6136[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m15/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step  
[1m30/49[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 4ms/step
[1m46/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m247/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.3388 - loss: 1.6068
[1m254/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.3389 - loss: 1.6064[32m [repeated 15x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 33ms/step
[1m16/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step 
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m5s[0m 14ms/step - accuracy: 0.4113 - loss: 1.1961 - val_accuracy: 0.4192 - val_loss: 1.1699[32m [repeated 9x across cluster][0m

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:15:17. Total running time: 5min 2s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             298.736 │
│ time_total_s                 298.736 │
│ training_iteration                 1 │
│ val_accuracy                 0.40407 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:15:17. Total running time: 5min 2s
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 10ms/step - accuracy: 0.3389 - loss: 1.6036 - val_accuracy: 0.4037 - val_loss: 1.2032
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  8/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.4408 - loss: 1.4206  
[1m 15/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.4010 - loss: 1.4852
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 58ms/step - accuracy: 0.2812 - loss: 1.3819[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 11/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.4466 - loss: 1.1985
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.4471 - loss: 1.1955[32m [repeated 181x across cluster][0m
[36m(train_cnn_ray_tune pid=3118316)[0m 
[1m80/84[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 15ms/step - accuracy: 0.4249 - loss: 1.2110
[1m83/84[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 15ms/step - accuracy: 0.4250 - loss: 1.2110[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.4301 - loss: 1.1934 [32m [repeated 53x across cluster][0m
[36m(train_cnn_ray_tune pid=3118309)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m Epoch 32/73[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m139/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.4412 - loss: 1.1676
[1m143/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step - accuracy: 0.4414 - loss: 1.1675
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step - accuracy: 0.4415 - loss: 1.1674
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m19s[0m 59ms/step - accuracy: 0.4062 - loss: 1.1384
[1m  8/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.4381 - loss: 1.1314  [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m204/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m1s[0m 8ms/step - accuracy: 0.4418 - loss: 1.1554[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.2697 - loss: 1.6723  
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  5/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 13ms/step - accuracy: 0.2705 - loss: 1.4316 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 13ms/step - accuracy: 0.2801 - loss: 1.4259
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 89/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.3362 - loss: 1.5722
[1m 95/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.3365 - loss: 1.5714[32m [repeated 89x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[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=3118311)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 12ms/step - accuracy: 0.4373 - loss: 1.1620 - val_accuracy: 0.4491 - val_loss: 1.1155[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 9ms/step - accuracy: 0.4421 - loss: 1.1552 - val_accuracy: 0.4836 - val_loss: 1.0633[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 9ms/step - accuracy: 0.4298 - loss: 1.1383  
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.4214 - loss: 1.1563
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m17s[0m 53ms/step - accuracy: 0.3750 - loss: 1.3826[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 66/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.4354 - loss: 1.1477
[1m 71/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.4355 - loss: 1.1486[32m [repeated 103x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 52/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m3s[0m 12ms/step - accuracy: 0.3410 - loss: 1.3714[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 76/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.4592 - loss: 1.1366
[1m 82/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.4591 - loss: 1.1372
[1m 88/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.4594 - loss: 1.1373
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 51ms/step - accuracy: 0.4688 - loss: 1.1424
[1m  6/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.4326 - loss: 1.1569
[1m 11/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.4322 - loss: 1.1532
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m11s[0m 243ms/step
[36m(train_cnn_ray_tune pid=3118320)[0m Epoch 49/121[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m15/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step   
[1m29/49[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m43/49[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m15/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step 
[1m29/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m42/96[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 4ms/step
[1m56/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.5006 - loss: 1.0963  
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 8ms/step - accuracy: 0.4858 - loss: 1.0995
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 4ms/step
[1m84/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 4ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:15:25. Total running time: 5min 10s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             307.661 │
│ time_total_s                 307.661 │
│ training_iteration                 1 │
│ val_accuracy                 0.42083 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:15:25. Total running time: 5min 10s
[36m(train_cnn_ray_tune pid=3118311)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 48ms/step - accuracy: 0.5938 - loss: 0.9626
[1m  5/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 13ms/step - accuracy: 0.5783 - loss: 0.9986[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m260/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.3481 - loss: 1.5390[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  6/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 12ms/step - accuracy: 0.3465 - loss: 1.3892 
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step - accuracy: 0.4621 - loss: 1.1607 
[1m 18/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step - accuracy: 0.4522 - loss: 1.1650
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.4442 - loss: 1.1646
[1m 50/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.4421 - loss: 1.1649[32m [repeated 90x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/step - accuracy: 0.5765 - loss: 0.9559 - val_accuracy: 0.5158 - val_loss: 1.0018[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m3s[0m 8ms/step - accuracy: 0.3482 - loss: 1.5369 - val_accuracy: 0.4001 - val_loss: 1.2018[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 47ms/step - accuracy: 0.3438 - loss: 1.2584[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.5855 - loss: 0.9511
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.5853 - loss: 0.9509[32m [repeated 81x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 60/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step - accuracy: 0.5862 - loss: 0.9580[32m [repeated 28x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.4082 - loss: 1.2012  
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.4250 - loss: 1.1740
[36m(train_cnn_ray_tune pid=3118320)[0m Epoch 52/121[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 10ms/step - accuracy: 0.3616 - loss: 1.3769 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 10ms/step - accuracy: 0.3707 - loss: 1.3676
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.3306 - loss: 1.5103  
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.3437 - loss: 1.5086
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 51ms/step - accuracy: 0.3438 - loss: 1.2498
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.4301 - loss: 1.2097  [32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m243/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 6ms/step - accuracy: 0.3509 - loss: 1.5003[32m [repeated 19x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m111/333[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.3642 - loss: 1.3528 
[1m117/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.3636 - loss: 1.3530[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m168/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.4654 - loss: 1.1243
[1m177/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 6ms/step - accuracy: 0.4655 - loss: 1.1241[32m [repeated 119x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.3197 - loss: 1.5445  
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.3245 - loss: 1.5527
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 13ms/step - accuracy: 0.5820 - loss: 0.9381 - val_accuracy: 0.5227 - val_loss: 0.9966[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 7ms/step - accuracy: 0.3514 - loss: 1.5007 - val_accuracy: 0.3991 - val_loss: 1.2008[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 44ms/step - accuracy: 0.2188 - loss: 1.6173[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m148/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 12ms/step - accuracy: 0.5821 - loss: 0.9145
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 12ms/step - accuracy: 0.5821 - loss: 0.9149[32m [repeated 44x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m107/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 12ms/step - accuracy: 0.5813 - loss: 0.9117[32m [repeated 12x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.3193 - loss: 1.4865  
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.3227 - loss: 1.4944
[36m(train_cnn_ray_tune pid=3118315)[0m Epoch 38/72[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m16s[0m 50ms/step - accuracy: 0.5000 - loss: 1.2431
[1m  7/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 10ms/step - accuracy: 0.4223 - loss: 1.3082 [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 8ms/step - accuracy: 0.4430 - loss: 1.1459[32m [repeated 22x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.4208 - loss: 1.1302 
[1m 13/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.4263 - loss: 1.1381[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step - accuracy: 0.4481 - loss: 1.1439
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 9ms/step - accuracy: 0.4482 - loss: 1.1439[32m [repeated 128x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3107 - loss: 1.5368  
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3067 - loss: 1.5507[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/step - accuracy: 0.5726 - loss: 0.9427 - val_accuracy: 0.5555 - val_loss: 0.9346[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m4s[0m 11ms/step - accuracy: 0.3582 - loss: 1.3459 - val_accuracy: 0.3745 - val_loss: 1.2785[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 46ms/step - accuracy: 0.6094 - loss: 0.8642[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 59/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 10ms/step - accuracy: 0.3468 - loss: 1.3333
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m2s[0m 9ms/step - accuracy: 0.3460 - loss: 1.3348 [32m [repeated 44x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 12ms/step - accuracy: 0.5940 - loss: 0.9263[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m Epoch 40/72[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m14s[0m 44ms/step - accuracy: 0.5625 - loss: 0.9158
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.4576 - loss: 1.1276  [32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 6ms/step - accuracy: 0.4485 - loss: 1.1161[32m [repeated 21x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.4380 - loss: 1.1434 
[1m 14/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.4368 - loss: 1.1404
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.4478 - loss: 1.1397
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 9ms/step - accuracy: 0.4481 - loss: 1.1399[32m [repeated 123x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m  9/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.3456 - loss: 1.4347  
[1m 17/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 7ms/step - accuracy: 0.3536 - loss: 1.4367[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[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=3118320)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 14ms/step - accuracy: 0.5958 - loss: 0.9095 - val_accuracy: 0.5792 - val_loss: 0.8836[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m160/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.3565 - loss: 1.3443 
[1m167/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.3568 - loss: 1.3440
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m12s[0m 262ms/step
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m16/49[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step   
[1m32/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step - accuracy: 0.4496 - loss: 1.1409 - val_accuracy: 0.4448 - val_loss: 1.1055[32m [repeated 8x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 8ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 9ms/step
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 46ms/step - accuracy: 0.3750 - loss: 1.1705[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.5971 - loss: 0.8969
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 11ms/step - accuracy: 0.5954 - loss: 0.8998[32m [repeated 46x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 12ms/step - accuracy: 0.6100 - loss: 0.8783[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 31ms/step
[1m17/96[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step 
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m34/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 3ms/step
[1m50/96[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 3ms/step
[1m84/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 3ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:15:43. Total running time: 5min 28s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              325.24 │
│ time_total_s                  325.24 │
│ training_iteration                 1 │
│ val_accuracy                 0.44481 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:15:43. Total running time: 5min 28s
[36m(train_cnn_ray_tune pid=3118320)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m Epoch 44/137[32m [repeated 10x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m15s[0m 46ms/step - accuracy: 0.5000 - loss: 1.0621
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.5062 - loss: 1.0396  [32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 5ms/step - accuracy: 0.4772 - loss: 1.1174[32m [repeated 7x across cluster][0m

Trial status: 4 RUNNING | 16 TERMINATED
Current time: 2025-11-05 11:15:46. Total running time: 5min 30s
Logical resource usage: 4.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            relu                                   64                128                  5          0.00168541         137                                              │
│ trial_9f7c8    RUNNING              2   adam            relu                                   32                128                  3          1.05278e-05         72                                              │
│ trial_9f7c8    RUNNING              3   rmsprop         relu                                   32                 64                  3          0.000159733         73                                              │
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    TERMINATED           2   adam            tanh                                   32                 32                  5          0.00357251         149        1           199.893          0.366951 │
│ trial_9f7c8    TERMINATED           4   rmsprop         tanh                                  128                 32                  3          0.000165933        107        1           296.992          0.457293 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           4   adam            relu                                   64                 64                  3          0.000386417        121        1           325.24           0.444809 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          1.56689e-05        116        1           261.789          0.365966 │
│ trial_9f7c8    TERMINATED           3   rmsprop         tanh                                   32                128                  3          0.00149202          59        1           307.661          0.420828 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   64                 32                  5          0.00247751         130        1           184.977          0.371222 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  3          0.000917269        145        1           295.091          0.604139 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111        1           260.985          0.396846 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                  128                128                  5          7.16419e-05         66        1           271.66           0.540079 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.0020272           93        1           269.098          0.593627 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00213202          64        1           298.736          0.404074 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
│ trial_9f7c8    TERMINATED           3   adam            tanh                                  128                128                  3          0.000931045         72        1           291.746          0.432983 │
│ trial_9f7c8    TERMINATED           4   rmsprop         relu                                  128                128                  3          0.00205798         108        1           250.829          0.576544 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 84/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3620 - loss: 1.4256
[1m 93/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3615 - loss: 1.4273[32m [repeated 110x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 11/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 5ms/step - accuracy: 0.2811 - loss: 1.6058  
[1m 22/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 5ms/step - accuracy: 0.2978 - loss: 1.5918
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 12ms/step - accuracy: 0.6108 - loss: 0.8904 - val_accuracy: 0.5499 - val_loss: 0.9066[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[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=3118318)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 7ms/step - accuracy: 0.4686 - loss: 1.1160 - val_accuracy: 0.4980 - val_loss: 1.0470[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 221ms/step
[1m23/49[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 2ms/step   
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m45/49[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 47ms/step - accuracy: 0.6250 - loss: 0.9217[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 10ms/step - accuracy: 0.6005 - loss: 0.8889
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step - accuracy: 0.6005 - loss: 0.8887[32m [repeated 34x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m142/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 10ms/step - accuracy: 0.6006 - loss: 0.8892[32m [repeated 13x across cluster][0m
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 6ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 6ms/step
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 24ms/step
[1m32/96[0m [32m━━━━━━[0m[37m━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step 
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3410 - loss: 1.4833  
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 5ms/step - accuracy: 0.3477 - loss: 1.4786
[36m(train_cnn_ray_tune pid=3118318)[0m 
[1m64/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step
[1m89/96[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 2ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:15:48. Total running time: 5min 33s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             330.557 │
│ time_total_s                 330.557 │
│ training_iteration                 1 │
│ val_accuracy                 0.49803 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:15:48. Total running time: 5min 33s
[36m(train_cnn_ray_tune pid=3118307)[0m Epoch 46/137[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 43ms/step - accuracy: 0.5156 - loss: 1.0147
[1m  7/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.5915 - loss: 0.9315[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 94/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.3421 - loss: 1.3185[32m [repeated 24x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m302/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 5ms/step - accuracy: 0.3596 - loss: 1.4352
[1m314/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 5ms/step - accuracy: 0.3593 - loss: 1.4354[32m [repeated 95x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step - accuracy: 0.5990 - loss: 0.9243 
[1m 15/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step - accuracy: 0.5927 - loss: 0.9207
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 141ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 11ms/step - accuracy: 0.6005 - loss: 0.8887 - val_accuracy: 0.5811 - val_loss: 0.8829
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m31/49[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 2ms/step  
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 24ms/step
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m34/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step 
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 2ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:15:52. Total running time: 5min 37s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             334.862 │
│ time_total_s                 334.862 │
│ training_iteration                 1 │
│ val_accuracy                 0.39159 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:15:52. Total running time: 5min 37s
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 6ms/step - accuracy: 0.3588 - loss: 1.4358 - val_accuracy: 0.3916 - val_loss: 1.2004[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118315)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 32ms/step - accuracy: 0.3125 - loss: 1.3448[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 25/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 10ms/step - accuracy: 0.6073 - loss: 0.9158
[1m 31/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 9ms/step - accuracy: 0.6086 - loss: 0.9093 [32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 8ms/step - accuracy: 0.6294 - loss: 0.8343 
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.6068 - loss: 0.9010 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 6ms/step - accuracy: 0.3581 - loss: 1.3064  
[1m 18/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3569 - loss: 1.3103[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step - accuracy: 0.6209 - loss: 0.8750 
[1m 17/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.6154 - loss: 0.8773
[36m(train_cnn_ray_tune pid=3118307)[0m Epoch 50/137[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.4062 - loss: 1.2789
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3885 - loss: 1.3327  
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step - accuracy: 0.6009 - loss: 0.8812[32m [repeated 14x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.5971 - loss: 0.8887 
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.6022 - loss: 0.8735
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 96/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.6066 - loss: 0.8599
[1m103/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.6066 - loss: 0.8596[32m [repeated 73x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 13/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 5ms/step - accuracy: 0.3512 - loss: 1.3200 
[1m 23/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 5ms/step - accuracy: 0.3585 - loss: 1.3146
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 8ms/step - accuracy: 0.6068 - loss: 0.8605 - val_accuracy: 0.5332 - val_loss: 0.9125[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.6562 - loss: 0.7499[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.6406 - loss: 0.8829
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.5882 - loss: 0.9091 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3257 - loss: 1.3574  
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3388 - loss: 1.3349
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 35/65[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.6406 - loss: 0.8255
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.6157 - loss: 0.8799 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m331/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 6ms/step - accuracy: 0.3656 - loss: 1.3072[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.6719 - loss: 0.7415
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.6306 - loss: 0.8450 
[36m(train_cnn_ray_tune pid=3118307)[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=3118307)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3301 - loss: 1.3491  
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3417 - loss: 1.3297
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 31/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.6226 - loss: 0.8522
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 7ms/step - accuracy: 0.6237 - loss: 0.8497[32m [repeated 70x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.6255 - loss: 0.8062 
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.6131 - loss: 0.8277
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 7ms/step - accuracy: 0.3633 - loss: 1.3085 - val_accuracy: 0.3748 - val_loss: 1.2664[32m [repeated 5x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  8/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.6848 - loss: 0.7841 
[1m 16/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.6630 - loss: 0.8131
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 30ms/step - accuracy: 0.7031 - loss: 0.7761[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m  9/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 7ms/step - accuracy: 0.6617 - loss: 0.8253 
[36m(train_cnn_ray_tune pid=3118307)[0m Epoch 57/137[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step - accuracy: 0.6300 - loss: 0.8403[32m [repeated 9x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 10/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3639 - loss: 1.3112  
[1m 19/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 6ms/step - accuracy: 0.3561 - loss: 1.3140
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 7ms/step - accuracy: 0.6210 - loss: 0.8468
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 7ms/step - accuracy: 0.6209 - loss: 0.8466[32m [repeated 74x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 176ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m25/49[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 2ms/step  
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m27/96[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step 
[1m54/96[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 2ms/step
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m86/96[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 2ms/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:16:07. Total running time: 5min 52s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             349.932 │
│ time_total_s                 349.932 │
│ training_iteration                 1 │
│ val_accuracy                  0.5749 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:16:07. Total running time: 5min 52s
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.3403
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3535 - loss: 1.2969 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 6ms/step - accuracy: 0.3672 - loss: 1.3034 - val_accuracy: 0.3722 - val_loss: 1.2635[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118307)[0m 
[1m 10/167[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step - accuracy: 0.5959 - loss: 0.8629 
[1m 18/167[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 6ms/step - accuracy: 0.6046 - loss: 0.8557[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m10s[0m 31ms/step - accuracy: 0.3750 - loss: 1.2901[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 40/65[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.3786 - loss: 1.2881[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3942 - loss: 1.2764 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m243/333[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.3823 - loss: 1.2866
[1m257/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.3824 - loss: 1.2864[32m [repeated 39x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.3825 - loss: 1.2858 - val_accuracy: 0.3788 - val_loss: 1.2612[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3594 - loss: 1.2791 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3554 - loss: 1.2837[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3438 - loss: 1.1967[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 43/65[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.2500 - loss: 1.2910
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3546 - loss: 1.2828 

Trial status: 19 TERMINATED | 1 RUNNING
Current time: 2025-11-05 11:16:16. Total running time: 6min 1s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    TERMINATED           4   adam            relu                                   64                128                  5          0.00168541         137        1           349.932          0.574901 │
│ trial_9f7c8    TERMINATED           2   adam            tanh                                   32                 32                  5          0.00357251         149        1           199.893          0.366951 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   32                128                  3          1.05278e-05         72        1           334.862          0.39159  │
│ trial_9f7c8    TERMINATED           4   rmsprop         tanh                                  128                 32                  3          0.000165933        107        1           296.992          0.457293 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   32                 64                  3          0.000159733         73        1           330.557          0.498029 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           4   adam            relu                                   64                 64                  3          0.000386417        121        1           325.24           0.444809 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          1.56689e-05        116        1           261.789          0.365966 │
│ trial_9f7c8    TERMINATED           3   rmsprop         tanh                                   32                128                  3          0.00149202          59        1           307.661          0.420828 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   64                 32                  5          0.00247751         130        1           184.977          0.371222 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  3          0.000917269        145        1           295.091          0.604139 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111        1           260.985          0.396846 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                  128                128                  5          7.16419e-05         66        1           271.66           0.540079 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.0020272           93        1           269.098          0.593627 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00213202          64        1           298.736          0.404074 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
│ trial_9f7c8    TERMINATED           3   adam            tanh                                  128                128                  3          0.000931045         72        1           291.746          0.432983 │
│ trial_9f7c8    TERMINATED           4   rmsprop         relu                                  128                128                  3          0.00205798         108        1           250.829          0.576544 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3613 - loss: 1.2831[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m303/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 4ms/step - accuracy: 0.3710 - loss: 1.2807
[1m316/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 4ms/step - accuracy: 0.3712 - loss: 1.2805[32m [repeated 33x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.3717 - loss: 1.2802 - val_accuracy: 0.3719 - val_loss: 1.2548[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3439 - loss: 1.3226 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3634 - loss: 1.2994[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.3125 - loss: 1.3775[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5312 - loss: 1.2532
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4343 - loss: 1.2672 
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 47/65[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.3882 - loss: 1.2624[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2005
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3834 - loss: 1.2721 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m134/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.3965 - loss: 1.2598
[1m148/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.3971 - loss: 1.2587[32m [repeated 38x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.3969 - loss: 1.2541 - val_accuracy: 0.3853 - val_loss: 1.2364[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3925 - loss: 1.2743 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3909 - loss: 1.2585[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.3443[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.3438 - loss: 1.1979
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4276 - loss: 1.1714 
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 50/65[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m322/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.4059 - loss: 1.2243[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m278/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.3975 - loss: 1.2167
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 4ms/step - accuracy: 0.3978 - loss: 1.2166[32m [repeated 38x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1637
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4239 - loss: 1.2146 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.3985 - loss: 1.2164 - val_accuracy: 0.4133 - val_loss: 1.2214[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3947 - loss: 1.2081 
[1m 28/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3930 - loss: 1.2104
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.2812 - loss: 1.3663
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5312 - loss: 1.1085
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4347 - loss: 1.2143 
[1m 28/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4291 - loss: 1.2095
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2657
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4399 - loss: 1.2024 
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 54/65[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m320/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.4149 - loss: 1.2114[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.4062 - loss: 1.3088
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4163 - loss: 1.2867 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4214 - loss: 1.2669
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 40/333[0m [32m━━[0m[37m━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4209 - loss: 1.2585
[1m 53/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4224 - loss: 1.2502[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.4165 - loss: 1.1952 - val_accuracy: 0.4409 - val_loss: 1.1756[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 24ms/step - accuracy: 0.4375 - loss: 1.2614
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4356 - loss: 1.1979 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4367 - loss: 1.1899
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9906
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4651 - loss: 1.1212 
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 57/65[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m332/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.4404 - loss: 1.1557[32m [repeated 6x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3438 - loss: 1.2452
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3893 - loss: 1.2358 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.3961 - loss: 1.2270
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m175/333[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.4123 - loss: 1.1882
[1m188/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.4129 - loss: 1.1872[32m [repeated 37x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0670
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4872 - loss: 1.1927 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.4168 - loss: 1.1830 - val_accuracy: 0.4405 - val_loss: 1.1624[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9821
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4603 - loss: 1.1103 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4612 - loss: 1.1258
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 60/65[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8s[0m 24ms/step - accuracy: 0.3438 - loss: 1.2257
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4297 - loss: 1.1323 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m321/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.4392 - loss: 1.1585[32m [repeated 7x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m296/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 4ms/step - accuracy: 0.4353 - loss: 1.1527
[1m310/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 4ms/step - accuracy: 0.4352 - loss: 1.1532[32m [repeated 35x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1615
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4263 - loss: 1.1547 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4319 - loss: 1.1463
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.4351 - loss: 1.1540 - val_accuracy: 0.4435 - val_loss: 1.1665[32m [repeated 3x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0846
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4815 - loss: 1.1436 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.4062 - loss: 1.0454
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4479 - loss: 1.1389 
[1m 27/333[0m [32m━[0m[37m━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4531 - loss: 1.1444
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 64/65[32m [repeated 4x across cluster][0m
Trial status: 19 TERMINATED | 1 RUNNING
Current time: 2025-11-05 11:16:46. Total running time: 6min 31s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    RUNNING              4   adam            tanh                                   32                128                  3          6.9214e-05          65                                              │
│ trial_9f7c8    TERMINATED           4   adam            relu                                   64                128                  5          0.00168541         137        1           349.932          0.574901 │
│ trial_9f7c8    TERMINATED           2   adam            tanh                                   32                 32                  5          0.00357251         149        1           199.893          0.366951 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   32                128                  3          1.05278e-05         72        1           334.862          0.39159  │
│ trial_9f7c8    TERMINATED           4   rmsprop         tanh                                  128                 32                  3          0.000165933        107        1           296.992          0.457293 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   32                 64                  3          0.000159733         73        1           330.557          0.498029 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           4   adam            relu                                   64                 64                  3          0.000386417        121        1           325.24           0.444809 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          1.56689e-05        116        1           261.789          0.365966 │
│ trial_9f7c8    TERMINATED           3   rmsprop         tanh                                   32                128                  3          0.00149202          59        1           307.661          0.420828 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   64                 32                  5          0.00247751         130        1           184.977          0.371222 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  3          0.000917269        145        1           295.091          0.604139 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111        1           260.985          0.396846 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                  128                128                  5          7.16419e-05         66        1           271.66           0.540079 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.0020272           93        1           269.098          0.593627 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00213202          64        1           298.736          0.404074 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
│ trial_9f7c8    TERMINATED           3   adam            tanh                                  128                128                  3          0.000931045         72        1           291.746          0.432983 │
│ trial_9f7c8    TERMINATED           4   rmsprop         relu                                  128                128                  3          0.00205798         108        1           250.829          0.576544 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.4338 - loss: 1.1617[32m [repeated 2x across cluster][0m
2025-11-05 11:16:49,444	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_ESANN_acc_superclasses_CPA_METs/ESANN_hyperparameters_tuning' in 0.0082s.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762337809.578496 3116668 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
[36m(train_cnn_ray_tune pid=3118319)[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=3118319)[0m   _log_deprecation_warning(
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m7s[0m 21ms/step - accuracy: 0.3125 - loss: 1.1951
[1m 14/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 4ms/step - accuracy: 0.4230 - loss: 1.1180 
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 81/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.4375 - loss: 1.1485
[1m 95/333[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 4ms/step - accuracy: 0.4378 - loss: 1.1491[32m [repeated 36x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m2s[0m 4ms/step - accuracy: 0.4382 - loss: 1.1558 - val_accuracy: 0.4363 - val_loss: 1.1730[32m [repeated 4x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m6s[0m 133ms/step
[1m34/49[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 2ms/step  
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m34/96[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step 
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step

Trial trial_9f7c8 finished iteration 1 at 2025-11-05 11:16:49. Total running time: 6min 34s
╭──────────────────────────────────────╮
│ Trial trial_9f7c8 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             391.333 │
│ time_total_s                 391.333 │
│ training_iteration                 1 │
│ val_accuracy                 0.43627 │
╰──────────────────────────────────────╯

Trial trial_9f7c8 completed after 1 iterations at 2025-11-05 11:16:49. Total running time: 6min 34s

Trial status: 20 TERMINATED
Current time: 2025-11-05 11:16:49. Total running time: 6min 34s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         N_capas   optimizador     funcion_activacion       tamanho_minilote     numero_filtros     tamanho_filtro     tasa_aprendizaje     epochs     iter     total time (s)     val_accuracy │
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_9f7c8    TERMINATED           4   adam            relu                                   64                128                  5          0.00168541         137        1           349.932          0.574901 │
│ trial_9f7c8    TERMINATED           2   adam            tanh                                   32                 32                  5          0.00357251         149        1           199.893          0.366951 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   32                128                  3          1.05278e-05         72        1           334.862          0.39159  │
│ trial_9f7c8    TERMINATED           4   rmsprop         tanh                                  128                 32                  3          0.000165933        107        1           296.992          0.457293 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   32                 64                  3          0.000159733         73        1           330.557          0.498029 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  5          0.000146441        116        1           172.777          0.383377 │
│ trial_9f7c8    TERMINATED           4   adam            relu                                   64                 64                  3          0.000386417        121        1           325.24           0.444809 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          1.56689e-05        116        1           261.789          0.365966 │
│ trial_9f7c8    TERMINATED           3   rmsprop         tanh                                   32                128                  3          0.00149202          59        1           307.661          0.420828 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   64                 32                  5          0.00247751         130        1           184.977          0.371222 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  3          0.000917269        145        1           295.091          0.604139 │
│ trial_9f7c8    TERMINATED           4   adam            tanh                                   32                128                  3          6.9214e-05          65        1           391.333          0.436268 │
│ trial_9f7c8    TERMINATED           3   rmsprop         relu                                   64                 32                  3          3.97418e-05        111        1           260.985          0.396846 │
│ trial_9f7c8    TERMINATED           2   adam            relu                                   64                 32                  5          0.000113503        140        1            65.9468         0.383706 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                  128                128                  5          7.16419e-05         66        1           271.66           0.540079 │
│ trial_9f7c8    TERMINATED           2   rmsprop         relu                                   64                 32                  5          0.0020272           93        1           269.098          0.593627 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   32                128                  3          0.00213202          64        1           298.736          0.404074 │
│ trial_9f7c8    TERMINATED           2   rmsprop         tanh                                   64                 32                  3          0.000203535         58        1            92.6448         0.374507 │
│ trial_9f7c8    TERMINATED           3   adam            tanh                                  128                128                  3          0.000931045         72        1           291.746          0.432983 │
│ trial_9f7c8    TERMINATED           4   rmsprop         relu                                  128                128                  3          0.00205798         108        1           250.829          0.576544 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros: {'N_capas': 2, 'optimizador': 'rmsprop', 'funcion_activacion': 'relu', 'tamanho_minilote': 64, 'numero_filtros': 32, 'tamanho_filtro': 3, 'tasa_aprendizaje': 0.0009172693153500152, 'epochs': 145}
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762337811.131881 3204756 service.cc:152] XLA service 0x75484c009a10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762337811.131912 3204756 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:16:51.165932: 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 00:00:1762337811.290292 3204756 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762337813.466759 3204756 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:59[0m 3s/step - accuracy: 0.3125 - loss: 2.1606
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2636 - loss: 2.3715 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2713 - loss: 2.2380
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2774 - loss: 2.1537
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2813 - loss: 2.0876
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2815 - loss: 2.0848
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2815 - loss: 2.0835 - val_accuracy: 0.3936 - val_loss: 1.2526
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 1.5880
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3253 - loss: 1.5198 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.5091
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3187 - loss: 1.4969
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3189 - loss: 1.4854 - val_accuracy: 0.3873 - val_loss: 1.2562
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2656 - loss: 1.4135
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3229 - loss: 1.3729 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3301 - loss: 1.3628
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3341 - loss: 1.3573
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3354 - loss: 1.3546 - val_accuracy: 0.3827 - val_loss: 1.2628
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.3107
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3530 - loss: 1.3299 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3557 - loss: 1.3263
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3556 - loss: 1.3261
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3560 - loss: 1.3262 - val_accuracy: 0.3804 - val_loss: 1.2527
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2490
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3788 - loss: 1.3079 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3765 - loss: 1.3045
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3758 - loss: 1.3026
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3744 - loss: 1.3023 - val_accuracy: 0.4001 - val_loss: 1.2361
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.4219 - loss: 1.2241
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.2986 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3690 - loss: 1.3000
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.2994
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3706 - loss: 1.2982 - val_accuracy: 0.3949 - val_loss: 1.2338
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.3432
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3751 - loss: 1.2921 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3812 - loss: 1.2866
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3824 - loss: 1.2844
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3827 - loss: 1.2824
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3827 - loss: 1.2823 - val_accuracy: 0.4090 - val_loss: 1.2090
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3438 - loss: 1.2537
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3996 - loss: 1.2422 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3968 - loss: 1.2477
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3965 - loss: 1.2498
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3956 - loss: 1.2519 - val_accuracy: 0.4139 - val_loss: 1.2003
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.3998
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3944 - loss: 1.2417 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.2416
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.2432
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3931 - loss: 1.2439 - val_accuracy: 0.4060 - val_loss: 1.1953
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3281 - loss: 1.2144
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3944 - loss: 1.2403 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4027 - loss: 1.2402
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4049 - loss: 1.2386
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4051 - loss: 1.2384
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4051 - loss: 1.2384 - val_accuracy: 0.4323 - val_loss: 1.1818
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2029
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3932 - loss: 1.2289 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3985 - loss: 1.2278
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3997 - loss: 1.2295
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4001 - loss: 1.2291 - val_accuracy: 0.4116 - val_loss: 1.1693
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2705
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4082 - loss: 1.2303 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4093 - loss: 1.2257
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4093 - loss: 1.2224
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4098 - loss: 1.2207 - val_accuracy: 0.4238 - val_loss: 1.1663
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2420
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4135 - loss: 1.2242 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4192 - loss: 1.2136
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4189 - loss: 1.2116
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4185 - loss: 1.2111 - val_accuracy: 0.4317 - val_loss: 1.1601
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3116
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4238 - loss: 1.2173 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4294 - loss: 1.2093
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2073
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4284 - loss: 1.2052 - val_accuracy: 0.4537 - val_loss: 1.1325
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1788
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4237 - loss: 1.2009 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4273 - loss: 1.1970
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4270 - loss: 1.1936
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4279 - loss: 1.1919 - val_accuracy: 0.4711 - val_loss: 1.1329
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.0961
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4368 - loss: 1.1772 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4379 - loss: 1.1820
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.1869
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4374 - loss: 1.1880 - val_accuracy: 0.4675 - val_loss: 1.1133
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.4493
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.1895 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.1840
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4357 - loss: 1.1815
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4367 - loss: 1.1815 - val_accuracy: 0.4819 - val_loss: 1.1128
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2231
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4293 - loss: 1.2024 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.1976
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.1911
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4386 - loss: 1.1869 - val_accuracy: 0.4806 - val_loss: 1.1010
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2502
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4254 - loss: 1.1956 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4373 - loss: 1.1814
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4414 - loss: 1.1763
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.1735
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4433 - loss: 1.1733 - val_accuracy: 0.4760 - val_loss: 1.1154
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1295
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4335 - loss: 1.1740 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4363 - loss: 1.1739
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4384 - loss: 1.1714
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4395 - loss: 1.1697
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4396 - loss: 1.1694 - val_accuracy: 0.4813 - val_loss: 1.0912
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2042
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4486 - loss: 1.1666 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4517 - loss: 1.1633
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1594
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4552 - loss: 1.1563 - val_accuracy: 0.4954 - val_loss: 1.0727
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.2989
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1573 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.1496
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1502
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4591 - loss: 1.1501 - val_accuracy: 0.4918 - val_loss: 1.0774
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3189
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1364 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1445
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1463
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4748 - loss: 1.1457 - val_accuracy: 0.4967 - val_loss: 1.0720
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1794
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1222 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1308
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1352
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4824 - loss: 1.1372 - val_accuracy: 0.4754 - val_loss: 1.0900
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1789
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1312 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1355
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1366
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1365
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4760 - loss: 1.1365 - val_accuracy: 0.4980 - val_loss: 1.0882
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0677
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1224 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4885 - loss: 1.1232
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1249
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1253
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4868 - loss: 1.1253 - val_accuracy: 0.5207 - val_loss: 1.0405
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0940
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4589 - loss: 1.1361 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1297
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4664 - loss: 1.1266
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.1251
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4682 - loss: 1.1251 - val_accuracy: 0.4793 - val_loss: 1.0788
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2432
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4677 - loss: 1.1169 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1183
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4759 - loss: 1.1184
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1198
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4767 - loss: 1.1199 - val_accuracy: 0.5030 - val_loss: 1.0416
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9950
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1194 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1206
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1205
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1191
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4878 - loss: 1.1188 - val_accuracy: 0.5289 - val_loss: 1.0248
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1571
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1057 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1066
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1079
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1092
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4907 - loss: 1.1093 - val_accuracy: 0.5306 - val_loss: 1.0227
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 1.0575
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.1077 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1099
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1101
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4936 - loss: 1.1095 - val_accuracy: 0.5476 - val_loss: 1.0074
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2722
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4834 - loss: 1.1196 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1124
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1097
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1081
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4900 - loss: 1.1080 - val_accuracy: 0.5378 - val_loss: 1.0082
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1252
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1209 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4906 - loss: 1.1148
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1116
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.1091
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4942 - loss: 1.1090 - val_accuracy: 0.5204 - val_loss: 1.0338
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 1.0932
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.1064 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0973
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.0960
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5020 - loss: 1.0949 - val_accuracy: 0.5342 - val_loss: 0.9975
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0992
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.0972 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.1011
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1001
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.0982
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4984 - loss: 1.0982 - val_accuracy: 0.5453 - val_loss: 0.9932
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5312 - loss: 1.0393
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0746 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0792
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0791
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5093 - loss: 1.0786 - val_accuracy: 0.5542 - val_loss: 0.9747
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.1306
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1022 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.0970
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.0914
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5025 - loss: 1.0884 - val_accuracy: 0.5184 - val_loss: 0.9991
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9596
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.0588 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.0677
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.0711
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0724
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5066 - loss: 1.0724 - val_accuracy: 0.5549 - val_loss: 0.9575
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5156 - loss: 1.0785
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0558 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.0651
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.0671
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5118 - loss: 1.0676 - val_accuracy: 0.5312 - val_loss: 1.0022
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.5156 - loss: 1.1045
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0958 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0914
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0842
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5217 - loss: 1.0799 - val_accuracy: 0.5503 - val_loss: 0.9544
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5156 - loss: 1.1001
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0694 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0675
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0661
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0659
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5148 - loss: 1.0659 - val_accuracy: 0.5552 - val_loss: 0.9598
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0355
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5399 - loss: 1.0266 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0365
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0428
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5284 - loss: 1.0471 - val_accuracy: 0.5844 - val_loss: 0.9232
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9785
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.0514 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0513
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0514
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0528
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5182 - loss: 1.0528 - val_accuracy: 0.5575 - val_loss: 0.9461
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5156 - loss: 1.1510
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0713 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0596
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0557
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0551
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5243 - loss: 1.0551 - val_accuracy: 0.5637 - val_loss: 0.9502
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1526
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.0536 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0582
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0544
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.0533 - val_accuracy: 0.5716 - val_loss: 0.9334
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1218
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.0930 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.0733
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.0646
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5095 - loss: 1.0604 - val_accuracy: 0.5315 - val_loss: 0.9856
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1686
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0589 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5209 - loss: 1.0525
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0503
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0491
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5238 - loss: 1.0489 - val_accuracy: 0.5493 - val_loss: 0.9638

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 491ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.
[36m(train_cnn_ray_tune pid=3118319)[0m Epoch 65/65
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m324/333[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 4ms/step - accuracy: 0.4382 - loss: 1.1558[32m [repeated 2x across cluster][0m
[36m(train_cnn_ray_tune pid=3118319)[0m 
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 4ms/step - accuracy: 0.4381 - loss: 1.1557
[1m310/333[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 4ms/step - accuracy: 0.4382 - loss: 1.1557[32m [repeated 8x across cluster][0m

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:52[0m 700ms/step
[1m 63/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 816us/step  
[1m130/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 781us/step
[1m200/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 762us/step
[1m267/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 758us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 744us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 54.89 [%]
Global F1 score (validation) = 56.08 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.41318643 0.49265173 0.00340482 0.09075697]
 [0.36989927 0.3467252  0.06436593 0.21900965]
 [0.38193357 0.34765202 0.08267173 0.18774265]
 ...
 [0.06241687 0.04338528 0.85320157 0.04099625]
 [0.0558251  0.03827824 0.86978805 0.03610867]
 [0.05075303 0.03519576 0.8816936  0.03235768]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 60.02 [%]
Global accuracy score (test) = 52.49 [%]
Global F1 score (train) = 61.2 [%]
Global F1 score (test) = 53.43 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.33      0.36      0.35       400
MODERATE-INTENSITY       0.49      0.55      0.51       400
         SEDENTARY       0.59      0.70      0.64       400
VIGOROUS-INTENSITY       0.93      0.49      0.64       345

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

2025-11-05 11:17:24.325514: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:17:24.336765: 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 00:00:1762337844.349999 3209915 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762337844.354174 3209915 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762337844.363872 3209915 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337844.363890 3209915 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337844.363893 3209915 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337844.363894 3209915 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:17:24.367057: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762337846.595863 3209915 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762337847.957185 3210024 service.cc:152] XLA service 0x7315c40044b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762337847.957215 3210024 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:17:27.992256: 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 00:00:1762337848.110408 3210024 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762337850.257777 3210024 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:23[0m 3s/step - accuracy: 0.2500 - loss: 2.6501
[1m 32/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2583 - loss: 2.4928 
[1m 74/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2642 - loss: 2.4289
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2704 - loss: 2.3498
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2757 - loss: 2.2805
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2761 - loss: 2.2748
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2762 - loss: 2.2734 - val_accuracy: 0.3942 - val_loss: 1.2770
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 1.6438
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3168 - loss: 1.6746 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3090 - loss: 1.6479
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3090 - loss: 1.6207
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3097 - loss: 1.5985 - val_accuracy: 0.3945 - val_loss: 1.2687
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2188 - loss: 1.5935
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3355 - loss: 1.4025 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3368 - loss: 1.3931
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3359 - loss: 1.3893
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3358 - loss: 1.3850 - val_accuracy: 0.3922 - val_loss: 1.2662
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2576
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3604 - loss: 1.3245 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.3257
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.3247
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3591 - loss: 1.3243 - val_accuracy: 0.4021 - val_loss: 1.2557
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.2771
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3690 - loss: 1.3123 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3649 - loss: 1.3148
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3658 - loss: 1.3128
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3656 - loss: 1.3111
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3656 - loss: 1.3110 - val_accuracy: 0.4047 - val_loss: 1.2442
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2500 - loss: 1.3197
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3601 - loss: 1.3048 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3681 - loss: 1.3017
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3707 - loss: 1.2985
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3721 - loss: 1.2964 - val_accuracy: 0.3945 - val_loss: 1.2342
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.2998
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3427 - loss: 1.2863 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3610 - loss: 1.2773
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.2742
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3714 - loss: 1.2735
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3715 - loss: 1.2735 - val_accuracy: 0.3840 - val_loss: 1.2207
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2835
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3885 - loss: 1.2665 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3868 - loss: 1.2629
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.2613
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3881 - loss: 1.2606 - val_accuracy: 0.4034 - val_loss: 1.2013
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 1.2074
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3941 - loss: 1.2442 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3931 - loss: 1.2510
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3923 - loss: 1.2526
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3925 - loss: 1.2529 - val_accuracy: 0.4175 - val_loss: 1.1934
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3438 - loss: 1.2638
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3936 - loss: 1.2532 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3962 - loss: 1.2482
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3987 - loss: 1.2459
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4002 - loss: 1.2437
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4003 - loss: 1.2436 - val_accuracy: 0.4244 - val_loss: 1.1564
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.3289
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3916 - loss: 1.2502 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3971 - loss: 1.2422
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4000 - loss: 1.2373
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4020 - loss: 1.2335 - val_accuracy: 0.4573 - val_loss: 1.1521
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1188
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.2364 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4054 - loss: 1.2371
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4056 - loss: 1.2337
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4070 - loss: 1.2306
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4073 - loss: 1.2302 - val_accuracy: 0.4494 - val_loss: 1.1466
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3906 - loss: 1.2062
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4085 - loss: 1.2161 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4203 - loss: 1.2062
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4229 - loss: 1.2039
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4241 - loss: 1.2036
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4242 - loss: 1.2035 - val_accuracy: 0.4297 - val_loss: 1.2002
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.3843
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4157 - loss: 1.2163 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4211 - loss: 1.2061
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4235 - loss: 1.2033
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4251 - loss: 1.2014
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4254 - loss: 1.2012 - val_accuracy: 0.4839 - val_loss: 1.1144
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1609
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.1909 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4303 - loss: 1.1902
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.1913
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4288 - loss: 1.1916 - val_accuracy: 0.4846 - val_loss: 1.1089
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3750 - loss: 1.2231
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4287 - loss: 1.1972 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4339 - loss: 1.1893
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4351 - loss: 1.1876
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4355 - loss: 1.1871
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4356 - loss: 1.1868 - val_accuracy: 0.4629 - val_loss: 1.1607
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3594 - loss: 1.2700
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4343 - loss: 1.1802 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4415 - loss: 1.1738
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.1716
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4427 - loss: 1.1708 - val_accuracy: 0.4964 - val_loss: 1.0894
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0959
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4438 - loss: 1.1875 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4385 - loss: 1.1836
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.1820
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.1799
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4359 - loss: 1.1796 - val_accuracy: 0.5085 - val_loss: 1.0654
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.1649
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4568 - loss: 1.1605 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1590
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1596
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4569 - loss: 1.1605 - val_accuracy: 0.4987 - val_loss: 1.0754
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0989
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4617 - loss: 1.1348 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.1460
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1516
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4540 - loss: 1.1540 - val_accuracy: 0.4977 - val_loss: 1.0977
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2887
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.1819 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.1700
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1655
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4553 - loss: 1.1642 - val_accuracy: 0.5112 - val_loss: 1.0871
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.3740
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1629 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1613
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1594
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4733 - loss: 1.1576 - val_accuracy: 0.4520 - val_loss: 1.1189
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0735
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4537 - loss: 1.1590 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1507
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1477
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4612 - loss: 1.1466 - val_accuracy: 0.4928 - val_loss: 1.1041

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 486ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 1: 52.49 [%]
F1-score capturado en la ejecución 1: 53.43 [%]

=== 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)
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:40[0m 663ms/step
[1m 71/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 719us/step  
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 690us/step
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 689us/step
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 697us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m71/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 720us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 49.21 [%]
Global F1 score (validation) = 49.76 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3404775  0.37860388 0.01526366 0.26565495]
 [0.30035728 0.2599313  0.2215849  0.21812657]
 [0.30348414 0.26326945 0.21487649 0.21836993]
 ...
 [0.2165122  0.18556254 0.4567774  0.1411479 ]
 [0.19139598 0.16055514 0.52439255 0.12365639]
 [0.21107383 0.17992248 0.4706208  0.13838285]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 53.45 [%]
Global accuracy score (test) = 50.49 [%]
Global F1 score (train) = 54.13 [%]
Global F1 score (test) = 50.05 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.49      0.42       400
MODERATE-INTENSITY       0.41      0.40      0.40       400
         SEDENTARY       0.67      0.76      0.72       400
VIGOROUS-INTENSITY       0.68      0.35      0.46       345

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

2025-11-05 11:17:51.194240: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:17:51.205319: 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 00:00:1762337871.218474 3212953 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762337871.222433 3212953 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762337871.232370 3212953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337871.232388 3212953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337871.232391 3212953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337871.232393 3212953 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:17:51.235533: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762337873.473328 3212953 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762337874.856948 3213086 service.cc:152] XLA service 0x7151d8016110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762337874.856986 3213086 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:17:54.890703: 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 00:00:1762337875.014921 3213086 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762337877.134227 3213086 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:22[0m 3s/step - accuracy: 0.2031 - loss: 2.9764
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2504 - loss: 2.6748 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2627 - loss: 2.5411
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2708 - loss: 2.4409
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2762 - loss: 2.3576
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2768 - loss: 2.3473
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2769 - loss: 2.3456 - val_accuracy: 0.3978 - val_loss: 1.2965
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2188 - loss: 1.8483
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2866 - loss: 1.6788 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2871 - loss: 1.6567
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2906 - loss: 1.6332
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2935 - loss: 1.6117
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.2940 - loss: 1.6083 - val_accuracy: 0.3893 - val_loss: 1.2887
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.3734
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3248 - loss: 1.3906 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.3909
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3209 - loss: 1.3871
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.3842
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3216 - loss: 1.3838 - val_accuracy: 0.3988 - val_loss: 1.2778
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2188 - loss: 1.4826
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3371 - loss: 1.3534 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3436 - loss: 1.3465
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.3446
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.3426
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.3424 - val_accuracy: 0.3873 - val_loss: 1.2662
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4062 - loss: 1.3184
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3671 - loss: 1.3187 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3638 - loss: 1.3200
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.3199
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3631 - loss: 1.3190
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3632 - loss: 1.3189 - val_accuracy: 0.3978 - val_loss: 1.2496
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.3155
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3395 - loss: 1.3248 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3514 - loss: 1.3182
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3565 - loss: 1.3151
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3593 - loss: 1.3129 - val_accuracy: 0.3804 - val_loss: 1.2339
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1891
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3976 - loss: 1.2678 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3858 - loss: 1.2778
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3813 - loss: 1.2824
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3797 - loss: 1.2836 - val_accuracy: 0.4093 - val_loss: 1.2379
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2376
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3910 - loss: 1.2787 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3902 - loss: 1.2760
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3907 - loss: 1.2739
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3913 - loss: 1.2725 - val_accuracy: 0.4077 - val_loss: 1.2086
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1708
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4092 - loss: 1.2428 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4061 - loss: 1.2468
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4042 - loss: 1.2497
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4032 - loss: 1.2503 - val_accuracy: 0.4179 - val_loss: 1.1788
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2406
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4251 - loss: 1.2286 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4206 - loss: 1.2309
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4193 - loss: 1.2304
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4185 - loss: 1.2308 - val_accuracy: 0.4212 - val_loss: 1.1706
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3906 - loss: 1.2733
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4116 - loss: 1.2233 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4160 - loss: 1.2206
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4179 - loss: 1.2210
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4182 - loss: 1.2217
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4182 - loss: 1.2217 - val_accuracy: 0.4524 - val_loss: 1.1560
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4688 - loss: 1.2826
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.2173 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4400 - loss: 1.2165
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4362 - loss: 1.2165
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4342 - loss: 1.2161 - val_accuracy: 0.4888 - val_loss: 1.1053
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1282
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.2111 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4314 - loss: 1.2140
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4316 - loss: 1.2134
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4323 - loss: 1.2120 - val_accuracy: 0.4616 - val_loss: 1.1259
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.0814
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4384 - loss: 1.1671 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4373 - loss: 1.1823
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4374 - loss: 1.1873
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4373 - loss: 1.1897 - val_accuracy: 0.4829 - val_loss: 1.1094
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3906 - loss: 1.2440
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4152 - loss: 1.2271 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4269 - loss: 1.2131
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.2056
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4371 - loss: 1.2018 - val_accuracy: 0.4990 - val_loss: 1.1043
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2316
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4548 - loss: 1.1694 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1685
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1717
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4537 - loss: 1.1734 - val_accuracy: 0.4944 - val_loss: 1.0983
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.3695
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4479 - loss: 1.2012 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4548 - loss: 1.1845
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4564 - loss: 1.1786
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4571 - loss: 1.1761 - val_accuracy: 0.5174 - val_loss: 1.0793
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.1431
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1631 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1612
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.1617
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4631 - loss: 1.1612 - val_accuracy: 0.4757 - val_loss: 1.1231
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2477
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1574 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1492
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1478
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4798 - loss: 1.1484 - val_accuracy: 0.4777 - val_loss: 1.0871
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4062 - loss: 1.2307
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1664 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1597
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1568
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4712 - loss: 1.1551 - val_accuracy: 0.5230 - val_loss: 1.0501
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1172
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1338 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.1375
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1383
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4876 - loss: 1.1385 - val_accuracy: 0.5122 - val_loss: 1.0545
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3906 - loss: 1.2506
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4669 - loss: 1.1609 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4733 - loss: 1.1499
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1445
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1391
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4788 - loss: 1.1390 - val_accuracy: 0.5443 - val_loss: 1.0274
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2374
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1199 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1171
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4986 - loss: 1.1181
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4983 - loss: 1.1190 - val_accuracy: 0.5503 - val_loss: 1.0234
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4219 - loss: 1.1915
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.1120 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1169
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1190
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1197
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4963 - loss: 1.1197 - val_accuracy: 0.5355 - val_loss: 1.0260
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0932
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1186 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1120
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.1086
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.1085
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5029 - loss: 1.1085 - val_accuracy: 0.5243 - val_loss: 1.0297
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5312 - loss: 1.2240
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1255 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.1152
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1116
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5033 - loss: 1.1087 - val_accuracy: 0.5306 - val_loss: 1.0064
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0168
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.0803 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.0899
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0930
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5017 - loss: 1.0932 - val_accuracy: 0.5585 - val_loss: 0.9919
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1009
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0888 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0930
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0982
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0982
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.0981 - val_accuracy: 0.5558 - val_loss: 0.9660
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0534
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1017 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5041 - loss: 1.0950
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0885
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.0855
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5083 - loss: 1.0854 - val_accuracy: 0.5470 - val_loss: 0.9709
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1116
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0821 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0833
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5165 - loss: 1.0847
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5163 - loss: 1.0846 - val_accuracy: 0.5739 - val_loss: 0.9499
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1269
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0676 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0612
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0592
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0587
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5199 - loss: 1.0587 - val_accuracy: 0.5578 - val_loss: 0.9515
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.4688 - loss: 1.0995
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0488 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0559
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0575
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5235 - loss: 1.0583 - val_accuracy: 0.5700 - val_loss: 0.9480
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0524
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0754 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0741
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0724
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0705
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5213 - loss: 1.0703 - val_accuracy: 0.5595 - val_loss: 0.9502
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0914
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5197 - loss: 1.0564 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0545
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0533
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0512
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5266 - loss: 1.0511 - val_accuracy: 0.5654 - val_loss: 0.9418
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.9773
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5599 - loss: 1.0062 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5518 - loss: 1.0223
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5464 - loss: 1.0308
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5430 - loss: 1.0358 - val_accuracy: 0.5473 - val_loss: 0.9594
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5000 - loss: 1.0718
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5506 - loss: 1.0316 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0364
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0358
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5438 - loss: 1.0364 - val_accuracy: 0.5693 - val_loss: 0.9465
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1843
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0592 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0452
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0397
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5305 - loss: 1.0376 - val_accuracy: 0.5890 - val_loss: 0.8914
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1187
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0415 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0338
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0312
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5388 - loss: 1.0293 - val_accuracy: 0.5890 - val_loss: 0.8994
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 0.9609
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0355 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0394
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0341
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0311
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5405 - loss: 1.0311 - val_accuracy: 0.5611 - val_loss: 0.9019
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1550
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5566 - loss: 1.0301 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5605 - loss: 1.0227
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5605 - loss: 1.0202
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5597 - loss: 1.0183 - val_accuracy: 0.5976 - val_loss: 0.8817
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1528
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 1.0332 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 1.0250
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5526 - loss: 1.0216
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5526 - loss: 1.0201 - val_accuracy: 0.6051 - val_loss: 0.8982
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1642
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0397 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0326
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0293
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5437 - loss: 1.0271 - val_accuracy: 0.5664 - val_loss: 0.9059
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9050
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 0.9931 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5546 - loss: 1.0001
[1m135/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5531 - loss: 1.0048
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5534 - loss: 1.0052 - val_accuracy: 0.5900 - val_loss: 0.8871
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5781 - loss: 0.9400
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0069 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0117
[1m135/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0126
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5442 - loss: 1.0118 - val_accuracy: 0.6101 - val_loss: 0.8540
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9468
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5568 - loss: 1.0082 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5552 - loss: 1.0068
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 1.0051
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 1.0042
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5553 - loss: 1.0041 - val_accuracy: 0.6032 - val_loss: 0.8591
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9763
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5606 - loss: 0.9799 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5577 - loss: 0.9830
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5558 - loss: 0.9859
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5552 - loss: 0.9879
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5551 - loss: 0.9880 - val_accuracy: 0.5503 - val_loss: 0.9838
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.0685
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0102 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0057
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 1.0054
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5463 - loss: 1.0056 - val_accuracy: 0.5989 - val_loss: 0.8907
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5625 - loss: 0.9487
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5664 - loss: 0.9590 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5620 - loss: 0.9698
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5602 - loss: 0.9762
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5597 - loss: 0.9798
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5597 - loss: 0.9803 - val_accuracy: 0.6032 - val_loss: 0.8795
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9816
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5694 - loss: 1.0082 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5670 - loss: 1.0012
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5652 - loss: 0.9995
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5635 - loss: 0.9984
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5632 - loss: 0.9981 - val_accuracy: 0.6035 - val_loss: 0.8678

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 497ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 2: 50.49 [%]
F1-score capturado en la ejecución 2: 50.05 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:47[0m 685ms/step
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 747us/step  
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 712us/step
[1m214/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 706us/step
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 689us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 775us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 59.95 [%]
Global F1 score (validation) = 59.1 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.27208254 0.22839889 0.26574606 0.23377244]
 [0.26583397 0.22372459 0.26549757 0.24494392]
 [0.30891684 0.26569062 0.21422616 0.21116637]
 ...
 [0.11987291 0.08294874 0.7097845  0.0873939 ]
 [0.05804754 0.03708526 0.8675914  0.03727586]
 [0.13205583 0.09259284 0.6771474  0.09820399]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 63.0 [%]
Global accuracy score (test) = 56.31 [%]
Global F1 score (train) = 62.44 [%]
Global F1 score (test) = 55.88 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.34      0.38       400
MODERATE-INTENSITY       0.46      0.48      0.47       400
         SEDENTARY       0.60      0.89      0.72       400
VIGOROUS-INTENSITY       0.91      0.53      0.67       345

          accuracy                           0.56      1545
         macro avg       0.60      0.56      0.56      1545
      weighted avg       0.58      0.56      0.55      1545

2025-11-05 11:18:26.699505: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:18:26.711134: 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 00:00:1762337906.724422 3218393 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762337906.728739 3218393 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762337906.738927 3218393 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337906.738947 3218393 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337906.738950 3218393 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337906.738951 3218393 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:18:26.742257: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762337908.996721 3218393 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762337910.357818 3218527 service.cc:152] XLA service 0x7664f4003a50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762337910.357856 3218527 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:18:30.392780: 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 00:00:1762337910.517175 3218527 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762337912.635964 3218527 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:21[0m 3s/step - accuracy: 0.2500 - loss: 2.1230
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2556 - loss: 2.4351 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2686 - loss: 2.3465
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2757 - loss: 2.2724
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2801 - loss: 2.2083
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2804 - loss: 2.2030
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2805 - loss: 2.2017 - val_accuracy: 0.3601 - val_loss: 1.2737
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2656 - loss: 1.5829
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3106 - loss: 1.5780 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3107 - loss: 1.5587
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3107 - loss: 1.5435
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3117 - loss: 1.5288 - val_accuracy: 0.3880 - val_loss: 1.2774
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.4415
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3267 - loss: 1.3857 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3314 - loss: 1.3800
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.3784
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.3756 - val_accuracy: 0.3798 - val_loss: 1.2822
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.3594 - loss: 1.3181
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3345 - loss: 1.3405 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3383 - loss: 1.3397
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.3389
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3434 - loss: 1.3376
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3435 - loss: 1.3376 - val_accuracy: 0.3834 - val_loss: 1.2723
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2526
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3440 - loss: 1.3293 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3471 - loss: 1.3238
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3517 - loss: 1.3206
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3544 - loss: 1.3181
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3547 - loss: 1.3179 - val_accuracy: 0.3853 - val_loss: 1.2554
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 1.3296
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3647 - loss: 1.3041 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3684 - loss: 1.2998
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3708 - loss: 1.2987
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3719 - loss: 1.2977
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3719 - loss: 1.2976 - val_accuracy: 0.3857 - val_loss: 1.2436
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2527
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3704 - loss: 1.2843 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3690 - loss: 1.2852
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3707 - loss: 1.2832
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3728 - loss: 1.2807 - val_accuracy: 0.4087 - val_loss: 1.2180
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2167
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3830 - loss: 1.2656 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3829 - loss: 1.2671
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3840 - loss: 1.2665
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3848 - loss: 1.2658 - val_accuracy: 0.4097 - val_loss: 1.1978
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4219 - loss: 1.2248
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3938 - loss: 1.2663 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3929 - loss: 1.2609
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3940 - loss: 1.2554
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3943 - loss: 1.2530 - val_accuracy: 0.4258 - val_loss: 1.1800
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1431
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3852 - loss: 1.2177 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3917 - loss: 1.2238
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3950 - loss: 1.2250
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3966 - loss: 1.2265 - val_accuracy: 0.4320 - val_loss: 1.1955
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3248
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4151 - loss: 1.2235 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4142 - loss: 1.2201
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4134 - loss: 1.2188
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4130 - loss: 1.2185 - val_accuracy: 0.4287 - val_loss: 1.1501
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.4531 - loss: 1.2537
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4117 - loss: 1.2183 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4131 - loss: 1.2138
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4132 - loss: 1.2120
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4133 - loss: 1.2123 - val_accuracy: 0.4428 - val_loss: 1.1745
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2633
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4011 - loss: 1.2350 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4059 - loss: 1.2266
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4083 - loss: 1.2229
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4104 - loss: 1.2199
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4104 - loss: 1.2199 - val_accuracy: 0.4662 - val_loss: 1.1145
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2371
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.1957 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4171 - loss: 1.2021
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4188 - loss: 1.2025
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2013
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4216 - loss: 1.2011 - val_accuracy: 0.4731 - val_loss: 1.1100
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1302
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4291 - loss: 1.2120 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.2014
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4343 - loss: 1.1970
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4359 - loss: 1.1940 - val_accuracy: 0.4616 - val_loss: 1.1164
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4844 - loss: 1.0755
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.1884 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4380 - loss: 1.1869
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.1864
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4357 - loss: 1.1867
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4357 - loss: 1.1868 - val_accuracy: 0.4760 - val_loss: 1.1120
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2044
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.1870 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4429 - loss: 1.1796
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.1785
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.1778
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4432 - loss: 1.1778 - val_accuracy: 0.4974 - val_loss: 1.0944
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4062 - loss: 1.3107
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4410 - loss: 1.1862 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4438 - loss: 1.1804
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4461 - loss: 1.1789
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4466 - loss: 1.1778 - val_accuracy: 0.4997 - val_loss: 1.0939
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3750 - loss: 1.1631
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.1857 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.1806
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.1774
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4411 - loss: 1.1751 - val_accuracy: 0.4826 - val_loss: 1.0914
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.1540
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4323 - loss: 1.1832 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4366 - loss: 1.1804
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.1775
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.1749
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4429 - loss: 1.1748 - val_accuracy: 0.4796 - val_loss: 1.0915
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4062 - loss: 1.2786
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4307 - loss: 1.1960 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.1810
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4418 - loss: 1.1755
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.1720
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4449 - loss: 1.1710 - val_accuracy: 0.4576 - val_loss: 1.1338
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0620
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4651 - loss: 1.1409 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4620 - loss: 1.1440
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4603 - loss: 1.1442
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1453
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4594 - loss: 1.1454 - val_accuracy: 0.4724 - val_loss: 1.0984
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1242
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1314 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1351
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1378
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1399
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4634 - loss: 1.1402 - val_accuracy: 0.5033 - val_loss: 1.0753
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2032
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1354 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.1374
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1376
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4612 - loss: 1.1392
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.1395 - val_accuracy: 0.4898 - val_loss: 1.0690
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1526
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4606 - loss: 1.1309 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1389
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1404
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4691 - loss: 1.1401
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4691 - loss: 1.1400 - val_accuracy: 0.5187 - val_loss: 1.0431
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9114
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1122 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1210
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1236
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1245
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4800 - loss: 1.1245 - val_accuracy: 0.5141 - val_loss: 1.0291
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1478
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.1418 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1355
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1326
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1306
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4748 - loss: 1.1305 - val_accuracy: 0.5184 - val_loss: 1.0449
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.1668
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1585 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1425
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1356
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4797 - loss: 1.1324 - val_accuracy: 0.5279 - val_loss: 1.0374
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9845
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1262 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4837 - loss: 1.1258
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1208
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4885 - loss: 1.1176 - val_accuracy: 0.5125 - val_loss: 1.0327
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2010
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1050 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1042
[1m135/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.1030
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5017 - loss: 1.1019 - val_accuracy: 0.5043 - val_loss: 1.0365
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.2499
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1405 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1293
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1244
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4875 - loss: 1.1222 - val_accuracy: 0.5460 - val_loss: 1.0178
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1023
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1139 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1107
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.1064
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4923 - loss: 1.1037 - val_accuracy: 0.5470 - val_loss: 0.9924
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0632
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0694 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0740
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0734
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0742
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5165 - loss: 1.0744 - val_accuracy: 0.5542 - val_loss: 0.9815
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1211
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.0745 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.0780
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.0806
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5023 - loss: 1.0803 - val_accuracy: 0.5348 - val_loss: 0.9908
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0833
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.0878 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5051 - loss: 1.0824
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0777
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0753
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5130 - loss: 1.0753 - val_accuracy: 0.5601 - val_loss: 0.9536
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5000 - loss: 1.0408
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5360 - loss: 1.0370 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0498
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0551
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5215 - loss: 1.0573 - val_accuracy: 0.5575 - val_loss: 0.9597
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1290
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0630 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0640
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0621
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5249 - loss: 1.0614 - val_accuracy: 0.5414 - val_loss: 0.9913
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.0972
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0529 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0537
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0560
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0578
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5245 - loss: 1.0581 - val_accuracy: 0.5716 - val_loss: 0.9493
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1234
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0284 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0378
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0410
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5307 - loss: 1.0415 - val_accuracy: 0.4924 - val_loss: 1.1208
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.2901
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0896 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0697
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0601
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0553
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5337 - loss: 1.0552 - val_accuracy: 0.4760 - val_loss: 1.0881
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.1193
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5344 - loss: 1.0601 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0508
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0461
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5401 - loss: 1.0427
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5401 - loss: 1.0426 - val_accuracy: 0.5670 - val_loss: 0.9623
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0631
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0240 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0241
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0259
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0289
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5353 - loss: 1.0292 - val_accuracy: 0.5673 - val_loss: 0.9527
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9601
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0386 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0340
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0336
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5271 - loss: 1.0331 - val_accuracy: 0.5759 - val_loss: 0.9369
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1315
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0183 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0203
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0214
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0220
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5414 - loss: 1.0222 - val_accuracy: 0.5857 - val_loss: 0.9209
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1785
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0455 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5554 - loss: 1.0300
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 1.0259
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 1.0239
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5543 - loss: 1.0237 - val_accuracy: 0.5703 - val_loss: 0.9457
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.9902
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5535 - loss: 1.0068 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0133
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5499 - loss: 1.0134
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5493 - loss: 1.0127
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5492 - loss: 1.0127 - val_accuracy: 0.5890 - val_loss: 0.9077
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0499
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0140 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0111
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5472 - loss: 1.0120
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5463 - loss: 1.0134
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5462 - loss: 1.0138 - val_accuracy: 0.5815 - val_loss: 0.9154
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0958
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0157 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0131
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5481 - loss: 1.0140
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5481 - loss: 1.0149
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5481 - loss: 1.0149 - val_accuracy: 0.5470 - val_loss: 0.9355
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.9184
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5726 - loss: 0.9810 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 0.9963
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5548 - loss: 1.0008
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5532 - loss: 1.0023
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5530 - loss: 1.0024 - val_accuracy: 0.5769 - val_loss: 0.9143
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9988
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 0.9826 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 0.9877
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 0.9928
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 0.9957
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5434 - loss: 0.9958 - val_accuracy: 0.6055 - val_loss: 0.8981
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0205
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5650 - loss: 1.0011 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5635 - loss: 1.0031
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5627 - loss: 1.0018
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5614 - loss: 1.0015 - val_accuracy: 0.5841 - val_loss: 0.9028
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9087
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5659 - loss: 1.0026 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5619 - loss: 1.0027
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5602 - loss: 1.0015
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5589 - loss: 1.0019 - val_accuracy: 0.5762 - val_loss: 0.9020
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0944
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5581 - loss: 0.9896 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5558 - loss: 0.9978
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5535 - loss: 1.0024
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5527 - loss: 1.0042
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5527 - loss: 1.0042 - val_accuracy: 0.5719 - val_loss: 0.8970
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0017
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 0.9681 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5617 - loss: 0.9702
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5612 - loss: 0.9743
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5601 - loss: 0.9785
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5600 - loss: 0.9788 - val_accuracy: 0.5880 - val_loss: 0.9292
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0237
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0036 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5479 - loss: 0.9977
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 0.9974
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5490 - loss: 0.9981
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5491 - loss: 0.9981 - val_accuracy: 0.5969 - val_loss: 0.8960
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5312 - loss: 0.9517
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 1.0130 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 1.0033
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 1.0014
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5604 - loss: 0.9987
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5607 - loss: 0.9982 - val_accuracy: 0.5634 - val_loss: 0.9258
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0035
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5516 - loss: 0.9772 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 0.9777
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5620 - loss: 0.9771
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5626 - loss: 0.9774 - val_accuracy: 0.5936 - val_loss: 0.8799
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 0.9430
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5676 - loss: 0.9742 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5631 - loss: 0.9808
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5618 - loss: 0.9836
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5617 - loss: 0.9849 - val_accuracy: 0.5700 - val_loss: 0.8969
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1205
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 0.9996 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 0.9928
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5547 - loss: 0.9911
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5567 - loss: 0.9894
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5569 - loss: 0.9893 - val_accuracy: 0.5946 - val_loss: 0.8935
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.6406 - loss: 0.8916
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5678 - loss: 0.9989 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5678 - loss: 0.9947
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5682 - loss: 0.9904
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5681 - loss: 0.9872
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5681 - loss: 0.9869 - val_accuracy: 0.6032 - val_loss: 0.8780
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.6406 - loss: 0.9761
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5975 - loss: 0.9470 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5844 - loss: 0.9600
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5794 - loss: 0.9641
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5766 - loss: 0.9663
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5761 - loss: 0.9666 - val_accuracy: 0.5608 - val_loss: 0.9048
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0699
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5673 - loss: 0.9667 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5673 - loss: 0.9722
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5690 - loss: 0.9727
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5696 - loss: 0.9737 - val_accuracy: 0.6032 - val_loss: 0.8687
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0333
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5728 - loss: 0.9566 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5705 - loss: 0.9572
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5716 - loss: 0.9579
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5724 - loss: 0.9579 - val_accuracy: 0.6104 - val_loss: 0.8580
Epoch 64/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 0.9873
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5808 - loss: 0.9549 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5781 - loss: 0.9607
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5780 - loss: 0.9610
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5771 - loss: 0.9611
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5770 - loss: 0.9612 - val_accuracy: 0.6163 - val_loss: 0.8641
Epoch 65/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 0.9278
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5554 - loss: 0.9474 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5599 - loss: 0.9593
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5609 - loss: 0.9640
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5611 - loss: 0.9659 - val_accuracy: 0.5903 - val_loss: 0.8951
Epoch 66/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.9632
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5795 - loss: 0.9865 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5717 - loss: 0.9798
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5699 - loss: 0.9777
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5696 - loss: 0.9750 - val_accuracy: 0.5352 - val_loss: 0.9736
Epoch 67/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0082
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5583 - loss: 1.0013 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5659 - loss: 0.9837
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5683 - loss: 0.9774
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5688 - loss: 0.9742
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5688 - loss: 0.9742 - val_accuracy: 0.5992 - val_loss: 0.8834
Epoch 68/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0725
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5742 - loss: 0.9578 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5733 - loss: 0.9573
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5720 - loss: 0.9596
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5715 - loss: 0.9602 - val_accuracy: 0.5943 - val_loss: 0.8810

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 476ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 3: 56.31 [%]
F1-score capturado en la ejecución 3: 55.88 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:48[0m 689ms/step
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 767us/step  
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 713us/step
[1m221/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 690us/step
[1m297/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 682us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m72/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 705us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 60.28 [%]
Global F1 score (validation) = 59.37 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.23685443 0.17373382 0.3938318  0.19557996]
 [0.34595433 0.28574345 0.1725628  0.19573943]
 [0.290357   0.22496082 0.2795692  0.20511298]
 ...
 [0.03089443 0.01601805 0.9385559  0.01453161]
 [0.05731604 0.03226905 0.8801104  0.03030459]
 [0.03088258 0.01601309 0.9385814  0.01452296]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 62.13 [%]
Global accuracy score (test) = 55.86 [%]
Global F1 score (train) = 60.97 [%]
Global F1 score (test) = 55.23 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.25      0.30       400
MODERATE-INTENSITY       0.49      0.58      0.53       400
         SEDENTARY       0.56      0.85      0.67       400
VIGOROUS-INTENSITY       0.97      0.55      0.71       345

          accuracy                           0.56      1545
         macro avg       0.60      0.56      0.55      1545
      weighted avg       0.59      0.56      0.55      1545

2025-11-05 11:19:08.471498: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:19:08.482991: 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 00:00:1762337948.496467 3225564 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762337948.500769 3225564 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762337948.510900 3225564 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337948.510921 3225564 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337948.510923 3225564 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337948.510925 3225564 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:19:08.514245: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762337950.726463 3225564 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762337952.101640 3225694 service.cc:152] XLA service 0x78e27800b1b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762337952.101669 3225694 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:19:12.135211: 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 00:00:1762337952.259907 3225694 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762337954.367643 3225694 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:18[0m 3s/step - accuracy: 0.1875 - loss: 2.5766
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2661 - loss: 2.2428 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2776 - loss: 2.1268
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2812 - loss: 2.0538
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2835 - loss: 1.9892
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2839 - loss: 1.9775
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2839 - loss: 1.9763 - val_accuracy: 0.4011 - val_loss: 1.2816
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3125 - loss: 1.5101
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3049 - loss: 1.4794 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3128 - loss: 1.4659
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3153 - loss: 1.4571
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3161 - loss: 1.4490
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3163 - loss: 1.4471 - val_accuracy: 0.3939 - val_loss: 1.2738
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.3458
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.3670 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3346 - loss: 1.3638
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3350 - loss: 1.3610
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3358 - loss: 1.3590 - val_accuracy: 0.3830 - val_loss: 1.2614
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2266
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3413 - loss: 1.3298 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3396 - loss: 1.3333
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.3324
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3447 - loss: 1.3308 - val_accuracy: 0.3791 - val_loss: 1.2513
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.3038
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3538 - loss: 1.3301 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.3216
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3619 - loss: 1.3192
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3626 - loss: 1.3178 - val_accuracy: 0.3876 - val_loss: 1.2461
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2114
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3676 - loss: 1.2969 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3636 - loss: 1.2974
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.2974
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3618 - loss: 1.2966 - val_accuracy: 0.3840 - val_loss: 1.2335
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.3302
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3729 - loss: 1.2727 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3809 - loss: 1.2695
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3820 - loss: 1.2709
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3809 - loss: 1.2722 - val_accuracy: 0.3919 - val_loss: 1.2143
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 1.3668
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.2690 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3841 - loss: 1.2684
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.2673
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3846 - loss: 1.2659 - val_accuracy: 0.4008 - val_loss: 1.1901
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4219 - loss: 1.2298
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4006 - loss: 1.2480 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4005 - loss: 1.2466
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4008 - loss: 1.2437
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4004 - loss: 1.2426 - val_accuracy: 0.3844 - val_loss: 1.2187
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1547
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4133 - loss: 1.2136 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4078 - loss: 1.2222
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4045 - loss: 1.2270
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4036 - loss: 1.2286 - val_accuracy: 0.4376 - val_loss: 1.1661
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.3024
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4030 - loss: 1.2333 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4105 - loss: 1.2296
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4104 - loss: 1.2274
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.2263
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4097 - loss: 1.2261 - val_accuracy: 0.4471 - val_loss: 1.1367
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3438 - loss: 1.1965
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4037 - loss: 1.2019 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4055 - loss: 1.2063
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4049 - loss: 1.2091
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4054 - loss: 1.2098 - val_accuracy: 0.4465 - val_loss: 1.1223
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.1969
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4149 - loss: 1.2087 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4174 - loss: 1.2094
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4190 - loss: 1.2085
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4196 - loss: 1.2080 - val_accuracy: 0.4629 - val_loss: 1.1163
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.3070
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4258 - loss: 1.1803 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4270 - loss: 1.1830
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.1853
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4290 - loss: 1.1863 - val_accuracy: 0.4596 - val_loss: 1.1246
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2517
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4385 - loss: 1.1984 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4382 - loss: 1.1918
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4364 - loss: 1.1899
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4351 - loss: 1.1899 - val_accuracy: 0.4550 - val_loss: 1.1263
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2500 - loss: 1.2697
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.1914 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4224 - loss: 1.1917
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4260 - loss: 1.1884
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4278 - loss: 1.1862 - val_accuracy: 0.4376 - val_loss: 1.1316
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 1.2923
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.2078 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4401 - loss: 1.1951
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4408 - loss: 1.1885
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4410 - loss: 1.1854
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4410 - loss: 1.1853 - val_accuracy: 0.4488 - val_loss: 1.1080
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2479
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4452 - loss: 1.1887 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4481 - loss: 1.1772
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4486 - loss: 1.1733
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4491 - loss: 1.1716
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4491 - loss: 1.1713 - val_accuracy: 0.4777 - val_loss: 1.1014
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1989
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4250 - loss: 1.1737 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.1737
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4394 - loss: 1.1690
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4429 - loss: 1.1667 - val_accuracy: 0.4415 - val_loss: 1.1042
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.4208
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4586 - loss: 1.1868 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1754
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1690
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4587 - loss: 1.1656 - val_accuracy: 0.4974 - val_loss: 1.0499
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1478
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1389 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1408
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1422
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4711 - loss: 1.1434 - val_accuracy: 0.5076 - val_loss: 1.0596
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5781 - loss: 1.0822
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4774 - loss: 1.1312 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4716 - loss: 1.1379
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1397
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4687 - loss: 1.1412 - val_accuracy: 0.4750 - val_loss: 1.0683
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1657
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1243 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4681 - loss: 1.1327
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1344
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4673 - loss: 1.1362 - val_accuracy: 0.4980 - val_loss: 1.0591
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0917
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1356 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1329
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1334
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4758 - loss: 1.1331
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4759 - loss: 1.1331 - val_accuracy: 0.5164 - val_loss: 1.0545
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0701
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.1099 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5030 - loss: 1.1108
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1158
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4930 - loss: 1.1193 - val_accuracy: 0.4727 - val_loss: 1.0920

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 494ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 4: 55.86 [%]
F1-score capturado en la ejecución 4: 55.23 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:45[0m 680ms/step
[1m 60/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 853us/step  
[1m128/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 794us/step
[1m194/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 785us/step
[1m265/333[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 765us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 771us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 47.63 [%]
Global F1 score (validation) = 46.84 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.28806823 0.2601594  0.23854993 0.21322247]
 [0.27495885 0.24232997 0.28542042 0.19729067]
 [0.31117615 0.27938476 0.20428906 0.2051501 ]
 ...
 [0.12165215 0.09506295 0.7044988  0.07878605]
 [0.08398825 0.06377647 0.79957575 0.05265956]
 [0.1217727  0.0953448  0.70298827 0.07989415]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.22 [%]
Global accuracy score (test) = 48.41 [%]
Global F1 score (train) = 55.1 [%]
Global F1 score (test) = 47.67 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.35      0.38      0.37       400
MODERATE-INTENSITY       0.37      0.33      0.35       400
         SEDENTARY       0.57      0.81      0.67       400
VIGOROUS-INTENSITY       0.72      0.41      0.52       345

          accuracy                           0.48      1545
         macro avg       0.50      0.48      0.48      1545
      weighted avg       0.50      0.48      0.48      1545

2025-11-05 11:19:36.029468: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:19:36.040825: 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 00:00:1762337976.054062 3228812 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762337976.058154 3228812 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762337976.067950 3228812 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337976.067969 3228812 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337976.067972 3228812 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762337976.067974 3228812 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:19:36.071101: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762337978.304974 3228812 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762337979.684232 3228950 service.cc:152] XLA service 0x76bdac014ec0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762337979.684267 3228950 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:19:39.722349: 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 00:00:1762337979.842192 3228950 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762337981.944661 3228950 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:19[0m 3s/step - accuracy: 0.2969 - loss: 2.5083
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2824 - loss: 2.5509 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2767 - loss: 2.4730
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2779 - loss: 2.4111
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2809 - loss: 2.3443
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2810 - loss: 2.3429 - val_accuracy: 0.3587 - val_loss: 1.2880
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.2344 - loss: 1.7621
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2997 - loss: 1.7106 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3035 - loss: 1.6864
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3038 - loss: 1.6595
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3032 - loss: 1.6382 - val_accuracy: 0.3768 - val_loss: 1.2783
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2344 - loss: 1.4789
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3022 - loss: 1.4278 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.4226
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3122 - loss: 1.4157
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3135 - loss: 1.4109 - val_accuracy: 0.3827 - val_loss: 1.2814
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.3610
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.3818 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3386 - loss: 1.3698
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3405 - loss: 1.3646
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3416 - loss: 1.3607 - val_accuracy: 0.3798 - val_loss: 1.2797
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.3005
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.3194 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3583 - loss: 1.3218
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3577 - loss: 1.3222
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3578 - loss: 1.3220 - val_accuracy: 0.3814 - val_loss: 1.2636
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2257
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3943 - loss: 1.2874 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3898 - loss: 1.2940
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3864 - loss: 1.2971
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3846 - loss: 1.2978
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3839 - loss: 1.2980 - val_accuracy: 0.3847 - val_loss: 1.2539
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3750 - loss: 1.3515
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3643 - loss: 1.3061 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3676 - loss: 1.3005
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.2988
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3714 - loss: 1.2968 - val_accuracy: 0.4146 - val_loss: 1.2395
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2738
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3877 - loss: 1.2791 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.2760
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3847 - loss: 1.2737
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3847 - loss: 1.2724
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3847 - loss: 1.2723 - val_accuracy: 0.3949 - val_loss: 1.2223
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2599
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3876 - loss: 1.2485 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3805 - loss: 1.2577
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3809 - loss: 1.2583
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3819 - loss: 1.2579 - val_accuracy: 0.4014 - val_loss: 1.1976
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.1546
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4042 - loss: 1.2423 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4020 - loss: 1.2424
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4006 - loss: 1.2425
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3994 - loss: 1.2419
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3992 - loss: 1.2419 - val_accuracy: 0.4208 - val_loss: 1.2115
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2564
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3728 - loss: 1.2590 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3873 - loss: 1.2505
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3933 - loss: 1.2459
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3964 - loss: 1.2430 - val_accuracy: 0.4254 - val_loss: 1.1760
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.2598
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4286 - loss: 1.2249 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4224 - loss: 1.2235
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4200 - loss: 1.2227
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4188 - loss: 1.2221 - val_accuracy: 0.4461 - val_loss: 1.1523
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1982
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4195 - loss: 1.2341 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4132 - loss: 1.2281
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4129 - loss: 1.2232
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4130 - loss: 1.2206
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4130 - loss: 1.2202 - val_accuracy: 0.4471 - val_loss: 1.1397
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.1333
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4274 - loss: 1.2026 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4242 - loss: 1.2045
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4235 - loss: 1.2038
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4245 - loss: 1.2024 - val_accuracy: 0.4566 - val_loss: 1.1424
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2268
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4375 - loss: 1.2168 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4378 - loss: 1.2070
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4355 - loss: 1.2025
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4342 - loss: 1.1999 - val_accuracy: 0.4642 - val_loss: 1.1091
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.5000 - loss: 1.1011
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4240 - loss: 1.1844 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4279 - loss: 1.1862
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4295 - loss: 1.1861
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4303 - loss: 1.1863
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4303 - loss: 1.1863 - val_accuracy: 0.4543 - val_loss: 1.1275
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3125 - loss: 1.2195
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.1955 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.1878
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4270 - loss: 1.1860
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4287 - loss: 1.1848 - val_accuracy: 0.4698 - val_loss: 1.1048
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.2788
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4263 - loss: 1.1781 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4297 - loss: 1.1763
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4321 - loss: 1.1746
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4341 - loss: 1.1738
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4344 - loss: 1.1737 - val_accuracy: 0.4616 - val_loss: 1.1100
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0844
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4350 - loss: 1.1730 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4383 - loss: 1.1740
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4394 - loss: 1.1724
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.1716
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4402 - loss: 1.1715 - val_accuracy: 0.4859 - val_loss: 1.0921
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1467
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4517 - loss: 1.1340 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.1495
[1m135/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.1546
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4442 - loss: 1.1570 - val_accuracy: 0.5168 - val_loss: 1.0841
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0945
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1414 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1477
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1496
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4547 - loss: 1.1518
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4545 - loss: 1.1521 - val_accuracy: 0.4823 - val_loss: 1.0914
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.1734
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.1639 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4400 - loss: 1.1571
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.1549
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4462 - loss: 1.1542 - val_accuracy: 0.5016 - val_loss: 1.0916
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0878
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1272 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1373
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1449
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4648 - loss: 1.1475 - val_accuracy: 0.4671 - val_loss: 1.1093
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4219 - loss: 1.2724
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.1700 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1599
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4514 - loss: 1.1575
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4529 - loss: 1.1552
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4529 - loss: 1.1552 - val_accuracy: 0.4882 - val_loss: 1.0896
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1846
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4531 - loss: 1.1551 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4542 - loss: 1.1481
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4544 - loss: 1.1447
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4544 - loss: 1.1439 - val_accuracy: 0.4859 - val_loss: 1.0830
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2307
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4266 - loss: 1.1959 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.1742
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4511 - loss: 1.1651
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1579
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4558 - loss: 1.1574 - val_accuracy: 0.5000 - val_loss: 1.0646
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1458
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1424 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1402
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1399
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4708 - loss: 1.1394 - val_accuracy: 0.5039 - val_loss: 1.0494
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0978
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1262 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4828 - loss: 1.1202
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4843 - loss: 1.1197
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4835 - loss: 1.1209 - val_accuracy: 0.5013 - val_loss: 1.0837
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1025
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1550 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.1499
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4677 - loss: 1.1463
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4682 - loss: 1.1431 - val_accuracy: 0.5204 - val_loss: 1.0493
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9658
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0876 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1061
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1108
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1135 - val_accuracy: 0.5204 - val_loss: 1.0420
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0213
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1090 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1149
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1172
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4789 - loss: 1.1186
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1186 - val_accuracy: 0.5145 - val_loss: 1.0320
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0815
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1075 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1110
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1111
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4786 - loss: 1.1105 - val_accuracy: 0.5154 - val_loss: 1.0502
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0932
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1288 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1231
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1196
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.1166
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1165 - val_accuracy: 0.5299 - val_loss: 1.0217
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0507
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.0980 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1005
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1039
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4907 - loss: 1.1060 - val_accuracy: 0.5375 - val_loss: 1.0087
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.2015
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1336 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.1195
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1131
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4951 - loss: 1.1114 - val_accuracy: 0.5181 - val_loss: 1.0493
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1807
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1149 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1091
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1067
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4868 - loss: 1.1051 - val_accuracy: 0.5437 - val_loss: 1.0076
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5938 - loss: 0.9546
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.0938 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.0955
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.0946
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5014 - loss: 1.0932 - val_accuracy: 0.5394 - val_loss: 0.9970
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1507
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4881 - loss: 1.1149 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4950 - loss: 1.1037
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.0967
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5004 - loss: 1.0942 - val_accuracy: 0.5355 - val_loss: 1.0030
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1011
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0548 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0698
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0757
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5121 - loss: 1.0779
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5121 - loss: 1.0781 - val_accuracy: 0.4938 - val_loss: 1.1148
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.2961
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.1176 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1031
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.0948
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5068 - loss: 1.0911
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5068 - loss: 1.0908 - val_accuracy: 0.5489 - val_loss: 0.9840
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9776
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0810 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.0805
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5104 - loss: 1.0820
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5092 - loss: 1.0826 - val_accuracy: 0.5526 - val_loss: 1.0056
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0792
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5109 - loss: 1.0738 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0722
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0728
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5109 - loss: 1.0720 - val_accuracy: 0.5634 - val_loss: 0.9597
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.1744
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5201 - loss: 1.0766 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0829
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0838
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5161 - loss: 1.0834 - val_accuracy: 0.5246 - val_loss: 0.9836
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 0.9994
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5345 - loss: 1.0373 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0513
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0536
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0555
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5205 - loss: 1.0556 - val_accuracy: 0.4694 - val_loss: 1.1468
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.2656
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.0927 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.0823
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0755
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5157 - loss: 1.0723 - val_accuracy: 0.5660 - val_loss: 0.9743
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.4375 - loss: 1.1542
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0786 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0761
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5125 - loss: 1.0745
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5123 - loss: 1.0729 - val_accuracy: 0.5552 - val_loss: 0.9699
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2569
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0859 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0719
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0665
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5198 - loss: 1.0639 - val_accuracy: 0.5483 - val_loss: 0.9745

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 509ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 5: 48.41 [%]
F1-score capturado en la ejecución 5: 47.67 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:44[0m 678ms/step
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 836us/step  
[1m137/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 737us/step
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 704us/step
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 690us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 755us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 55.22 [%]
Global F1 score (validation) = 55.85 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.30088496 0.27904966 0.12559637 0.294469  ]
 [0.34672567 0.44177213 0.00373997 0.20776217]
 [0.31263447 0.28471723 0.13972746 0.2629209 ]
 ...
 [0.18190162 0.14145207 0.57785994 0.09878632]
 [0.1300524  0.0963028  0.7000797  0.07356516]
 [0.08097374 0.05658892 0.8159978  0.04643959]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 60.45 [%]
Global accuracy score (test) = 53.14 [%]
Global F1 score (train) = 60.62 [%]
Global F1 score (test) = 53.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.33      0.29      0.31       400
MODERATE-INTENSITY       0.44      0.60      0.51       400
         SEDENTARY       0.65      0.76      0.70       400
VIGOROUS-INTENSITY       0.89      0.47      0.61       345

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

2025-11-05 11:20:10.695832: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:20:10.707180: 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 00:00:1762338010.720259 3234070 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338010.724220 3234070 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338010.734255 3234070 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338010.734274 3234070 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338010.734276 3234070 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338010.734278 3234070 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:20:10.737511: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338013.052909 3234070 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338014.448574 3234177 service.cc:152] XLA service 0x72d298003290 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338014.448632 3234177 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:20:14.491346: 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 00:00:1762338014.614938 3234177 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338016.706758 3234177 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:18[0m 3s/step - accuracy: 0.2188 - loss: 2.5983
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2657 - loss: 2.4813 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2745 - loss: 2.3798
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 2.3085
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2811 - loss: 2.2444
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2815 - loss: 2.2307
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2816 - loss: 2.2294 - val_accuracy: 0.3985 - val_loss: 1.2756
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2969 - loss: 1.5960
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3034 - loss: 1.6137 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3044 - loss: 1.5936
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3048 - loss: 1.5744
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3056 - loss: 1.5578 - val_accuracy: 0.3926 - val_loss: 1.2836
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.1719 - loss: 1.5057
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3079 - loss: 1.4042 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3136 - loss: 1.3997
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.3938
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3197 - loss: 1.3878
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3199 - loss: 1.3875 - val_accuracy: 0.3870 - val_loss: 1.2692
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.4649
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3443 - loss: 1.3479 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3457 - loss: 1.3445
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.3412
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3484 - loss: 1.3392 - val_accuracy: 0.3834 - val_loss: 1.2682
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3438 - loss: 1.3109
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3566 - loss: 1.3146 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3581 - loss: 1.3137
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3602 - loss: 1.3125
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3611 - loss: 1.3120 - val_accuracy: 0.3903 - val_loss: 1.2538
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.2879
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3693 - loss: 1.3032 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3753 - loss: 1.3029
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3775 - loss: 1.3010
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3785 - loss: 1.2990
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3785 - loss: 1.2988 - val_accuracy: 0.3837 - val_loss: 1.2344
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2525
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3673 - loss: 1.2902 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3703 - loss: 1.2851
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.2811
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3754 - loss: 1.2790
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3755 - loss: 1.2789 - val_accuracy: 0.3991 - val_loss: 1.2130
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3155
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3656 - loss: 1.2806 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.2747
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3780 - loss: 1.2705
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3802 - loss: 1.2682
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3804 - loss: 1.2680 - val_accuracy: 0.4284 - val_loss: 1.1872
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1768
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3950 - loss: 1.2395 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3955 - loss: 1.2421
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3950 - loss: 1.2427
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3947 - loss: 1.2430
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3948 - loss: 1.2429 - val_accuracy: 0.4287 - val_loss: 1.1792
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 1.3097
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4039 - loss: 1.2297 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4039 - loss: 1.2307
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4037 - loss: 1.2299
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4038 - loss: 1.2295
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4039 - loss: 1.2295 - val_accuracy: 0.4300 - val_loss: 1.1592
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.1513
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4019 - loss: 1.2323 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4066 - loss: 1.2279
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4088 - loss: 1.2254
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4095 - loss: 1.2240
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4096 - loss: 1.2239 - val_accuracy: 0.4602 - val_loss: 1.1765
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.3841
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4035 - loss: 1.2318 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4132 - loss: 1.2212
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4147 - loss: 1.2177
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2150
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4159 - loss: 1.2149 - val_accuracy: 0.4750 - val_loss: 1.1222
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1342
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4363 - loss: 1.1831 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4342 - loss: 1.1893
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.1921
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4316 - loss: 1.1927 - val_accuracy: 0.4593 - val_loss: 1.1233
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1836
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4334 - loss: 1.1961 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.1966
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.1952
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4349 - loss: 1.1942 - val_accuracy: 0.4652 - val_loss: 1.1180
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.1718
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4523 - loss: 1.1674 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.1731
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4444 - loss: 1.1748
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4437 - loss: 1.1752 - val_accuracy: 0.4159 - val_loss: 1.2024
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2735
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4148 - loss: 1.2146 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2063
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4280 - loss: 1.1997
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4299 - loss: 1.1957 - val_accuracy: 0.4839 - val_loss: 1.0935
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0674
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4419 - loss: 1.1698 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4408 - loss: 1.1716
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4418 - loss: 1.1711
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4418 - loss: 1.1708 - val_accuracy: 0.5007 - val_loss: 1.0949
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.1565
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4252 - loss: 1.1820 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4315 - loss: 1.1748
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4359 - loss: 1.1716
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.1690
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4391 - loss: 1.1689 - val_accuracy: 0.4642 - val_loss: 1.1210
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3438 - loss: 1.1644
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4553 - loss: 1.1610 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4516 - loss: 1.1601
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4518 - loss: 1.1600
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4511 - loss: 1.1603 - val_accuracy: 0.4967 - val_loss: 1.0999
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1303
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4737 - loss: 1.1388 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1437
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1457
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4655 - loss: 1.1464 - val_accuracy: 0.5200 - val_loss: 1.0683
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0783
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4584 - loss: 1.1513 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1501
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1484
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.1472 - val_accuracy: 0.5043 - val_loss: 1.0726
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3906 - loss: 1.2106
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4568 - loss: 1.1598 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4558 - loss: 1.1587
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1574
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1556
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4596 - loss: 1.1555 - val_accuracy: 0.5158 - val_loss: 1.0632
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.6250 - loss: 0.9694
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1278 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1305
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1319
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1323
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4790 - loss: 1.1322 - val_accuracy: 0.5151 - val_loss: 1.0599
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0941
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.1332 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1318
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4614 - loss: 1.1325
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4626 - loss: 1.1334 - val_accuracy: 0.5108 - val_loss: 1.0531
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2072
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4450 - loss: 1.1393 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1332
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1278
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4694 - loss: 1.1256 - val_accuracy: 0.5174 - val_loss: 1.0555
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9948
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1192 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1266
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.1248
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4877 - loss: 1.1238 - val_accuracy: 0.5010 - val_loss: 1.0585
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0325
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1016 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4737 - loss: 1.1099
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1131
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4777 - loss: 1.1127 - val_accuracy: 0.4878 - val_loss: 1.0649
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3438 - loss: 1.1848
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1201 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1153
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4925 - loss: 1.1149
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1142 - val_accuracy: 0.5085 - val_loss: 1.0463
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1967
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1143 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1111
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1118
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4882 - loss: 1.1115 - val_accuracy: 0.4622 - val_loss: 1.0958
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2925
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1134 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1066
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1050
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4938 - loss: 1.1034 - val_accuracy: 0.4918 - val_loss: 1.0736
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0199
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1129 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1082
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1057
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4954 - loss: 1.1030 - val_accuracy: 0.4970 - val_loss: 1.0567
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.3561
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4836 - loss: 1.1307 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1107
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4934 - loss: 1.1058
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1040
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4943 - loss: 1.1039 - val_accuracy: 0.5112 - val_loss: 1.0189
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9386
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5047 - loss: 1.0847 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.0872
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.0881
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5004 - loss: 1.0881 - val_accuracy: 0.5352 - val_loss: 0.9864
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5781 - loss: 1.0668
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0798 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5237 - loss: 1.0709
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0718
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5180 - loss: 1.0731 - val_accuracy: 0.5562 - val_loss: 0.9794
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1000
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.0674 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0698
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0735
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5176 - loss: 1.0755 - val_accuracy: 0.5401 - val_loss: 0.9900
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1600
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0837 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0836
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0828
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5147 - loss: 1.0812 - val_accuracy: 0.5618 - val_loss: 0.9576
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0920
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0540 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0542
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0564
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5265 - loss: 1.0580 - val_accuracy: 0.5457 - val_loss: 0.9885
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9081
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0408 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0466
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0492
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0523
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5236 - loss: 1.0526 - val_accuracy: 0.5483 - val_loss: 0.9857
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1465
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0525 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0491
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0497
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5288 - loss: 1.0518 - val_accuracy: 0.5539 - val_loss: 0.9932
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0628
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5362 - loss: 1.0600 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0613
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0624
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5265 - loss: 1.0622 - val_accuracy: 0.5614 - val_loss: 0.9523
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.0757
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5300 - loss: 1.0376 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0439
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0460
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5305 - loss: 1.0461 - val_accuracy: 0.5443 - val_loss: 0.9792
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0375
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0293 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0358
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0371
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5295 - loss: 1.0381 - val_accuracy: 0.5614 - val_loss: 0.9770
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0163
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0449 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0468
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0481
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5385 - loss: 1.0473 - val_accuracy: 0.5677 - val_loss: 0.9569
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1643
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5479 - loss: 1.0363 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0291
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0309
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5441 - loss: 1.0316 - val_accuracy: 0.5526 - val_loss: 0.9607
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1171
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5205 - loss: 1.0550 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0433
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0409
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5301 - loss: 1.0401 - val_accuracy: 0.5788 - val_loss: 0.9184
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0226
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0168 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0201
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5448 - loss: 1.0242
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5439 - loss: 1.0254 - val_accuracy: 0.5696 - val_loss: 0.9355
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9657
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0314 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0237
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0209
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5435 - loss: 1.0218 - val_accuracy: 0.5384 - val_loss: 0.9584
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9645
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0190 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0230
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0239
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5369 - loss: 1.0240 - val_accuracy: 0.5641 - val_loss: 0.9455
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5625 - loss: 1.3216
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0679 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0441
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0366
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5434 - loss: 1.0328 - val_accuracy: 0.5867 - val_loss: 0.9228
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.9334
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5724 - loss: 0.9998 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5626 - loss: 1.0112
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 1.0141
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5563 - loss: 1.0145 - val_accuracy: 0.5693 - val_loss: 0.9447

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 487ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 6: 53.14 [%]
F1-score capturado en la ejecución 6: 53.22 [%]

=== EJECUCIÓN 7 ===

--- 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:52[0m 701ms/step
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 690us/step  
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 678us/step
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 692us/step
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 694us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 741us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 56.24 [%]
Global F1 score (validation) = 56.53 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4006471  0.37678462 0.05490411 0.16766414]
 [0.3148224  0.2594031  0.19452201 0.23125246]
 [0.39694044 0.3494948  0.095231   0.1583338 ]
 ...
 [0.05632651 0.03239945 0.8734294  0.03784463]
 [0.15959004 0.10591396 0.6285568  0.10593924]
 [0.05724187 0.0329888  0.87130696 0.03846236]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 62.04 [%]
Global accuracy score (test) = 56.89 [%]
Global F1 score (train) = 62.23 [%]
Global F1 score (test) = 56.81 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.41      0.42       400
MODERATE-INTENSITY       0.47      0.48      0.47       400
         SEDENTARY       0.62      0.88      0.72       400
VIGOROUS-INTENSITY       0.97      0.50      0.66       345

          accuracy                           0.57      1545
         macro avg       0.62      0.57      0.57      1545
      weighted avg       0.61      0.57      0.57      1545

2025-11-05 11:20:46.384313: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:20:46.395601: 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 00:00:1762338046.408750 3239587 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338046.412904 3239587 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338046.422668 3239587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338046.422687 3239587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338046.422689 3239587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338046.422690 3239587 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:20:46.425846: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338048.655567 3239587 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338050.062578 3239692 service.cc:152] XLA service 0x7b6410009a30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338050.062632 3239692 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:20:50.103679: 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 00:00:1762338050.227401 3239692 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338052.335866 3239692 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:22[0m 3s/step - accuracy: 0.2500 - loss: 2.4792
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2882 - loss: 2.3131 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2892 - loss: 2.2600
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2910 - loss: 2.2017
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2923 - loss: 2.1549
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2924 - loss: 2.1514
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2925 - loss: 2.1502 - val_accuracy: 0.3975 - val_loss: 1.2393
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3906 - loss: 1.6122
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3448 - loss: 1.5572 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.5456
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3342 - loss: 1.5322
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3326 - loss: 1.5196 - val_accuracy: 0.4011 - val_loss: 1.2549
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2031 - loss: 1.5790
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3377 - loss: 1.3928 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.3798
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3406 - loss: 1.3734
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3417 - loss: 1.3692 - val_accuracy: 0.3929 - val_loss: 1.2552
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.3110
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3561 - loss: 1.3247 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3581 - loss: 1.3216
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3597 - loss: 1.3197
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3603 - loss: 1.3190 - val_accuracy: 0.3798 - val_loss: 1.2542
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2356
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3536 - loss: 1.3049 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3596 - loss: 1.3046
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3636 - loss: 1.3031
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3659 - loss: 1.3018 - val_accuracy: 0.3857 - val_loss: 1.2454
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2678
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3747 - loss: 1.2743 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3696 - loss: 1.2804
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3684 - loss: 1.2819
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3689 - loss: 1.2823
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3690 - loss: 1.2823 - val_accuracy: 0.3939 - val_loss: 1.2173
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2382
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3874 - loss: 1.2819 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3883 - loss: 1.2774
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3885 - loss: 1.2731
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3883 - loss: 1.2709 - val_accuracy: 0.4005 - val_loss: 1.2039
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.3134
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3735 - loss: 1.2673 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3795 - loss: 1.2614
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3815 - loss: 1.2596
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3829 - loss: 1.2579
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3829 - loss: 1.2578 - val_accuracy: 0.4067 - val_loss: 1.2027
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2674
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3973 - loss: 1.2381 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3963 - loss: 1.2388
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3967 - loss: 1.2389
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3976 - loss: 1.2385
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3977 - loss: 1.2384 - val_accuracy: 0.4635 - val_loss: 1.1610
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1559
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4078 - loss: 1.2260 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4076 - loss: 1.2266
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4088 - loss: 1.2263
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4087 - loss: 1.2258 - val_accuracy: 0.4399 - val_loss: 1.1541
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1766
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2147 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4225 - loss: 1.2083
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4232 - loss: 1.2064
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4222 - loss: 1.2060 - val_accuracy: 0.4350 - val_loss: 1.1536
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4219 - loss: 1.2371
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4218 - loss: 1.2179 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4245 - loss: 1.2099
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4249 - loss: 1.2050
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4246 - loss: 1.2026 - val_accuracy: 0.4767 - val_loss: 1.1187
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.2051
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4362 - loss: 1.1767 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.1839
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4297 - loss: 1.1872
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4298 - loss: 1.1887
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4298 - loss: 1.1888 - val_accuracy: 0.4655 - val_loss: 1.1129
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2085
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.1938 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.1910
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4374 - loss: 1.1884
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.1864
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4371 - loss: 1.1863 - val_accuracy: 0.4928 - val_loss: 1.0834
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0711
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4294 - loss: 1.1850 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.1779
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.1769
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4366 - loss: 1.1765
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4366 - loss: 1.1766 - val_accuracy: 0.4941 - val_loss: 1.0869
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1318
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4360 - loss: 1.1563 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4337 - loss: 1.1632
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.1655
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4364 - loss: 1.1659
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4369 - loss: 1.1658 - val_accuracy: 0.4888 - val_loss: 1.0690
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2055
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1613 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1625
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.1622
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4552 - loss: 1.1629 - val_accuracy: 0.5131 - val_loss: 1.0783
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0567
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1528 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.1559
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4553 - loss: 1.1553
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4555 - loss: 1.1552 - val_accuracy: 0.5039 - val_loss: 1.0739
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.0890
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1505 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4557 - loss: 1.1530
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4558 - loss: 1.1543
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4557 - loss: 1.1541 - val_accuracy: 0.4967 - val_loss: 1.0505
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0347
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1295 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1371
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1390
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4738 - loss: 1.1403 - val_accuracy: 0.4458 - val_loss: 1.1000
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2073
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1390 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4816 - loss: 1.1356
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1366
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4763 - loss: 1.1366 - val_accuracy: 0.5102 - val_loss: 1.0423
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.1527
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.1613 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4505 - loss: 1.1508
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1432
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4614 - loss: 1.1408
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4616 - loss: 1.1407 - val_accuracy: 0.5220 - val_loss: 1.0388
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1121
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5018 - loss: 1.1268 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1282
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1288
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4817 - loss: 1.1291
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4815 - loss: 1.1291 - val_accuracy: 0.4921 - val_loss: 1.0671
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5000 - loss: 1.0302
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1030 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1110
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1145
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1168
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4753 - loss: 1.1169 - val_accuracy: 0.5158 - val_loss: 1.0335
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1886
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4725 - loss: 1.1261 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1233
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1212
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1196
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4832 - loss: 1.1195 - val_accuracy: 0.5066 - val_loss: 1.0471
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0788
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0898 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1040
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.1080
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4961 - loss: 1.1095 - val_accuracy: 0.5105 - val_loss: 1.0652
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.4062 - loss: 1.3031
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1315 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1211
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.1167
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4877 - loss: 1.1152 - val_accuracy: 0.4724 - val_loss: 1.0969
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2985
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1359 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1225
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1174
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4858 - loss: 1.1151 - val_accuracy: 0.4701 - val_loss: 1.1235
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2102
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0921 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0906
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.0899
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.0905
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5009 - loss: 1.0906 - val_accuracy: 0.5309 - val_loss: 1.0355

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 472ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 7: 56.89 [%]
F1-score capturado en la ejecución 7: 56.81 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:45[0m 681ms/step
[1m 62/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 833us/step  
[1m134/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 760us/step
[1m208/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 730us/step
[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 720us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 762us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 53.29 [%]
Global F1 score (validation) = 52.17 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.33833906 0.29870197 0.13981232 0.22314669]
 [0.27357998 0.2533701  0.14341047 0.3296394 ]
 [0.2983186  0.25455838 0.21571307 0.2314099 ]
 ...
 [0.05549205 0.03338674 0.880295   0.03082632]
 [0.06449816 0.03959495 0.8577514  0.03815548]
 [0.131754   0.08833054 0.70592666 0.07398883]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 57.37 [%]
Global accuracy score (test) = 50.42 [%]
Global F1 score (train) = 56.6 [%]
Global F1 score (test) = 48.76 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.39      0.40       400
MODERATE-INTENSITY       0.40      0.40      0.40       400
         SEDENTARY       0.57      0.87      0.69       400
VIGOROUS-INTENSITY       0.74      0.34      0.46       345

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

2025-11-05 11:21:15.245426: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:21:15.257123: 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 00:00:1762338075.270409 3243184 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338075.274809 3243184 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338075.284894 3243184 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338075.284915 3243184 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338075.284917 3243184 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338075.284919 3243184 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:21:15.288120: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338077.526365 3243184 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338078.904316 3243298 service.cc:152] XLA service 0x758f8401cb60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338078.904343 3243298 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:21:18.939420: 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 00:00:1762338079.061978 3243298 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338081.191660 3243298 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:24[0m 3s/step - accuracy: 0.2500 - loss: 2.3011
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2816 - loss: 2.3447 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2822 - loss: 2.2849
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2829 - loss: 2.2257
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2843 - loss: 2.1757
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2846 - loss: 2.1681
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2847 - loss: 2.1668 - val_accuracy: 0.3893 - val_loss: 1.2702
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.6533
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.6027 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3110 - loss: 1.5721
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3143 - loss: 1.5469
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3155 - loss: 1.5305 - val_accuracy: 0.3985 - val_loss: 1.2604
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3278
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3423 - loss: 1.3802 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.3787
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3381 - loss: 1.3754
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3380 - loss: 1.3721 - val_accuracy: 0.4074 - val_loss: 1.2539
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.4019
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3139 - loss: 1.3696 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3290 - loss: 1.3550
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3352 - loss: 1.3480
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3392 - loss: 1.3436 - val_accuracy: 0.3876 - val_loss: 1.2431
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.3398
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3965 - loss: 1.2952 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3848 - loss: 1.2958
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3806 - loss: 1.2973
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3780 - loss: 1.2989 - val_accuracy: 0.3972 - val_loss: 1.2303
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2246
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3640 - loss: 1.2891 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3665 - loss: 1.2882
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3679 - loss: 1.2875
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3690 - loss: 1.2866
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3691 - loss: 1.2865 - val_accuracy: 0.3991 - val_loss: 1.2095
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2739
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3745 - loss: 1.2683 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3710 - loss: 1.2711
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3705 - loss: 1.2712
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3704 - loss: 1.2714 - val_accuracy: 0.3962 - val_loss: 1.2132
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.2745
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3976 - loss: 1.2503 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3972 - loss: 1.2525
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3969 - loss: 1.2535
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3969 - loss: 1.2535
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3969 - loss: 1.2535 - val_accuracy: 0.3991 - val_loss: 1.1882
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.3262
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3688 - loss: 1.2632 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3825 - loss: 1.2500
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3867 - loss: 1.2459
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3889 - loss: 1.2435 - val_accuracy: 0.4159 - val_loss: 1.1656
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2794
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4233 - loss: 1.2485 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4152 - loss: 1.2467
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4126 - loss: 1.2455
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4108 - loss: 1.2442
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4107 - loss: 1.2440 - val_accuracy: 0.4773 - val_loss: 1.1490
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4688 - loss: 1.1870
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4342 - loss: 1.1826 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.1956
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.2017
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4221 - loss: 1.2052
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4220 - loss: 1.2053 - val_accuracy: 0.4244 - val_loss: 1.1766
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1952
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4187 - loss: 1.2230 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4194 - loss: 1.2178
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4193 - loss: 1.2159
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4205 - loss: 1.2141 - val_accuracy: 0.4478 - val_loss: 1.1405
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1439
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4243 - loss: 1.2153 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2112
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2090
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4216 - loss: 1.2083 - val_accuracy: 0.4573 - val_loss: 1.1354
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2338
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4191 - loss: 1.2285 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4267 - loss: 1.2120
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4299 - loss: 1.2060
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4315 - loss: 1.2022 - val_accuracy: 0.4632 - val_loss: 1.1270
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.4688 - loss: 1.1745
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.1806 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.1832
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.1853
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.1854
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4376 - loss: 1.1854 - val_accuracy: 0.4786 - val_loss: 1.1005
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2561
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.2133 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4281 - loss: 1.2064
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4301 - loss: 1.2005
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4326 - loss: 1.1954 - val_accuracy: 0.4734 - val_loss: 1.1354
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1836
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4503 - loss: 1.1769 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4511 - loss: 1.1733
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.1738
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4483 - loss: 1.1750 - val_accuracy: 0.4415 - val_loss: 1.1122
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1471
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4350 - loss: 1.1870 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4391 - loss: 1.1817
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.1795
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4418 - loss: 1.1775 - val_accuracy: 0.4773 - val_loss: 1.1024
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.4531 - loss: 1.1741
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4450 - loss: 1.1653 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4488 - loss: 1.1660
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4501 - loss: 1.1667
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4507 - loss: 1.1664 - val_accuracy: 0.4967 - val_loss: 1.0902
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3081
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1702 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1620
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1593
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4588 - loss: 1.1592 - val_accuracy: 0.4898 - val_loss: 1.1011
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.0713
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4605 - loss: 1.1339 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1400
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1436
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4570 - loss: 1.1465 - val_accuracy: 0.5007 - val_loss: 1.0817
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3750 - loss: 1.1493
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1704 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1644
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1613
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4570 - loss: 1.1602 - val_accuracy: 0.5187 - val_loss: 1.0751
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1036
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1295 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4671 - loss: 1.1343
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1359
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4644 - loss: 1.1366 - val_accuracy: 0.4836 - val_loss: 1.0786
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.0987
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1293 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1312
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1330
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1349
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4625 - loss: 1.1354 - val_accuracy: 0.4947 - val_loss: 1.0558
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2594
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1394 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4725 - loss: 1.1399
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1406
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1403
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4696 - loss: 1.1401 - val_accuracy: 0.5177 - val_loss: 1.0471
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1202
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4576 - loss: 1.1463 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1495
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1474
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1457
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4614 - loss: 1.1456 - val_accuracy: 0.4993 - val_loss: 1.0533
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5000 - loss: 1.1057
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1092 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1143
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1165
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4803 - loss: 1.1192 - val_accuracy: 0.5273 - val_loss: 1.0426
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0982
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1266 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1277
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1267
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4799 - loss: 1.1271 - val_accuracy: 0.5407 - val_loss: 1.0252
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0987
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1288 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1258
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1243
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4857 - loss: 1.1244 - val_accuracy: 0.5473 - val_loss: 1.0323
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0786
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4754 - loss: 1.1362 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1347
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1310
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4776 - loss: 1.1288 - val_accuracy: 0.5315 - val_loss: 1.0606
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.2275
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1470 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1323
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4847 - loss: 1.1268
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4858 - loss: 1.1237 - val_accuracy: 0.5289 - val_loss: 1.0325
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1363
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4719 - loss: 1.1267 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.1254
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4826 - loss: 1.1221
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1200
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4847 - loss: 1.1199 - val_accuracy: 0.5499 - val_loss: 0.9918
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9776
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.0991 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5001 - loss: 1.1049
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1075
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.1070
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4944 - loss: 1.1070 - val_accuracy: 0.5355 - val_loss: 1.0131
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2888
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1196 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4921 - loss: 1.1090
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1051
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4926 - loss: 1.1047 - val_accuracy: 0.4839 - val_loss: 1.0997
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3594 - loss: 1.3062
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1142 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.1039
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.0988
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4973 - loss: 1.0967 - val_accuracy: 0.5473 - val_loss: 1.0122
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1065
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1015 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1023
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.1008
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4972 - loss: 1.0997 - val_accuracy: 0.5493 - val_loss: 0.9865
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.4844 - loss: 1.1225
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.0796 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.0752
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.0743
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5090 - loss: 1.0745 - val_accuracy: 0.5900 - val_loss: 0.9598
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1601
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0746 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0760
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0739
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0734
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5133 - loss: 1.0734 - val_accuracy: 0.5664 - val_loss: 0.9650
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0582
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5219 - loss: 1.0794 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0797
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0774
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0760
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5182 - loss: 1.0760 - val_accuracy: 0.5335 - val_loss: 1.0015
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1904
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0633 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0600
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0616
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0642
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5150 - loss: 1.0643 - val_accuracy: 0.5618 - val_loss: 0.9576
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0674
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0825 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.0784
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5131 - loss: 1.0750
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0735
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5135 - loss: 1.0734 - val_accuracy: 0.5542 - val_loss: 0.9569
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.0211
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0593 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0595
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0607
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5200 - loss: 1.0607 - val_accuracy: 0.5644 - val_loss: 0.9282
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0856
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0404 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0526
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0521
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0514
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5263 - loss: 1.0515 - val_accuracy: 0.5614 - val_loss: 0.9774
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0686
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0583 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0532
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0498
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0489
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5297 - loss: 1.0489 - val_accuracy: 0.5700 - val_loss: 0.9562
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1306
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5264 - loss: 1.0545 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0565
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0572
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5247 - loss: 1.0573 - val_accuracy: 0.5726 - val_loss: 0.9110
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5156 - loss: 1.0206
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0334 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0342
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0338
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0342
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5418 - loss: 1.0344 - val_accuracy: 0.5841 - val_loss: 0.9068
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6094 - loss: 1.0269
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0596 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0486
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0422
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5353 - loss: 1.0389 - val_accuracy: 0.5769 - val_loss: 0.9115
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1724
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5252 - loss: 1.0726 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0512
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0439
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5421 - loss: 1.0415 - val_accuracy: 0.5480 - val_loss: 0.9887
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1829
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5504 - loss: 1.0146 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0091
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5469 - loss: 1.0120
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5466 - loss: 1.0141 - val_accuracy: 0.5917 - val_loss: 0.9141
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.1980
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0637 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0518
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0461
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5420 - loss: 1.0435 - val_accuracy: 0.5936 - val_loss: 0.8936
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5156 - loss: 1.1900
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0419 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0352
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 1.0322
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5354 - loss: 1.0316 - val_accuracy: 0.5831 - val_loss: 0.9010
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5469 - loss: 1.0553
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5373 - loss: 1.0486 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0458
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5346 - loss: 1.0424
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0408
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5339 - loss: 1.0407 - val_accuracy: 0.5844 - val_loss: 0.8954
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9835
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5618 - loss: 0.9909 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 1.0014
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5532 - loss: 1.0033
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5522 - loss: 1.0051 - val_accuracy: 0.5821 - val_loss: 0.8940
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 0.9685
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0506 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0303
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0216
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0166
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5439 - loss: 1.0165 - val_accuracy: 0.6097 - val_loss: 0.8789
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9947
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 1.0066 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0063
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5479 - loss: 1.0083
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5481 - loss: 1.0093 - val_accuracy: 0.5867 - val_loss: 0.8870
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0024
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 0.9939 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0004
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5469 - loss: 1.0044
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5474 - loss: 1.0050 - val_accuracy: 0.6078 - val_loss: 0.8753
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 1.0250
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5536 - loss: 1.0002 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5541 - loss: 0.9969
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 0.9971
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5538 - loss: 0.9981 - val_accuracy: 0.5903 - val_loss: 0.8772
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0379
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 1.0244 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5468 - loss: 1.0152
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 1.0101
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5503 - loss: 1.0085 - val_accuracy: 0.5917 - val_loss: 0.8760
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.6250 - loss: 0.9429
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5634 - loss: 0.9801 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5577 - loss: 0.9893
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5564 - loss: 0.9925
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5549 - loss: 0.9950
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5548 - loss: 0.9951 - val_accuracy: 0.5920 - val_loss: 0.8870
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.7031 - loss: 0.8660
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5648 - loss: 1.0137 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5599 - loss: 1.0110
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 1.0069
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5591 - loss: 1.0046 - val_accuracy: 0.6028 - val_loss: 0.8728
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6719 - loss: 0.9637
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5842 - loss: 0.9617 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5726 - loss: 0.9757
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5680 - loss: 0.9804
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5652 - loss: 0.9831 - val_accuracy: 0.5963 - val_loss: 0.8918
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0519
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0053 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5427 - loss: 1.0004
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0007
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5454 - loss: 1.0001 - val_accuracy: 0.6078 - val_loss: 0.8471
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5938 - loss: 0.8536
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5502 - loss: 0.9751 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5500 - loss: 0.9825
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 0.9843
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5493 - loss: 0.9857 - val_accuracy: 0.5706 - val_loss: 0.8933
Epoch 64/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 34ms/step - accuracy: 0.5312 - loss: 0.9288
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0056 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0001
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 0.9967
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5464 - loss: 0.9956 - val_accuracy: 0.5673 - val_loss: 0.9120
Epoch 65/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.3894
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5610 - loss: 1.0208 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5617 - loss: 1.0051
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5612 - loss: 1.0000
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5604 - loss: 0.9961
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5604 - loss: 0.9952 - val_accuracy: 0.5851 - val_loss: 0.9073
Epoch 66/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0409
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5598 - loss: 1.0077 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5629 - loss: 0.9929
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5626 - loss: 0.9903
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5622 - loss: 0.9891 - val_accuracy: 0.5910 - val_loss: 0.8687
Epoch 67/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 1.0518
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5709 - loss: 0.9759 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5660 - loss: 0.9760
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5635 - loss: 0.9791
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5630 - loss: 0.9802 - val_accuracy: 0.6035 - val_loss: 0.8796

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 477ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 8: 50.42 [%]
F1-score capturado en la ejecución 8: 48.76 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:40[0m 664ms/step
[1m 59/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 863us/step  
[1m127/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 795us/step
[1m197/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 769us/step
[1m271/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 744us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m64/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 802us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 60.74 [%]
Global F1 score (validation) = 59.48 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.39791688 0.50676084 0.00282085 0.09250143]
 [0.2920822  0.24368703 0.20721853 0.25701225]
 [0.30578193 0.2585599  0.18446608 0.2511921 ]
 ...
 [0.04004    0.02190078 0.91730165 0.02075757]
 [0.03982113 0.02177207 0.91775656 0.0206502 ]
 [0.04976591 0.02797457 0.8957408  0.02651872]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 62.41 [%]
Global accuracy score (test) = 54.95 [%]
Global F1 score (train) = 60.59 [%]
Global F1 score (test) = 54.06 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.25      0.31       400
MODERATE-INTENSITY       0.47      0.49      0.48       400
         SEDENTARY       0.53      0.89      0.67       400
VIGOROUS-INTENSITY       0.93      0.57      0.70       345

          accuracy                           0.55      1545
         macro avg       0.59      0.55      0.54      1545
      weighted avg       0.57      0.55      0.53      1545

2025-11-05 11:21:56.453586: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:21:56.465025: 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 00:00:1762338116.478386 3250263 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338116.482387 3250263 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338116.492539 3250263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338116.492561 3250263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338116.492563 3250263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338116.492565 3250263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:21:56.495627: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338118.737725 3250263 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338120.120146 3250372 service.cc:152] XLA service 0x7e419c0052a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338120.120191 3250372 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:22:00.156871: 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 00:00:1762338120.277897 3250372 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338122.370568 3250372 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:18[0m 3s/step - accuracy: 0.2656 - loss: 2.5281
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2826 - loss: 2.3205 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 2.2374
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2881 - loss: 2.1644
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2886 - loss: 2.1076
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2886 - loss: 2.1064
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2886 - loss: 2.1051 - val_accuracy: 0.3844 - val_loss: 1.2710
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.4683
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.5355 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3045 - loss: 1.5183
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3047 - loss: 1.5029
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3057 - loss: 1.4895 - val_accuracy: 0.3876 - val_loss: 1.2817
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2656 - loss: 1.4150
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3164 - loss: 1.3851 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3214 - loss: 1.3748
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3256 - loss: 1.3704
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3282 - loss: 1.3671 - val_accuracy: 0.3867 - val_loss: 1.2729
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2799
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3646 - loss: 1.3099 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.3198
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.3212
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3581 - loss: 1.3220
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3581 - loss: 1.3221 - val_accuracy: 0.3798 - val_loss: 1.2613
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.3197
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.3247 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3454 - loss: 1.3196
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3494 - loss: 1.3156
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3515 - loss: 1.3131 - val_accuracy: 0.3883 - val_loss: 1.2492
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.3566
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.2894 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3764 - loss: 1.2886
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3763 - loss: 1.2884
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3763 - loss: 1.2882 - val_accuracy: 0.3844 - val_loss: 1.2286
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.2242
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3929 - loss: 1.2599 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3856 - loss: 1.2641
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3825 - loss: 1.2664
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3812 - loss: 1.2675 - val_accuracy: 0.3870 - val_loss: 1.2109
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.1777
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4015 - loss: 1.2359 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3992 - loss: 1.2407
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3956 - loss: 1.2447
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3925 - loss: 1.2477 - val_accuracy: 0.3857 - val_loss: 1.2081
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3438 - loss: 1.2544
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4038 - loss: 1.2354 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3997 - loss: 1.2368
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3978 - loss: 1.2377
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3960 - loss: 1.2387 - val_accuracy: 0.4054 - val_loss: 1.1853
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.3114
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4124 - loss: 1.2524 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4079 - loss: 1.2459
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4055 - loss: 1.2430
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4036 - loss: 1.2405
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4035 - loss: 1.2403 - val_accuracy: 0.4376 - val_loss: 1.1848
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2181
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4052 - loss: 1.2193 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4077 - loss: 1.2169
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4085 - loss: 1.2164
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4082 - loss: 1.2166 - val_accuracy: 0.4241 - val_loss: 1.1575
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0905
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4068 - loss: 1.2076 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4033 - loss: 1.2136
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4022 - loss: 1.2145
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4024 - loss: 1.2139 - val_accuracy: 0.4379 - val_loss: 1.1498
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.1371
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4072 - loss: 1.2002 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4058 - loss: 1.2077
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4072 - loss: 1.2090
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4088 - loss: 1.2083 - val_accuracy: 0.4333 - val_loss: 1.1416
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1041
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4089 - loss: 1.1955 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4124 - loss: 1.1991
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4126 - loss: 1.2000
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4138 - loss: 1.1990 - val_accuracy: 0.4488 - val_loss: 1.1285
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4219 - loss: 1.1915
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3997 - loss: 1.2267 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4051 - loss: 1.2201
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4100 - loss: 1.2131
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4122 - loss: 1.2087 - val_accuracy: 0.4547 - val_loss: 1.1291
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5156 - loss: 1.0410
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4189 - loss: 1.1822 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4154 - loss: 1.1864
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4173 - loss: 1.1862
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4186 - loss: 1.1860 - val_accuracy: 0.4442 - val_loss: 1.1564
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2805
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4474 - loss: 1.1731 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4416 - loss: 1.1744
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4393 - loss: 1.1736
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4369 - loss: 1.1743
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4368 - loss: 1.1743 - val_accuracy: 0.4465 - val_loss: 1.1237
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.3013
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4310 - loss: 1.1884 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.1826
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4364 - loss: 1.1800
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4368 - loss: 1.1781 - val_accuracy: 0.4596 - val_loss: 1.1173
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1851
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4235 - loss: 1.1743 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4243 - loss: 1.1712
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4263 - loss: 1.1700
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4279 - loss: 1.1700 - val_accuracy: 0.4442 - val_loss: 1.1083
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1816
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4548 - loss: 1.1783 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1697
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4532 - loss: 1.1668
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4526 - loss: 1.1643
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4524 - loss: 1.1640 - val_accuracy: 0.4520 - val_loss: 1.0972
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.1671
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.1713 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4477 - loss: 1.1660
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4477 - loss: 1.1637
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4475 - loss: 1.1627 - val_accuracy: 0.4678 - val_loss: 1.0891
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1780
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1606 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1561
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1548
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4534 - loss: 1.1545
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4534 - loss: 1.1546 - val_accuracy: 0.4671 - val_loss: 1.0917
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1271
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4407 - loss: 1.1535 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4457 - loss: 1.1510
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4486 - loss: 1.1479
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4507 - loss: 1.1467 - val_accuracy: 0.4602 - val_loss: 1.0830
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 1.2185
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.1368 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.1422
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.1439
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4516 - loss: 1.1438 - val_accuracy: 0.4632 - val_loss: 1.0928
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5312 - loss: 1.0904
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1445 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1401
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1378
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4690 - loss: 1.1378 - val_accuracy: 0.4553 - val_loss: 1.0783
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5469 - loss: 1.1636
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1468 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1465
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4759 - loss: 1.1433
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4752 - loss: 1.1421 - val_accuracy: 0.4675 - val_loss: 1.0638
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1134
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4819 - loss: 1.1166 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1202
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4703 - loss: 1.1249
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1274
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4685 - loss: 1.1275 - val_accuracy: 0.4757 - val_loss: 1.0995
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9352
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1223 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1256
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1288
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1292 - val_accuracy: 0.4865 - val_loss: 1.0682
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5156 - loss: 1.1075
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1499 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1426
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1360
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4690 - loss: 1.1315 - val_accuracy: 0.4796 - val_loss: 1.0740
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.1586
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1152 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4855 - loss: 1.1154
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4814 - loss: 1.1188
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4796 - loss: 1.1196 - val_accuracy: 0.4977 - val_loss: 1.0630
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.0491
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1164 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1210
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1187
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1179
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4753 - loss: 1.1179 - val_accuracy: 0.4941 - val_loss: 1.0602
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0128
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1143 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1173
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1151
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1135
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4919 - loss: 1.1134 - val_accuracy: 0.5049 - val_loss: 1.0463
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1628
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.0979 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.0984
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.0984
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4937 - loss: 1.0982 - val_accuracy: 0.4895 - val_loss: 1.0549
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1722
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5073 - loss: 1.1029 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.1030
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1022
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4997 - loss: 1.1021 - val_accuracy: 0.5039 - val_loss: 1.0397
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4219 - loss: 1.1485
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.1157 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5109 - loss: 1.1047
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.0993
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.0964
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5091 - loss: 1.0963 - val_accuracy: 0.4964 - val_loss: 1.0283
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2178
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1027 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1013
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.0996
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4972 - loss: 1.0979 - val_accuracy: 0.4918 - val_loss: 1.0346
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.0876
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.0752 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0736
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0740
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5115 - loss: 1.0745 - val_accuracy: 0.5089 - val_loss: 1.0350
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1232
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0648 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5199 - loss: 1.0628
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0639
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0656
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5168 - loss: 1.0658 - val_accuracy: 0.5365 - val_loss: 0.9968
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9257
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0648 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0711
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0726
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5178 - loss: 1.0730 - val_accuracy: 0.5108 - val_loss: 1.0288
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1300
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0797 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0761
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0740
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5123 - loss: 1.0739 - val_accuracy: 0.5010 - val_loss: 1.0335
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1313
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0968 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0812
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0762
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5130 - loss: 1.0732
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5132 - loss: 1.0730 - val_accuracy: 0.5220 - val_loss: 0.9837
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5469 - loss: 1.1181
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0658 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0683
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0679
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.0676
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5186 - loss: 1.0675 - val_accuracy: 0.5299 - val_loss: 0.9788
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0671
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.0425 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0443
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0472
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5138 - loss: 1.0495
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5139 - loss: 1.0497 - val_accuracy: 0.5131 - val_loss: 1.0196
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.0056
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0361 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0453
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0491
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5234 - loss: 1.0510 - val_accuracy: 0.5348 - val_loss: 0.9645
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1711
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0336 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0361
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5352 - loss: 1.0380
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0391
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5342 - loss: 1.0393 - val_accuracy: 0.5516 - val_loss: 0.9518
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6094 - loss: 0.8787
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5234 - loss: 1.0387 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0426
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0469
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0469 - val_accuracy: 0.4819 - val_loss: 1.0611
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0454
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.0831 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0692
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5202 - loss: 1.0647
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0608
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5223 - loss: 1.0603 - val_accuracy: 0.5358 - val_loss: 0.9632
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9666
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0324 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5285 - loss: 1.0383
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0408
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5305 - loss: 1.0408 - val_accuracy: 0.5440 - val_loss: 0.9598
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5000 - loss: 1.0857
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0477 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0520
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5289 - loss: 1.0490
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5306 - loss: 1.0469 - val_accuracy: 0.5338 - val_loss: 0.9734
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.6719 - loss: 0.9684
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5533 - loss: 1.0074 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5495 - loss: 1.0128
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5487 - loss: 1.0151
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5470 - loss: 1.0180 - val_accuracy: 0.5237 - val_loss: 0.9615

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 487ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 9: 54.95 [%]
F1-score capturado en la ejecución 9: 54.06 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
['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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:47[0m 685ms/step
[1m 65/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 791us/step  
[1m127/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 800us/step
[1m198/333[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 771us/step
[1m271/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 749us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m73/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 699us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 53.48 [%]
Global F1 score (validation) = 54.51 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4692277  0.47884583 0.0053886  0.04653786]
 [0.4319027  0.36867073 0.06353721 0.13588928]
 [0.4258481  0.37983614 0.06022555 0.1340901 ]
 ...
 [0.02355705 0.01374649 0.94723487 0.0154615 ]
 [0.02388161 0.01394693 0.94652027 0.01565115]
 [0.02358967 0.0137823  0.9471519  0.01547616]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.21 [%]
Global accuracy score (test) = 53.27 [%]
Global F1 score (train) = 60.71 [%]
Global F1 score (test) = 52.45 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.48      0.44       400
MODERATE-INTENSITY       0.44      0.39      0.41       400
         SEDENTARY       0.59      0.86      0.70       400
VIGOROUS-INTENSITY       0.95      0.38      0.54       345

          accuracy                           0.53      1545
         macro avg       0.60      0.53      0.52      1545
      weighted avg       0.58      0.53      0.52      1545

2025-11-05 11:22:32.089746: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:22:32.101089: 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 00:00:1762338152.114656 3255782 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338152.118864 3255782 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338152.129283 3255782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338152.129304 3255782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338152.129306 3255782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338152.129307 3255782 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:22:32.132576: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338154.361146 3255782 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338155.746381 3255899 service.cc:152] XLA service 0x7c7aa400a550 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338155.746424 3255899 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:22:35.784671: 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 00:00:1762338155.910857 3255899 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338158.031820 3255899 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:24[0m 3s/step - accuracy: 0.1875 - loss: 2.6925
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2440 - loss: 2.5094 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2615 - loss: 2.4034
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2717 - loss: 2.3222
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2788 - loss: 2.2474
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2789 - loss: 2.2459
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2791 - loss: 2.2444 - val_accuracy: 0.3827 - val_loss: 1.2673
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 1.5149
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3213 - loss: 1.6147 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3167 - loss: 1.5985
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3153 - loss: 1.5790
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3157 - loss: 1.5636 - val_accuracy: 0.3906 - val_loss: 1.2602
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.4345
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3303 - loss: 1.3977 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.3906
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3324 - loss: 1.3875
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.3842 - val_accuracy: 0.4018 - val_loss: 1.2656
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2872
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3366 - loss: 1.3364 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3408 - loss: 1.3373
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3441 - loss: 1.3348
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3461 - loss: 1.3329 - val_accuracy: 0.3972 - val_loss: 1.2504
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 1.3416
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3668 - loss: 1.3273 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3674 - loss: 1.3160
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3677 - loss: 1.3122
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3679 - loss: 1.3101 - val_accuracy: 0.3870 - val_loss: 1.2360
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2772
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4014 - loss: 1.2676 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3959 - loss: 1.2737
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3925 - loss: 1.2763
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3909 - loss: 1.2773
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3908 - loss: 1.2774 - val_accuracy: 0.3919 - val_loss: 1.2514
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.3897
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3888 - loss: 1.2978 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3856 - loss: 1.2882
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3853 - loss: 1.2843
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3858 - loss: 1.2819
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3858 - loss: 1.2817 - val_accuracy: 0.4054 - val_loss: 1.2045
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1773
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3882 - loss: 1.2544 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3911 - loss: 1.2555
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3915 - loss: 1.2570
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3900 - loss: 1.2578
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3899 - loss: 1.2579 - val_accuracy: 0.4093 - val_loss: 1.2091
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.3410
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3882 - loss: 1.2451 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3887 - loss: 1.2492
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3891 - loss: 1.2488
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3901 - loss: 1.2483
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3902 - loss: 1.2483 - val_accuracy: 0.4346 - val_loss: 1.1843
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.2462
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4172 - loss: 1.2500 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4145 - loss: 1.2406
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4121 - loss: 1.2378
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4105 - loss: 1.2362 - val_accuracy: 0.4097 - val_loss: 1.2049
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1734
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4158 - loss: 1.2270 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4173 - loss: 1.2259
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4160 - loss: 1.2255
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4159 - loss: 1.2243 - val_accuracy: 0.4363 - val_loss: 1.1709
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2391
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3998 - loss: 1.2409 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4064 - loss: 1.2307
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4082 - loss: 1.2266
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4101 - loss: 1.2239 - val_accuracy: 0.4432 - val_loss: 1.1703
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.2451
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4206 - loss: 1.2210 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.2206
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.2188
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.2168
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4168 - loss: 1.2166 - val_accuracy: 0.4507 - val_loss: 1.1298
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0028
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4362 - loss: 1.1993 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.2051
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4304 - loss: 1.2056
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4293 - loss: 1.2045 - val_accuracy: 0.4639 - val_loss: 1.1481
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2688
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4216 - loss: 1.2096 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4265 - loss: 1.2022
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4300 - loss: 1.1971
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.1952
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4312 - loss: 1.1952 - val_accuracy: 0.4451 - val_loss: 1.1578
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2495
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.1699 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4578 - loss: 1.1704
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1741
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4509 - loss: 1.1767 - val_accuracy: 0.4593 - val_loss: 1.1640
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2672
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.2019 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.1927
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4374 - loss: 1.1885
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4375 - loss: 1.1868
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4377 - loss: 1.1863 - val_accuracy: 0.4901 - val_loss: 1.1039
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3281 - loss: 1.2495
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4299 - loss: 1.1961 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4385 - loss: 1.1869
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4422 - loss: 1.1833
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.1818
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4439 - loss: 1.1816 - val_accuracy: 0.4717 - val_loss: 1.1127
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0845
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4321 - loss: 1.1868 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4369 - loss: 1.1832
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4409 - loss: 1.1779
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4433 - loss: 1.1753
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4437 - loss: 1.1750 - val_accuracy: 0.5059 - val_loss: 1.0799
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1839
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4701 - loss: 1.1515 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1591
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1612
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1622
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4684 - loss: 1.1622 - val_accuracy: 0.5033 - val_loss: 1.0827
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2454
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1727 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1623
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4621 - loss: 1.1582
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1574
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4628 - loss: 1.1574 - val_accuracy: 0.4846 - val_loss: 1.0847
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2813
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1595 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4708 - loss: 1.1589
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1561
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4691 - loss: 1.1548 - val_accuracy: 0.5105 - val_loss: 1.0587
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2397
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4542 - loss: 1.1559 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1539
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1527
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1516
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4659 - loss: 1.1516 - val_accuracy: 0.5158 - val_loss: 1.0732
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1969
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1483 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1504
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1504
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4717 - loss: 1.1499 - val_accuracy: 0.5131 - val_loss: 1.0504
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0130
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4739 - loss: 1.1371 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1378
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1402
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1403
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4785 - loss: 1.1402 - val_accuracy: 0.5020 - val_loss: 1.0484
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0679
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4794 - loss: 1.1152 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1177
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.1195
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4856 - loss: 1.1214 - val_accuracy: 0.5243 - val_loss: 1.0397
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.2285
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4492 - loss: 1.1647 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1473
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1393
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1350
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4708 - loss: 1.1349 - val_accuracy: 0.5306 - val_loss: 1.0417
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0181
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5137 - loss: 1.0998 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5036 - loss: 1.1060
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1085
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.1106
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4965 - loss: 1.1107 - val_accuracy: 0.5263 - val_loss: 1.0160
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.9821
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.1085 
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.1163
[1m136/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.1181
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4934 - loss: 1.1187 - val_accuracy: 0.4852 - val_loss: 1.1093
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0903
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0971 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1037
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1058
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4976 - loss: 1.1077
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4976 - loss: 1.1078 - val_accuracy: 0.5122 - val_loss: 1.0444
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4219 - loss: 1.0974
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.0903 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4959 - loss: 1.0953
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.0981
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4942 - loss: 1.1009 - val_accuracy: 0.5296 - val_loss: 1.0322
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1188
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.0926 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4947 - loss: 1.0939
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4942 - loss: 1.0949
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.0970
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4930 - loss: 1.0973 - val_accuracy: 0.5319 - val_loss: 1.0479
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1385
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5033 - loss: 1.1055 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1054
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1050
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1035
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5018 - loss: 1.1034 - val_accuracy: 0.5053 - val_loss: 1.0473

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 483ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 10: 53.27 [%]
F1-score capturado en la ejecución 10: 52.45 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:45[0m 679ms/step
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 714us/step  
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 707us/step
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 700us/step
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 693us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m72/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 713us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 51.22 [%]
Global F1 score (validation) = 51.84 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.34184745 0.30813956 0.12259328 0.22741972]
 [0.35651475 0.3285439  0.09453916 0.22040214]
 [0.3259953  0.2829467  0.16593635 0.22512166]
 ...
 [0.1917549  0.14491433 0.573256   0.09007478]
 [0.10062938 0.06730393 0.79107445 0.04099227]
 [0.06141154 0.03860077 0.8739516  0.02603604]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 55.76 [%]
Global accuracy score (test) = 52.56 [%]
Global F1 score (train) = 56.09 [%]
Global F1 score (test) = 52.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.34      0.32      0.33       400
MODERATE-INTENSITY       0.45      0.58      0.51       400
         SEDENTARY       0.63      0.78      0.70       400
VIGOROUS-INTENSITY       0.90      0.40      0.56       345

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

2025-11-05 11:23:02.288101: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:23:02.299540: 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 00:00:1762338182.313166 3259745 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338182.317383 3259745 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338182.327162 3259745 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338182.327181 3259745 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338182.327183 3259745 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338182.327185 3259745 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:23:02.330441: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338184.585746 3259745 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338185.953377 3259862 service.cc:152] XLA service 0x72e440009c80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338185.953411 3259862 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:23:05.989168: 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 00:00:1762338186.109960 3259862 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338188.243942 3259862 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:25[0m 3s/step - accuracy: 0.2812 - loss: 2.2154
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 2.3349 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2879 - loss: 2.2508
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2869 - loss: 2.1963
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2871 - loss: 2.1517
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2872 - loss: 2.1418
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2872 - loss: 2.1408 - val_accuracy: 0.4113 - val_loss: 1.2755
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.3594 - loss: 1.5921
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.5776 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3172 - loss: 1.5658
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3161 - loss: 1.5522
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3160 - loss: 1.5387
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3160 - loss: 1.5380 - val_accuracy: 0.3959 - val_loss: 1.2700
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.3042
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3319 - loss: 1.3838 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3325 - loss: 1.3826
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3332 - loss: 1.3792
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3339 - loss: 1.3766
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3340 - loss: 1.3762 - val_accuracy: 0.3932 - val_loss: 1.2676
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.3120
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3687 - loss: 1.3158 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3656 - loss: 1.3192
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3644 - loss: 1.3204
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3631 - loss: 1.3211 - val_accuracy: 0.4100 - val_loss: 1.2539
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2673
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3632 - loss: 1.3050 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3655 - loss: 1.3053
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.3033
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.3015
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3701 - loss: 1.3013 - val_accuracy: 0.4031 - val_loss: 1.2336
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2656 - loss: 1.3971
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3542 - loss: 1.2894 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.2868
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3643 - loss: 1.2859
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3665 - loss: 1.2854
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3665 - loss: 1.2854 - val_accuracy: 0.4093 - val_loss: 1.2367
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3145
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3763 - loss: 1.2781 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3795 - loss: 1.2734
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3801 - loss: 1.2715
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3812 - loss: 1.2695
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3813 - loss: 1.2694 - val_accuracy: 0.4057 - val_loss: 1.2105
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3906 - loss: 1.2273
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3785 - loss: 1.2610 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3848 - loss: 1.2583
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3874 - loss: 1.2572
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3879 - loss: 1.2565 - val_accuracy: 0.4231 - val_loss: 1.1938
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.2291
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4081 - loss: 1.2583 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4034 - loss: 1.2573
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4013 - loss: 1.2551
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4015 - loss: 1.2523 - val_accuracy: 0.4156 - val_loss: 1.1769
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1797
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3959 - loss: 1.2300 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3964 - loss: 1.2376
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3966 - loss: 1.2390
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3977 - loss: 1.2386
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3978 - loss: 1.2386 - val_accuracy: 0.4402 - val_loss: 1.1699
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.2946
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3974 - loss: 1.2401 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4042 - loss: 1.2331
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4064 - loss: 1.2304
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4071 - loss: 1.2291 - val_accuracy: 0.4553 - val_loss: 1.1618
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3129
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4176 - loss: 1.2213 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4121 - loss: 1.2234
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4109 - loss: 1.2231
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4111 - loss: 1.2220
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4112 - loss: 1.2219 - val_accuracy: 0.4294 - val_loss: 1.1647
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.3277
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4220 - loss: 1.2147 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.2133
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4155 - loss: 1.2132
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4156 - loss: 1.2131
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4157 - loss: 1.2131 - val_accuracy: 0.4553 - val_loss: 1.1533
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.2080
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4287 - loss: 1.2255 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4248 - loss: 1.2223
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2187
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4226 - loss: 1.2172 - val_accuracy: 0.4593 - val_loss: 1.1448
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3438 - loss: 1.1657
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4069 - loss: 1.2028 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4149 - loss: 1.2018
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4186 - loss: 1.2016
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4197 - loss: 1.2023
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4197 - loss: 1.2024 - val_accuracy: 0.4435 - val_loss: 1.1471
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1918
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4288 - loss: 1.2072 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4300 - loss: 1.2017
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.2013
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4285 - loss: 1.2014
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4285 - loss: 1.2014 - val_accuracy: 0.4514 - val_loss: 1.1374
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4844 - loss: 1.1015
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4492 - loss: 1.1697 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.1763
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4405 - loss: 1.1818
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.1847
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4387 - loss: 1.1850 - val_accuracy: 0.4563 - val_loss: 1.1276
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.2918
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2343 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4297 - loss: 1.2179
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4322 - loss: 1.2092
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4332 - loss: 1.2045
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4334 - loss: 1.2039 - val_accuracy: 0.4878 - val_loss: 1.1296
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1333
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4558 - loss: 1.1854 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.1871
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4487 - loss: 1.1852
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4477 - loss: 1.1840
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4476 - loss: 1.1839 - val_accuracy: 0.4934 - val_loss: 1.1132
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2369
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4541 - loss: 1.1746 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4541 - loss: 1.1752
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1763
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1775
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4539 - loss: 1.1776 - val_accuracy: 0.4727 - val_loss: 1.1178
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2452
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1661 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.1663
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4451 - loss: 1.1669
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4443 - loss: 1.1683
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4442 - loss: 1.1685 - val_accuracy: 0.4783 - val_loss: 1.1084
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2885
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4364 - loss: 1.1951 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.1834
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.1805
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4508 - loss: 1.1784 - val_accuracy: 0.5135 - val_loss: 1.0785
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0558
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4535 - loss: 1.1657 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.1665
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1668
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1663
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4595 - loss: 1.1662 - val_accuracy: 0.5168 - val_loss: 1.0915
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.3337
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4532 - loss: 1.2043 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1841
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1759
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4633 - loss: 1.1716 - val_accuracy: 0.4869 - val_loss: 1.0992
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1411
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1461 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1506
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1522
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4701 - loss: 1.1535 - val_accuracy: 0.5181 - val_loss: 1.0728
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4062 - loss: 1.2418
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4593 - loss: 1.1632 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1555
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1531
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4670 - loss: 1.1531
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4671 - loss: 1.1529 - val_accuracy: 0.5108 - val_loss: 1.0634
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.2463
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1446 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1475
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4683 - loss: 1.1502
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4689 - loss: 1.1512 - val_accuracy: 0.4905 - val_loss: 1.0823
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 1.1494
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1339 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1376
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1369
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4745 - loss: 1.1374 - val_accuracy: 0.5164 - val_loss: 1.0555
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.1798
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1501 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1482
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1475
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4747 - loss: 1.1473 - val_accuracy: 0.5430 - val_loss: 1.0278
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1201
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4700 - loss: 1.1582 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1462
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1431
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1414
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4801 - loss: 1.1410 - val_accuracy: 0.5430 - val_loss: 1.0221
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0983
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.1134 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1206
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.1244
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1251 - val_accuracy: 0.5286 - val_loss: 1.0406
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2553
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1416 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1378
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1362
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1349
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4862 - loss: 1.1343 - val_accuracy: 0.5279 - val_loss: 1.0344
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1408
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1385 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.1324
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1302
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1281
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4957 - loss: 1.1279 - val_accuracy: 0.5306 - val_loss: 1.0436
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1588
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4816 - loss: 1.1053 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1075
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1092
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4896 - loss: 1.1107 - val_accuracy: 0.5453 - val_loss: 1.0140
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1425
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4875 - loss: 1.1157 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1161
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1169
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4900 - loss: 1.1160 - val_accuracy: 0.5532 - val_loss: 0.9908
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.9785
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.1139 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.1112
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.1111
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5038 - loss: 1.1101 - val_accuracy: 0.4816 - val_loss: 1.0755
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1863
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.1207 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.1145
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.1121
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5028 - loss: 1.1105 - val_accuracy: 0.5384 - val_loss: 0.9982
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.9445
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.0866 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.0859
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5039 - loss: 1.0883
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5053 - loss: 1.0901 - val_accuracy: 0.5237 - val_loss: 1.0292
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.0716
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.0794 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5054 - loss: 1.0796
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0841
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5072 - loss: 1.0870
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5072 - loss: 1.0872 - val_accuracy: 0.5404 - val_loss: 1.0024
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9998
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.0947 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0946
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5076 - loss: 1.0904
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0894
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5092 - loss: 1.0893 - val_accuracy: 0.5591 - val_loss: 0.9761
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1718
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1117 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5007 - loss: 1.0990
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.0951
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5061 - loss: 1.0929 - val_accuracy: 0.5470 - val_loss: 0.9882
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6250 - loss: 0.9416
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0734 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0776
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0807
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5133 - loss: 1.0825
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5134 - loss: 1.0825 - val_accuracy: 0.5690 - val_loss: 0.9593
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9777
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5320 - loss: 1.0727 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0791
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0814
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5190 - loss: 1.0826 - val_accuracy: 0.5624 - val_loss: 0.9555
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.1635
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0621 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0580
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5288 - loss: 1.0611
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0637
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5274 - loss: 1.0639 - val_accuracy: 0.5604 - val_loss: 0.9779
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5625 - loss: 1.0188
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5372 - loss: 1.0469 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0528
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5334 - loss: 1.0570
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0592
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5319 - loss: 1.0596 - val_accuracy: 0.5512 - val_loss: 0.9586
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2143
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5103 - loss: 1.0895 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5161 - loss: 1.0792
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0759
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5192 - loss: 1.0742 - val_accuracy: 0.5664 - val_loss: 0.9627
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0359
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 1.0634 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0661
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0640
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5360 - loss: 1.0634 - val_accuracy: 0.5903 - val_loss: 0.9191
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 1.0048
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0569 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0577
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0568
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0563 - val_accuracy: 0.5664 - val_loss: 0.9463
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.5781 - loss: 0.9371
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5527 - loss: 1.0281 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0359
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0409
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5425 - loss: 1.0442 - val_accuracy: 0.5805 - val_loss: 0.9536
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0831
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0334 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0398
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 1.0421
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0431
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5404 - loss: 1.0433 - val_accuracy: 0.5700 - val_loss: 0.9455
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9471
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0285 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0357
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0391
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0407
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5399 - loss: 1.0408 - val_accuracy: 0.5713 - val_loss: 0.9473
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0457
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0595 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0506
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0470
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0452
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5456 - loss: 1.0452 - val_accuracy: 0.5470 - val_loss: 0.9486

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 511ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 11: 52.56 [%]
F1-score capturado en la ejecución 11: 52.22 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:45[0m 679ms/step
[1m 66/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 777us/step  
[1m141/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 721us/step
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 705us/step
[1m294/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 689us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m73/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 696us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 54.76 [%]
Global F1 score (validation) = 53.71 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44939047 0.44847566 0.02696743 0.07516655]
 [0.38965303 0.38588306 0.03471809 0.18974581]
 [0.4078983  0.38090873 0.06343527 0.14775775]
 ...
 [0.06953335 0.04435467 0.8364902  0.04962185]
 [0.04952905 0.03048035 0.88824433 0.03174622]
 [0.04564282 0.02791467 0.8972332  0.02920932]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 60.02 [%]
Global accuracy score (test) = 55.53 [%]
Global F1 score (train) = 59.74 [%]
Global F1 score (test) = 54.51 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.47      0.40      0.43       400
MODERATE-INTENSITY       0.46      0.49      0.48       400
         SEDENTARY       0.58      0.89      0.70       400
VIGOROUS-INTENSITY       0.86      0.43      0.57       345

          accuracy                           0.56      1545
         macro avg       0.59      0.55      0.55      1545
      weighted avg       0.58      0.56      0.54      1545

2025-11-05 11:23:38.785915: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:23:38.797302: 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 00:00:1762338218.810437 3265453 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338218.814351 3265453 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338218.824276 3265453 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338218.824293 3265453 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338218.824295 3265453 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338218.824296 3265453 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:23:38.827525: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338221.065765 3265453 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338222.471129 3265559 service.cc:152] XLA service 0x7c2d9400b0b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338222.471186 3265559 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:23:42.519666: 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 00:00:1762338222.639872 3265559 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338224.741356 3265559 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:24[0m 3s/step - accuracy: 0.2344 - loss: 2.6088
[1m 31/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2728 - loss: 2.3058 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2802 - loss: 2.1808
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2846 - loss: 2.0985
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2864 - loss: 2.0305
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2867 - loss: 2.0170
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2867 - loss: 2.0157 - val_accuracy: 0.3633 - val_loss: 1.2676
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2500 - loss: 1.5195
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3146 - loss: 1.4848 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3177 - loss: 1.4734
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3194 - loss: 1.4643
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3196 - loss: 1.4579 - val_accuracy: 0.3725 - val_loss: 1.2762
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3535
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3399 - loss: 1.3808 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.3753
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3387 - loss: 1.3715
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3387 - loss: 1.3679 - val_accuracy: 0.3952 - val_loss: 1.2745
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.3335
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3356 - loss: 1.3291 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3374 - loss: 1.3308
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3389 - loss: 1.3323
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3414 - loss: 1.3318 - val_accuracy: 0.4064 - val_loss: 1.2723
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2031 - loss: 1.4344
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.3373 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3426 - loss: 1.3274
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3458 - loss: 1.3232
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.3205
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3476 - loss: 1.3204 - val_accuracy: 0.3998 - val_loss: 1.2585
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2828
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3922 - loss: 1.2857 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3840 - loss: 1.2913
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3809 - loss: 1.2936
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3777 - loss: 1.2949
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3776 - loss: 1.2949 - val_accuracy: 0.3932 - val_loss: 1.2469
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3125 - loss: 1.3214
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3682 - loss: 1.2990 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3705 - loss: 1.2930
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.2910
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3699 - loss: 1.2897 - val_accuracy: 0.3975 - val_loss: 1.2405
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.2922
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3742 - loss: 1.2737 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3771 - loss: 1.2719
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3773 - loss: 1.2703
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3775 - loss: 1.2691
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3775 - loss: 1.2690 - val_accuracy: 0.3886 - val_loss: 1.2130
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.3289
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3890 - loss: 1.2588 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3955 - loss: 1.2519
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3955 - loss: 1.2490
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3949 - loss: 1.2475 - val_accuracy: 0.4537 - val_loss: 1.1912
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2246
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3899 - loss: 1.2402 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3948 - loss: 1.2404
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3957 - loss: 1.2401
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3973 - loss: 1.2387 - val_accuracy: 0.4106 - val_loss: 1.1715
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2989
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4075 - loss: 1.2278 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4100 - loss: 1.2276
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.2263
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4110 - loss: 1.2247 - val_accuracy: 0.4514 - val_loss: 1.1507
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.1967
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3976 - loss: 1.2265 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4011 - loss: 1.2238
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4062 - loss: 1.2191
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4096 - loss: 1.2166
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4100 - loss: 1.2163 - val_accuracy: 0.4074 - val_loss: 1.1540
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.2573
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.1940 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4344 - loss: 1.1926
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.1925
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.1930
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4288 - loss: 1.1930 - val_accuracy: 0.4777 - val_loss: 1.1305
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1413
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1897 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4455 - loss: 1.1889
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4407 - loss: 1.1903
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4386 - loss: 1.1891 - val_accuracy: 0.4685 - val_loss: 1.1292
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.0892
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4320 - loss: 1.1652 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.1751
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4301 - loss: 1.1786
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4305 - loss: 1.1791 - val_accuracy: 0.4944 - val_loss: 1.1020
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.2260
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.2102 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.2017
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4376 - loss: 1.1978
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4384 - loss: 1.1943 - val_accuracy: 0.4869 - val_loss: 1.1055
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1823
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4412 - loss: 1.1687 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.1699
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4409 - loss: 1.1699
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4406 - loss: 1.1706
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4406 - loss: 1.1706 - val_accuracy: 0.5066 - val_loss: 1.1027
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2035
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.1518 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4446 - loss: 1.1597
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.1620
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4444 - loss: 1.1631
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4444 - loss: 1.1632 - val_accuracy: 0.4662 - val_loss: 1.1226
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1503
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4541 - loss: 1.1802 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4480 - loss: 1.1772
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.1755
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.1732
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4464 - loss: 1.1731 - val_accuracy: 0.4829 - val_loss: 1.1061
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1901
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.1778 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.1740
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4518 - loss: 1.1721
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4526 - loss: 1.1696 - val_accuracy: 0.4931 - val_loss: 1.0984
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1325
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1439 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1383
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1393
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4676 - loss: 1.1398
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4676 - loss: 1.1399 - val_accuracy: 0.4862 - val_loss: 1.1136
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0854
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1613 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1595
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4598 - loss: 1.1564
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1537
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4601 - loss: 1.1535 - val_accuracy: 0.5085 - val_loss: 1.0816
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0531
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1379 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1425
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4742 - loss: 1.1448
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4718 - loss: 1.1465 - val_accuracy: 0.5072 - val_loss: 1.0639
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1996
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4692 - loss: 1.1360 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1348
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1353
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4723 - loss: 1.1359 - val_accuracy: 0.5033 - val_loss: 1.0704
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1073
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1272 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1290
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1281
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4873 - loss: 1.1290
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4873 - loss: 1.1290 - val_accuracy: 0.5181 - val_loss: 1.0488
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.1206
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4562 - loss: 1.1376 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1305
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1279
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4732 - loss: 1.1259
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4734 - loss: 1.1258 - val_accuracy: 0.5200 - val_loss: 1.0454
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.1046
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1088 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1172
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1199
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4793 - loss: 1.1201 - val_accuracy: 0.5342 - val_loss: 1.0416
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2273
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1330 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4845 - loss: 1.1280
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1248
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1235
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4858 - loss: 1.1234 - val_accuracy: 0.5066 - val_loss: 1.0692
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1294
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1036 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1056
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1070
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1082 - val_accuracy: 0.5509 - val_loss: 1.0090
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0942
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1038 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1056
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1058
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1057
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4954 - loss: 1.1057 - val_accuracy: 0.5246 - val_loss: 1.0386
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1913
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1291 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4811 - loss: 1.1242
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4844 - loss: 1.1200
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4867 - loss: 1.1168 - val_accuracy: 0.5283 - val_loss: 1.0231
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5000 - loss: 1.1404
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1251 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1176
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1124
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4917 - loss: 1.1087
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4920 - loss: 1.1083 - val_accuracy: 0.5151 - val_loss: 1.0540
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2335
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4849 - loss: 1.1103 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1000
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.0937
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5008 - loss: 1.0921 - val_accuracy: 0.5089 - val_loss: 1.0416
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9885
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.1038 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.1003
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.0978
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.0947
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5063 - loss: 1.0945 - val_accuracy: 0.5260 - val_loss: 1.0071
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1625
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0850 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0938
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0927
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0908
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5211 - loss: 1.0908 - val_accuracy: 0.5329 - val_loss: 1.0162
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0916
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0712 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0732
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0767
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5097 - loss: 1.0786 - val_accuracy: 0.5463 - val_loss: 0.9921
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9456
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0311 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0407
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0484
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0532
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5280 - loss: 1.0534 - val_accuracy: 0.5325 - val_loss: 1.0391
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2007
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0908 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0844
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0833
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0825
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5229 - loss: 1.0824 - val_accuracy: 0.5493 - val_loss: 0.9762
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.0963
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.0858 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0703
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0659
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5194 - loss: 1.0643 - val_accuracy: 0.5427 - val_loss: 0.9996
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9636
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5157 - loss: 1.0581 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5200 - loss: 1.0571
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0576
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0569
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5223 - loss: 1.0568 - val_accuracy: 0.5332 - val_loss: 1.0305
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0893
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0438 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0499
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5266 - loss: 1.0537
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5267 - loss: 1.0552 - val_accuracy: 0.5483 - val_loss: 0.9801
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1420
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0792 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0705
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0668
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5240 - loss: 1.0644 - val_accuracy: 0.5624 - val_loss: 0.9561
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0546
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0680 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0621
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0597
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5405 - loss: 1.0588 - val_accuracy: 0.5604 - val_loss: 0.9472
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.9587
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5179 - loss: 1.0520 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0569
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0575
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0565
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5241 - loss: 1.0565 - val_accuracy: 0.5631 - val_loss: 0.9422
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1301
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0580 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0458
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5459 - loss: 1.0415
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0421
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5433 - loss: 1.0424 - val_accuracy: 0.5453 - val_loss: 0.9821
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9997
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 1.0297 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5506 - loss: 1.0333
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0367
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5489 - loss: 1.0387 - val_accuracy: 0.5660 - val_loss: 0.9291
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1862
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5472 - loss: 1.0422 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0419
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0432
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0430
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5426 - loss: 1.0429 - val_accuracy: 0.5026 - val_loss: 1.0251
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.1796
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0573 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0446
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0409
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5385 - loss: 1.0384 - val_accuracy: 0.5591 - val_loss: 0.9422
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5625 - loss: 1.0501
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0481 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5401 - loss: 1.0447
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0417
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0396
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5429 - loss: 1.0394 - val_accuracy: 0.5641 - val_loss: 0.9258
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0661
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 0.9976 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0039
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0093
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5412 - loss: 1.0134 - val_accuracy: 0.5352 - val_loss: 0.9880
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0768
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 1.0109 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5489 - loss: 1.0170
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5487 - loss: 1.0188
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5487 - loss: 1.0195 - val_accuracy: 0.5736 - val_loss: 0.9351
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1548
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0314 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 1.0264
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5443 - loss: 1.0249
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5457 - loss: 1.0238 - val_accuracy: 0.5769 - val_loss: 0.9167
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0848
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0303 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5303 - loss: 1.0253
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0226
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5386 - loss: 1.0203 - val_accuracy: 0.5792 - val_loss: 0.9019
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0500
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5635 - loss: 1.0162 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5597 - loss: 1.0118
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5569 - loss: 1.0122
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5559 - loss: 1.0122
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5559 - loss: 1.0120 - val_accuracy: 0.5805 - val_loss: 0.9140
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0841
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0398 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0278
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0212
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5441 - loss: 1.0181 - val_accuracy: 0.5493 - val_loss: 0.9498
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0177
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5556 - loss: 1.0158 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5590 - loss: 1.0084
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5604 - loss: 1.0044
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5606 - loss: 1.0044
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5606 - loss: 1.0044 - val_accuracy: 0.5739 - val_loss: 0.9103
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5156 - loss: 1.0398
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5584 - loss: 0.9990 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5581 - loss: 0.9984
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5578 - loss: 0.9999
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5573 - loss: 1.0010 - val_accuracy: 0.5815 - val_loss: 0.9174
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0730
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5468 - loss: 1.0055 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5521 - loss: 1.0022
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5549 - loss: 1.0021
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5562 - loss: 1.0016 - val_accuracy: 0.5982 - val_loss: 0.8815
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.7895
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5746 - loss: 0.9634 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5630 - loss: 0.9847
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5595 - loss: 0.9881
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5579 - loss: 0.9904 - val_accuracy: 0.5700 - val_loss: 0.9246
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.3384
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0492 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5521 - loss: 1.0334
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5554 - loss: 1.0239
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5573 - loss: 1.0191 - val_accuracy: 0.5828 - val_loss: 0.8945
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.4219 - loss: 1.1714
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5401 - loss: 1.0258 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0112
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0061
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5479 - loss: 1.0019 - val_accuracy: 0.5575 - val_loss: 0.9733
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0448
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5526 - loss: 1.0096 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5557 - loss: 0.9993
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5572 - loss: 0.9952
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5575 - loss: 0.9947
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5575 - loss: 0.9946 - val_accuracy: 0.5759 - val_loss: 0.9072
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.5625 - loss: 0.9952
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5605 - loss: 1.0282 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5604 - loss: 1.0164
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5592 - loss: 1.0124
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5591 - loss: 1.0099 - val_accuracy: 0.5752 - val_loss: 0.9173

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 479ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 12: 55.53 [%]
F1-score capturado en la ejecución 12: 54.51 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:51[0m 696ms/step
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 797us/step  
[1m135/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 748us/step
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 716us/step
[1m279/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 722us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m66/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 775us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 56.73 [%]
Global F1 score (validation) = 56.57 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.33897215 0.27060586 0.18643402 0.203988  ]
 [0.321574   0.2924871  0.05368731 0.33225158]
 [0.3300871  0.26096758 0.20985965 0.19908568]
 ...
 [0.01907069 0.01041826 0.9589843  0.01152664]
 [0.02553857 0.01426469 0.9443837  0.01581303]
 [0.02854637 0.0159646  0.9383146  0.01717442]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 62.87 [%]
Global accuracy score (test) = 54.56 [%]
Global F1 score (train) = 62.81 [%]
Global F1 score (test) = 54.22 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.34      0.36       400
MODERATE-INTENSITY       0.44      0.46      0.45       400
         SEDENTARY       0.60      0.87      0.71       400
VIGOROUS-INTENSITY       0.88      0.51      0.64       345

          accuracy                           0.55      1545
         macro avg       0.58      0.54      0.54      1545
      weighted avg       0.57      0.55      0.54      1545

2025-11-05 11:24:18.777674: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:24:18.789235: 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 00:00:1762338258.802343 3272172 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338258.806452 3272172 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338258.816186 3272172 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338258.816212 3272172 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338258.816214 3272172 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338258.816216 3272172 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:24:18.819358: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338261.046367 3272172 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338262.419883 3272290 service.cc:152] XLA service 0x76621400a5d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338262.419937 3272290 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:24:22.466754: 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 00:00:1762338262.585538 3272290 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338264.689842 3272290 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:20[0m 3s/step - accuracy: 0.2812 - loss: 2.9292
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2565 - loss: 2.5063 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2623 - loss: 2.3883
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2658 - loss: 2.3049
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2688 - loss: 2.2295
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2692 - loss: 2.2201
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2692 - loss: 2.2186 - val_accuracy: 0.3922 - val_loss: 1.2761
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2656 - loss: 1.5255
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3073 - loss: 1.5708 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3100 - loss: 1.5551
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3116 - loss: 1.5377
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3136 - loss: 1.5233 - val_accuracy: 0.3936 - val_loss: 1.2718
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.5185
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3166 - loss: 1.4195 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3224 - loss: 1.4051
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3253 - loss: 1.3954
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3278 - loss: 1.3877 - val_accuracy: 0.3867 - val_loss: 1.2646
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2535
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3602 - loss: 1.3315 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3539 - loss: 1.3329
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3533 - loss: 1.3313
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3531 - loss: 1.3298
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3531 - loss: 1.3297 - val_accuracy: 0.3962 - val_loss: 1.2536
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2401
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3673 - loss: 1.2967 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3677 - loss: 1.3010
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3683 - loss: 1.3016
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3690 - loss: 1.3011 - val_accuracy: 0.3906 - val_loss: 1.2473
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2740
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.2825 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3846 - loss: 1.2782
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3840 - loss: 1.2782
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3836 - loss: 1.2789
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3835 - loss: 1.2789 - val_accuracy: 0.4143 - val_loss: 1.2359
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.3027
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4052 - loss: 1.2521 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4012 - loss: 1.2594
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3978 - loss: 1.2635
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3951 - loss: 1.2660 - val_accuracy: 0.4014 - val_loss: 1.2240
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2340
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3922 - loss: 1.2549 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.2578
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3888 - loss: 1.2565
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3898 - loss: 1.2549 - val_accuracy: 0.4139 - val_loss: 1.1983
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1644
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4052 - loss: 1.2396 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4022 - loss: 1.2456
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4024 - loss: 1.2481
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4025 - loss: 1.2487
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4026 - loss: 1.2487 - val_accuracy: 0.4330 - val_loss: 1.1911
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.2449
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4129 - loss: 1.2290 
[1m 92/167[0m [32m━━━━━━━━━━━[0m[37m━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4098 - loss: 1.2299
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4092 - loss: 1.2315
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4088 - loss: 1.2316 - val_accuracy: 0.4198 - val_loss: 1.2041
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.3467
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4022 - loss: 1.2587 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4060 - loss: 1.2427
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4071 - loss: 1.2344
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4076 - loss: 1.2320 - val_accuracy: 0.4852 - val_loss: 1.1707
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2704
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4108 - loss: 1.2342 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4145 - loss: 1.2277
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4160 - loss: 1.2236
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4173 - loss: 1.2210 - val_accuracy: 0.4277 - val_loss: 1.1863
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1639
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4104 - loss: 1.2208 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4137 - loss: 1.2229
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4174 - loss: 1.2198
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4188 - loss: 1.2182 - val_accuracy: 0.4445 - val_loss: 1.1468
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2064
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4125 - loss: 1.1952 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4148 - loss: 1.2012
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4140 - loss: 1.2041
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4152 - loss: 1.2038 - val_accuracy: 0.4832 - val_loss: 1.1310
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1410
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2092 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4291 - loss: 1.2084
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4290 - loss: 1.2035
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4295 - loss: 1.2011
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4295 - loss: 1.2010 - val_accuracy: 0.4474 - val_loss: 1.1268
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.4219 - loss: 1.3057
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4135 - loss: 1.2193 
[1m 72/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4204 - loss: 1.2065
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4244 - loss: 1.2011
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4271 - loss: 1.1980
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4274 - loss: 1.1976 - val_accuracy: 0.4593 - val_loss: 1.1214
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2136
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4503 - loss: 1.1735 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.1764
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4451 - loss: 1.1784
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4448 - loss: 1.1789 - val_accuracy: 0.4783 - val_loss: 1.1206
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1918
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4340 - loss: 1.2202 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.2033
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4397 - loss: 1.1964
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4409 - loss: 1.1927 - val_accuracy: 0.4740 - val_loss: 1.1106
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1546
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4384 - loss: 1.1904 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.1866
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4417 - loss: 1.1823
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4420 - loss: 1.1801
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4420 - loss: 1.1801 - val_accuracy: 0.4832 - val_loss: 1.1133
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2978
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4383 - loss: 1.2096 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4422 - loss: 1.1965
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.1923
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4422 - loss: 1.1889
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4423 - loss: 1.1888 - val_accuracy: 0.4497 - val_loss: 1.1339
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0893
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.1762 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4484 - loss: 1.1734
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1717
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.1706
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4508 - loss: 1.1706 - val_accuracy: 0.4997 - val_loss: 1.0794
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.2661
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4295 - loss: 1.1979 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.1900
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.1854
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4401 - loss: 1.1815 - val_accuracy: 0.4882 - val_loss: 1.0863
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3906 - loss: 1.1486
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4450 - loss: 1.1390 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.1433
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.1464
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4497 - loss: 1.1486
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4498 - loss: 1.1487 - val_accuracy: 0.4888 - val_loss: 1.0820
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1092
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4837 - loss: 1.1538 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1544
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1519
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4712 - loss: 1.1514 - val_accuracy: 0.4701 - val_loss: 1.1055
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1404
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1302 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4709 - loss: 1.1407
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1446
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4670 - loss: 1.1463 - val_accuracy: 0.4800 - val_loss: 1.0665
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0657
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1352 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1383
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1402
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4651 - loss: 1.1407 - val_accuracy: 0.4938 - val_loss: 1.0788
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.2283
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1353 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1385
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1387
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4676 - loss: 1.1385 - val_accuracy: 0.5039 - val_loss: 1.0627
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4844 - loss: 1.1547
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1074 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4744 - loss: 1.1120
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1167
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1199
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4736 - loss: 1.1200 - val_accuracy: 0.5043 - val_loss: 1.0625
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1315
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1386 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1378
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1358
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4749 - loss: 1.1339 - val_accuracy: 0.4954 - val_loss: 1.0532
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.0565
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4541 - loss: 1.1164 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1200
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4662 - loss: 1.1209
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1210
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4685 - loss: 1.1210 - val_accuracy: 0.5026 - val_loss: 1.0521
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 1.1523
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1380 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1343
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1306
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1287
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4686 - loss: 1.1287 - val_accuracy: 0.4934 - val_loss: 1.0777
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1462
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1196 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.1210
[1m135/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1200
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4891 - loss: 1.1202 - val_accuracy: 0.4918 - val_loss: 1.0595
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5625 - loss: 1.0364
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.0813 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.0885
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.0929
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4817 - loss: 1.0977
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4816 - loss: 1.0981 - val_accuracy: 0.5138 - val_loss: 1.0338
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0977
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4610 - loss: 1.1267 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1240
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1237
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1219
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4749 - loss: 1.1217 - val_accuracy: 0.5062 - val_loss: 1.0234
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1247
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1495 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4746 - loss: 1.1417
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4771 - loss: 1.1370
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4790 - loss: 1.1323 - val_accuracy: 0.4990 - val_loss: 1.0586
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1691
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1058 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4796 - loss: 1.1075
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1061
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4873 - loss: 1.1044 - val_accuracy: 0.5020 - val_loss: 1.0150
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0527
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1137 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4859 - loss: 1.1085
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4867 - loss: 1.1060
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4871 - loss: 1.1053 - val_accuracy: 0.5243 - val_loss: 1.0185
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1464
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1049 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1001
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.0995
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4917 - loss: 1.0994 - val_accuracy: 0.5125 - val_loss: 1.0428
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.3095
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1268 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1128
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.1067
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.1038
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4945 - loss: 1.1036 - val_accuracy: 0.5266 - val_loss: 1.0180
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0233
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0705 
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0806
[1m135/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.0835
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5013 - loss: 1.0847 - val_accuracy: 0.4731 - val_loss: 1.1578
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2386
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4822 - loss: 1.1212 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1104
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4930 - loss: 1.1062
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4940 - loss: 1.1023 - val_accuracy: 0.5470 - val_loss: 0.9789
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0191
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.0894 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.0875
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.0853
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.0837
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5039 - loss: 1.0833 - val_accuracy: 0.5092 - val_loss: 1.0409
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1119
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1023 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.0910
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5020 - loss: 1.0863
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5045 - loss: 1.0839 - val_accuracy: 0.4754 - val_loss: 1.1460
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5781 - loss: 1.0470
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.0952 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.0863
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.0823
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5087 - loss: 1.0801 - val_accuracy: 0.5220 - val_loss: 0.9831
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0706
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0671 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0673
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0669
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5232 - loss: 1.0658 - val_accuracy: 0.4819 - val_loss: 1.1011
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.3238
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1035 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.0912
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.0851
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5012 - loss: 1.0821 - val_accuracy: 0.5519 - val_loss: 0.9658
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9252
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0423 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0509
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5280 - loss: 1.0541
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5258 - loss: 1.0558 - val_accuracy: 0.5503 - val_loss: 0.9616
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1105
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0603 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0611
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0596
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5240 - loss: 1.0585 - val_accuracy: 0.5348 - val_loss: 0.9689
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0473
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.0820 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0705
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0657
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0627
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5179 - loss: 1.0626 - val_accuracy: 0.5535 - val_loss: 0.9773
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.0710
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0532 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0508
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5190 - loss: 1.0512
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0505 - val_accuracy: 0.5512 - val_loss: 0.9591
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1260
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0424 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0381
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0362
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0358
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5388 - loss: 1.0358 - val_accuracy: 0.5549 - val_loss: 0.9543
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.3656
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5135 - loss: 1.0884 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0638
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0556
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5353 - loss: 1.0526 - val_accuracy: 0.5775 - val_loss: 0.9392
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1638
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.0614 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0537
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0478
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5195 - loss: 1.0436 - val_accuracy: 0.5598 - val_loss: 0.9352
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9281
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 0.9980 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0066
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0134
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0172
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5378 - loss: 1.0174 - val_accuracy: 0.5608 - val_loss: 0.9428
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0897
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0479 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0393
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0356
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5308 - loss: 1.0335
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5308 - loss: 1.0335 - val_accuracy: 0.5690 - val_loss: 0.9231
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9359
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.5701 - loss: 0.9875 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5605 - loss: 1.0071
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5547 - loss: 1.0156
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0194
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5515 - loss: 1.0196 - val_accuracy: 0.5772 - val_loss: 0.9385
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0679
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0394 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0320
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0280
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5416 - loss: 1.0268
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5416 - loss: 1.0268 - val_accuracy: 0.5506 - val_loss: 0.9430
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9704
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 0.9945 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0134
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5374 - loss: 1.0191
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0203
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5390 - loss: 1.0203 - val_accuracy: 0.5618 - val_loss: 0.9205
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 0.9867
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0199 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0203
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0213
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5388 - loss: 1.0216 - val_accuracy: 0.5706 - val_loss: 0.9127
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0271
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 1.0200 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5596 - loss: 1.0122
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5600 - loss: 1.0099
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5590 - loss: 1.0095 - val_accuracy: 0.5516 - val_loss: 0.9321
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0603
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0111 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0047
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0033
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5482 - loss: 1.0032 - val_accuracy: 0.5532 - val_loss: 0.9287
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5312 - loss: 1.0675
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0235 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 1.0178
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0159
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5456 - loss: 1.0147
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5457 - loss: 1.0146 - val_accuracy: 0.5749 - val_loss: 0.8991
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 1.0006
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 0.9959 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0002
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0010
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 0.9998
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5480 - loss: 0.9998 - val_accuracy: 0.5808 - val_loss: 0.9004
Epoch 64/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5156 - loss: 1.0737
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5562 - loss: 1.0106 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5554 - loss: 1.0073
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 1.0086
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5517 - loss: 1.0074 - val_accuracy: 0.5851 - val_loss: 0.8956
Epoch 65/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9557
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5621 - loss: 0.9784 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5601 - loss: 0.9862
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 0.9920
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5554 - loss: 0.9950 - val_accuracy: 0.5933 - val_loss: 0.8912
Epoch 66/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.9189
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5669 - loss: 0.9968 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5645 - loss: 0.9945
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5648 - loss: 0.9930
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5643 - loss: 0.9934 - val_accuracy: 0.5940 - val_loss: 0.8905
Epoch 67/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1701
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5521 - loss: 1.0032 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 0.9985
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5566 - loss: 0.9970
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5580 - loss: 0.9970 - val_accuracy: 0.5861 - val_loss: 0.8771
Epoch 68/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1125
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5773 - loss: 0.9699 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5683 - loss: 0.9798
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5650 - loss: 0.9827
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5636 - loss: 0.9835
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5635 - loss: 0.9836 - val_accuracy: 0.5447 - val_loss: 0.9407
Epoch 69/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 34ms/step - accuracy: 0.5156 - loss: 1.0757
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0131 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0092
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0027
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 0.9986
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5382 - loss: 0.9980 - val_accuracy: 0.5969 - val_loss: 0.8743
Epoch 70/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9103
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5597 - loss: 0.9675 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5593 - loss: 0.9781
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5594 - loss: 0.9803
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5594 - loss: 0.9816
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5594 - loss: 0.9816 - val_accuracy: 0.5907 - val_loss: 0.9073
Epoch 71/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1319
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 0.9928 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5552 - loss: 0.9923
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5568 - loss: 0.9894
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 0.9877
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5574 - loss: 0.9876 - val_accuracy: 0.5772 - val_loss: 0.8759
Epoch 72/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0870
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5536 - loss: 0.9752 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 0.9744
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5566 - loss: 0.9755
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5565 - loss: 0.9764 - val_accuracy: 0.5404 - val_loss: 0.9090
Epoch 73/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9831
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5561 - loss: 0.9730 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5553 - loss: 0.9809
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5547 - loss: 0.9826
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5546 - loss: 0.9819 - val_accuracy: 0.5700 - val_loss: 0.9109
Epoch 74/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 31ms/step - accuracy: 0.5156 - loss: 0.9539
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5512 - loss: 0.9813 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 0.9806
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 0.9820
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5478 - loss: 0.9841 - val_accuracy: 0.5907 - val_loss: 0.8814

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 501ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 13: 54.56 [%]
F1-score capturado en la ejecución 13: 54.22 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:48[0m 690ms/step
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 758us/step  
[1m136/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 742us/step
[1m209/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 724us/step
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 705us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m2s[0m 23ms/step
[1m76/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 673us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 59.4 [%]
Global F1 score (validation) = 57.26 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.29935777 0.24147846 0.19619845 0.2629653 ]
 [0.30751857 0.2485499  0.19057505 0.25335646]
 [0.2882109  0.22488862 0.24177724 0.2451232 ]
 ...
 [0.0443989  0.02561725 0.9040173  0.02596649]
 [0.05700111 0.03376492 0.87446517 0.03476873]
 [0.07997263 0.04947624 0.81838477 0.05216638]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 62.02 [%]
Global accuracy score (test) = 56.12 [%]
Global F1 score (train) = 59.63 [%]
Global F1 score (test) = 54.52 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.23      0.29       400
MODERATE-INTENSITY       0.49      0.52      0.50       400
         SEDENTARY       0.55      0.93      0.69       400
VIGOROUS-INTENSITY       0.88      0.58      0.70       345

          accuracy                           0.56      1545
         macro avg       0.58      0.56      0.55      1545
      weighted avg       0.57      0.56      0.54      1545

2025-11-05 11:25:02.364064: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:25:02.375652: 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 00:00:1762338302.388946 3279885 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338302.393109 3279885 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338302.402990 3279885 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338302.403009 3279885 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338302.403011 3279885 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338302.403013 3279885 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:25:02.406206: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338304.672722 3279885 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338306.044519 3280020 service.cc:152] XLA service 0x76f71c004bb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338306.044567 3280020 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:25:06.077620: 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 00:00:1762338306.196006 3280020 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338308.318829 3280020 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:22[0m 3s/step - accuracy: 0.2500 - loss: 2.4658
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2694 - loss: 2.4462 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2780 - loss: 2.3315
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2821 - loss: 2.2436
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2857 - loss: 2.1713
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2858 - loss: 2.1698
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2859 - loss: 2.1683 - val_accuracy: 0.3755 - val_loss: 1.2683
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2500 - loss: 1.5772
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.5576 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3102 - loss: 1.5465
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3140 - loss: 1.5308
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3158 - loss: 1.5173
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3163 - loss: 1.5136 - val_accuracy: 0.3909 - val_loss: 1.2669
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.3002
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3501 - loss: 1.3693 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3496 - loss: 1.3647
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.3639
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3447 - loss: 1.3626
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3446 - loss: 1.3623 - val_accuracy: 0.3922 - val_loss: 1.2676
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2973
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3674 - loss: 1.3287 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3626 - loss: 1.3298
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3612 - loss: 1.3284
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3611 - loss: 1.3271 - val_accuracy: 0.3886 - val_loss: 1.2633
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.3033
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3800 - loss: 1.2983 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3704 - loss: 1.3011
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3672 - loss: 1.3021
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3671 - loss: 1.3015 - val_accuracy: 0.3834 - val_loss: 1.2482
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2910
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3772 - loss: 1.2995 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3742 - loss: 1.2974
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3744 - loss: 1.2957
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3749 - loss: 1.2942
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3749 - loss: 1.2941 - val_accuracy: 0.3824 - val_loss: 1.2398
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.2666
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3906 - loss: 1.2671 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3876 - loss: 1.2701
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.2700
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3878 - loss: 1.2695 - val_accuracy: 0.3873 - val_loss: 1.2275
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3101
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3807 - loss: 1.2551 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3832 - loss: 1.2549
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3844 - loss: 1.2560
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3851 - loss: 1.2567 - val_accuracy: 0.3949 - val_loss: 1.2086
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2677
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3901 - loss: 1.2454 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3906 - loss: 1.2430
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3916 - loss: 1.2411
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3918 - loss: 1.2403 - val_accuracy: 0.4008 - val_loss: 1.1880
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1959
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3825 - loss: 1.2389 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3884 - loss: 1.2392
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3920 - loss: 1.2377
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3939 - loss: 1.2370 - val_accuracy: 0.4093 - val_loss: 1.1763
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1868
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4060 - loss: 1.2224 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4063 - loss: 1.2228
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4078 - loss: 1.2221
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4090 - loss: 1.2204 - val_accuracy: 0.4537 - val_loss: 1.1604
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2066
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4156 - loss: 1.1942 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4179 - loss: 1.1993
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4161 - loss: 1.2029
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4149 - loss: 1.2047
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4149 - loss: 1.2048 - val_accuracy: 0.4402 - val_loss: 1.1397
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1679
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4159 - loss: 1.2012 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.1967
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4218 - loss: 1.1962
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4217 - loss: 1.1958 - val_accuracy: 0.4488 - val_loss: 1.1346
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.3006
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.2250 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4168 - loss: 1.2112
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.2042
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4210 - loss: 1.2020
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4210 - loss: 1.2018 - val_accuracy: 0.4520 - val_loss: 1.1298
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1104
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4178 - loss: 1.1802 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4257 - loss: 1.1761
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4280 - loss: 1.1763
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4289 - loss: 1.1773 - val_accuracy: 0.4859 - val_loss: 1.1022
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1605
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4487 - loss: 1.1801 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.1804
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.1814
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4416 - loss: 1.1824
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4415 - loss: 1.1824 - val_accuracy: 0.4619 - val_loss: 1.1123
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0496
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4488 - loss: 1.1402 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.1468
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4443 - loss: 1.1521
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.1565
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4426 - loss: 1.1568 - val_accuracy: 0.4947 - val_loss: 1.1083
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0458
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4537 - loss: 1.1707 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4527 - loss: 1.1738
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4504 - loss: 1.1743
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4495 - loss: 1.1733 - val_accuracy: 0.4786 - val_loss: 1.0934
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2029
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4563 - loss: 1.1799 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4500 - loss: 1.1741
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4486 - loss: 1.1717
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.1698
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4483 - loss: 1.1697 - val_accuracy: 0.4570 - val_loss: 1.1615
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1754
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1371 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4818 - loss: 1.1416
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1457
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1489
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4702 - loss: 1.1491 - val_accuracy: 0.4629 - val_loss: 1.0980
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 1.1988
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4419 - loss: 1.1650 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.1653
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4493 - loss: 1.1645
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4503 - loss: 1.1635
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4504 - loss: 1.1635 - val_accuracy: 0.4589 - val_loss: 1.1602
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.2646
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.1842 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4409 - loss: 1.1729
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.1670
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4487 - loss: 1.1635
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4489 - loss: 1.1632 - val_accuracy: 0.4373 - val_loss: 1.1500
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5156 - loss: 1.0673
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4593 - loss: 1.1514 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4548 - loss: 1.1531
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4536 - loss: 1.1531
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4545 - loss: 1.1527 - val_accuracy: 0.5108 - val_loss: 1.0621
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2484
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4436 - loss: 1.2011 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1809
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1696
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4603 - loss: 1.1633
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4604 - loss: 1.1629 - val_accuracy: 0.4947 - val_loss: 1.0884
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1746
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1596 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1539
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4666 - loss: 1.1507
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1477
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4674 - loss: 1.1476 - val_accuracy: 0.5007 - val_loss: 1.0670
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1586
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1515 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.1526
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4593 - loss: 1.1496
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4613 - loss: 1.1469 - val_accuracy: 0.5030 - val_loss: 1.0611
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.0619
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1337 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1278
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4900 - loss: 1.1263
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4891 - loss: 1.1266 - val_accuracy: 0.5243 - val_loss: 1.0415
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.0835
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1300 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4725 - loss: 1.1338
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4726 - loss: 1.1331
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1317
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4730 - loss: 1.1316 - val_accuracy: 0.5210 - val_loss: 1.0404
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0667
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1137 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1182
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1197
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4847 - loss: 1.1197 - val_accuracy: 0.4849 - val_loss: 1.0665
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1394
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1161 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4680 - loss: 1.1211
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4706 - loss: 1.1205
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4730 - loss: 1.1184
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.1184 - val_accuracy: 0.4901 - val_loss: 1.0954
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1395
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.0930 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4907 - loss: 1.0966
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.0999
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4895 - loss: 1.1013 - val_accuracy: 0.5306 - val_loss: 1.0238
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0511
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1043 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1061
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1045
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1041
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4970 - loss: 1.1041 - val_accuracy: 0.5306 - val_loss: 1.0143
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1693
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1069 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.1044
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4906 - loss: 1.1020
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4928 - loss: 1.1006
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4928 - loss: 1.1006 - val_accuracy: 0.5503 - val_loss: 0.9956
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.5000 - loss: 1.1684
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5083 - loss: 1.0853 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0829
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0849
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0869
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5114 - loss: 1.0870 - val_accuracy: 0.5125 - val_loss: 1.0274
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.2115
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4717 - loss: 1.1084 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1012
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.0988
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4879 - loss: 1.0979
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4883 - loss: 1.0978 - val_accuracy: 0.5486 - val_loss: 0.9981
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.5312 - loss: 0.9372
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0659 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0736
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5063 - loss: 1.0777
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5061 - loss: 1.0792 - val_accuracy: 0.5453 - val_loss: 0.9824
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9780
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0840 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0821
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0813
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.0811 - val_accuracy: 0.5368 - val_loss: 0.9876
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0405
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0645 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5212 - loss: 1.0631
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0628
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0631
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0631 - val_accuracy: 0.5368 - val_loss: 0.9893
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1440
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0777 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0752
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0740
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0737 - val_accuracy: 0.5601 - val_loss: 0.9579
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5625 - loss: 1.1812
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0956 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5198 - loss: 1.0856
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5215 - loss: 1.0779
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5219 - loss: 1.0745 - val_accuracy: 0.5568 - val_loss: 0.9542
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1974
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5314 - loss: 1.0587 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0632
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0653
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5253 - loss: 1.0647 - val_accuracy: 0.5549 - val_loss: 0.9568
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9830
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.0773 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5134 - loss: 1.0674
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0629
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5167 - loss: 1.0612 - val_accuracy: 0.4757 - val_loss: 1.2948
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2415
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0832 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0736
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5261 - loss: 1.0695
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5259 - loss: 1.0666 - val_accuracy: 0.5723 - val_loss: 0.9284
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9458
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0332 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5380 - loss: 1.0476
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0511
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0521
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5354 - loss: 1.0521 - val_accuracy: 0.5706 - val_loss: 0.9182
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0513
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0499 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0476
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0460
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5319 - loss: 1.0462 - val_accuracy: 0.5598 - val_loss: 0.9392
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 1.0045
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0475 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0450
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0443
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5332 - loss: 1.0430 - val_accuracy: 0.5687 - val_loss: 0.9509
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5312 - loss: 1.1930
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0497 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5349 - loss: 1.0481
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0449
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5379 - loss: 1.0418 - val_accuracy: 0.5808 - val_loss: 0.9197
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0463
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0445 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0430
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0423
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0415
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5369 - loss: 1.0414 - val_accuracy: 0.5690 - val_loss: 0.9331
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9416
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5556 - loss: 1.0048 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 1.0163
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 1.0210
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5455 - loss: 1.0241 - val_accuracy: 0.5903 - val_loss: 0.9065
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1510
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5492 - loss: 1.0317 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5504 - loss: 1.0262
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0266
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0270
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5474 - loss: 1.0270 - val_accuracy: 0.5539 - val_loss: 0.9888
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0927
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5304 - loss: 1.0634 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0531
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5354 - loss: 1.0478
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5363 - loss: 1.0464 - val_accuracy: 0.5864 - val_loss: 0.9025
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0182
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0044 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0121
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5401 - loss: 1.0150
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0161
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5401 - loss: 1.0164 - val_accuracy: 0.5802 - val_loss: 0.9392
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4688 - loss: 1.1224
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0319 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5269 - loss: 1.0272
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0243
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0221
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5365 - loss: 1.0218 - val_accuracy: 0.5867 - val_loss: 0.9066
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9882
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0155 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 1.0111
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5476 - loss: 1.0099
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 1.0098
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5491 - loss: 1.0098 - val_accuracy: 0.5890 - val_loss: 0.8990
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9853
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0239 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5448 - loss: 1.0217
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5443 - loss: 1.0209
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5449 - loss: 1.0192 - val_accuracy: 0.5877 - val_loss: 0.8943
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0273
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5566 - loss: 0.9783 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 0.9834
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 0.9892
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5504 - loss: 0.9932 - val_accuracy: 0.5953 - val_loss: 0.8979
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9010
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5547 - loss: 1.0174 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 1.0178
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5512 - loss: 1.0162
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 1.0152
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5522 - loss: 1.0151 - val_accuracy: 0.6091 - val_loss: 0.8810
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0343
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5497 - loss: 1.0035 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0220
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5447 - loss: 1.0240
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5461 - loss: 1.0220
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5464 - loss: 1.0214 - val_accuracy: 0.5841 - val_loss: 0.9037
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.2072
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0418 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5492 - loss: 1.0309
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5493 - loss: 1.0252
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5492 - loss: 1.0209
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5492 - loss: 1.0208 - val_accuracy: 0.6028 - val_loss: 0.8780
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6094 - loss: 0.9789
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5546 - loss: 1.0168 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5556 - loss: 1.0093
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 1.0072
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5555 - loss: 1.0063 - val_accuracy: 0.6028 - val_loss: 0.8721
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 0.9858
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5566 - loss: 0.9915 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5531 - loss: 0.9978
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 1.0016
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5528 - loss: 1.0025 - val_accuracy: 0.5765 - val_loss: 0.9176
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5781 - loss: 1.0806
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5549 - loss: 0.9983 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5532 - loss: 0.9988
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 0.9989
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5519 - loss: 0.9997 - val_accuracy: 0.5782 - val_loss: 0.8997
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9351
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5704 - loss: 0.9879 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5680 - loss: 0.9879
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5647 - loss: 0.9900
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5633 - loss: 0.9918 - val_accuracy: 0.5907 - val_loss: 0.8911
Epoch 64/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9216
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5681 - loss: 0.9833 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5642 - loss: 0.9842
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5625 - loss: 0.9868
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5619 - loss: 0.9872
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5619 - loss: 0.9872 - val_accuracy: 0.5887 - val_loss: 0.8846
Epoch 65/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9857
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5509 - loss: 0.9983 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5502 - loss: 1.0009
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 0.9983
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5546 - loss: 0.9979
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5547 - loss: 0.9979 - val_accuracy: 0.5943 - val_loss: 0.8712
Epoch 66/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 1.0679
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 0.9909 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5567 - loss: 0.9875
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5594 - loss: 0.9843
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5602 - loss: 0.9838
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5603 - loss: 0.9838 - val_accuracy: 0.5844 - val_loss: 0.8866
Epoch 67/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9652
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5415 - loss: 0.9974 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5532 - loss: 0.9873
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5550 - loss: 0.9849
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5556 - loss: 0.9839 - val_accuracy: 0.5923 - val_loss: 0.8681
Epoch 68/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1361
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5298 - loss: 1.0229 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0151
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0111
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5460 - loss: 1.0069 - val_accuracy: 0.5210 - val_loss: 1.0547
Epoch 69/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0375
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5528 - loss: 1.0273 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 1.0147
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5567 - loss: 1.0081
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 1.0028
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5586 - loss: 1.0026 - val_accuracy: 0.6091 - val_loss: 0.8599
Epoch 70/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0201
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5678 - loss: 1.0061 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5631 - loss: 0.9963
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5612 - loss: 0.9944
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5603 - loss: 0.9938 - val_accuracy: 0.5903 - val_loss: 0.8854
Epoch 71/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9948
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5821 - loss: 0.9705 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5745 - loss: 0.9790
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5697 - loss: 0.9845
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5681 - loss: 0.9849
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5680 - loss: 0.9849 - val_accuracy: 0.5923 - val_loss: 0.8895
Epoch 72/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.6875 - loss: 0.8178
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5805 - loss: 0.9640 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5730 - loss: 0.9662
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5708 - loss: 0.9669
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5697 - loss: 0.9676
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5697 - loss: 0.9676 - val_accuracy: 0.6035 - val_loss: 0.8577
Epoch 73/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.1334
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5630 - loss: 0.9996 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5665 - loss: 0.9873
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5681 - loss: 0.9848
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5685 - loss: 0.9833
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5684 - loss: 0.9831 - val_accuracy: 0.6025 - val_loss: 0.8568
Epoch 74/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.9143
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5530 - loss: 0.9678 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5575 - loss: 0.9710
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5583 - loss: 0.9715
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5589 - loss: 0.9724 - val_accuracy: 0.5992 - val_loss: 0.8832
Epoch 75/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9576
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5610 - loss: 0.9758 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5640 - loss: 0.9762
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5642 - loss: 0.9770
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5643 - loss: 0.9763
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5644 - loss: 0.9762 - val_accuracy: 0.6071 - val_loss: 0.8745
Epoch 76/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.8380
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5582 - loss: 0.9860 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5621 - loss: 0.9832
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5643 - loss: 0.9799
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5655 - loss: 0.9778 - val_accuracy: 0.5920 - val_loss: 0.8692
Epoch 77/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.5469 - loss: 1.0782
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5705 - loss: 0.9674 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5668 - loss: 0.9747
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5654 - loss: 0.9768
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5644 - loss: 0.9768
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5644 - loss: 0.9767 - val_accuracy: 0.6071 - val_loss: 0.8457
Epoch 78/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 0.8316
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5897 - loss: 0.9387 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5866 - loss: 0.9406
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5824 - loss: 0.9487
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5796 - loss: 0.9535 - val_accuracy: 0.6094 - val_loss: 0.8453
Epoch 79/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9336
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 0.9737 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5558 - loss: 0.9777
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5619 - loss: 0.9732
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5640 - loss: 0.9720
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5640 - loss: 0.9720 - val_accuracy: 0.6091 - val_loss: 0.8711
Epoch 80/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0405
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5735 - loss: 0.9762 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5694 - loss: 0.9807
[1m136/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5663 - loss: 0.9804
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5662 - loss: 0.9790 - val_accuracy: 0.6124 - val_loss: 0.8561
Epoch 81/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.8593
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5799 - loss: 0.9370 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5783 - loss: 0.9449
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5776 - loss: 0.9492
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5766 - loss: 0.9516
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5764 - loss: 0.9522 - val_accuracy: 0.4842 - val_loss: 1.0770
Epoch 82/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0007
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5548 - loss: 0.9775 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5640 - loss: 0.9653
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5670 - loss: 0.9607
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5685 - loss: 0.9592 - val_accuracy: 0.5995 - val_loss: 0.8604
Epoch 83/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.7887
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5650 - loss: 0.9408 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5675 - loss: 0.9474
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5689 - loss: 0.9481
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5686 - loss: 0.9505
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5685 - loss: 0.9506 - val_accuracy: 0.5949 - val_loss: 0.8679

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 492ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 14: 56.12 [%]
F1-score capturado en la ejecución 14: 54.52 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:49[0m 691ms/step
[1m 64/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 796us/step  
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 721us/step
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 700us/step
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 698us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m74/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 694us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 58.31 [%]
Global F1 score (validation) = 58.5 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.43688303 0.45805967 0.01843948 0.08661783]
 [0.3201227  0.25844216 0.21285318 0.20858197]
 [0.44716072 0.44592673 0.0253235  0.08158903]
 ...
 [0.19247495 0.12735379 0.57890695 0.10126439]
 [0.03049812 0.01527383 0.93706405 0.01716403]
 [0.02651083 0.01306546 0.945777   0.01464673]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 63.21 [%]
Global accuracy score (test) = 59.48 [%]
Global F1 score (train) = 63.16 [%]
Global F1 score (test) = 59.35 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.45      0.37      0.41       400
MODERATE-INTENSITY       0.52      0.57      0.54       400
         SEDENTARY       0.60      0.88      0.71       400
VIGOROUS-INTENSITY       0.97      0.56      0.71       345

          accuracy                           0.59      1545
         macro avg       0.64      0.59      0.59      1545
      weighted avg       0.63      0.59      0.59      1545

2025-11-05 11:25:48.765021: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:25:48.776533: 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 00:00:1762338348.789928 3288459 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338348.794162 3288459 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338348.804007 3288459 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338348.804025 3288459 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338348.804028 3288459 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338348.804029 3288459 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:25:48.807167: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338351.038354 3288459 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338352.399205 3288574 service.cc:152] XLA service 0x7f67840048b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338352.399234 3288574 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:25:52.432568: 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 00:00:1762338352.551684 3288574 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338354.695122 3288574 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:24[0m 3s/step - accuracy: 0.1875 - loss: 2.4690
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2670 - loss: 2.3539 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2768 - loss: 2.2389
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2804 - loss: 2.1710
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2834 - loss: 2.1144
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2837 - loss: 2.1069
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2838 - loss: 2.1057 - val_accuracy: 0.3978 - val_loss: 1.2578
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.5015
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2958 - loss: 1.5675 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2986 - loss: 1.5523
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3019 - loss: 1.5327
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3045 - loss: 1.5179 - val_accuracy: 0.4093 - val_loss: 1.2694
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3281 - loss: 1.3979
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3273 - loss: 1.3855 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3306 - loss: 1.3781
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.3728
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3382 - loss: 1.3686
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3387 - loss: 1.3678 - val_accuracy: 0.3959 - val_loss: 1.2634
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.3532
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3815 - loss: 1.3293 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3726 - loss: 1.3264
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3679 - loss: 1.3253
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3663 - loss: 1.3242 - val_accuracy: 0.3873 - val_loss: 1.2556
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 1.3731
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3603 - loss: 1.3218 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3635 - loss: 1.3155
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.3144
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3627 - loss: 1.3129 - val_accuracy: 0.4021 - val_loss: 1.2441
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.3938
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.2958 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3590 - loss: 1.2937
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.2916
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3639 - loss: 1.2911
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3639 - loss: 1.2911 - val_accuracy: 0.4008 - val_loss: 1.2274
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 1.2804
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3591 - loss: 1.2897 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3661 - loss: 1.2808
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.2786
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3725 - loss: 1.2774
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3733 - loss: 1.2770 - val_accuracy: 0.4074 - val_loss: 1.2114
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.2352
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3793 - loss: 1.2668 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3847 - loss: 1.2629
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3877 - loss: 1.2623
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3894 - loss: 1.2618
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3896 - loss: 1.2618 - val_accuracy: 0.4011 - val_loss: 1.1939
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1794
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4234 - loss: 1.2432 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4155 - loss: 1.2465
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4114 - loss: 1.2466
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4099 - loss: 1.2465 - val_accuracy: 0.4228 - val_loss: 1.1720
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.1901
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3884 - loss: 1.2496 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3941 - loss: 1.2452
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3967 - loss: 1.2422
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3983 - loss: 1.2403 - val_accuracy: 0.4336 - val_loss: 1.1539
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2567
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4255 - loss: 1.2013 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4229 - loss: 1.2071
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4197 - loss: 1.2106
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4180 - loss: 1.2123
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4179 - loss: 1.2124 - val_accuracy: 0.4777 - val_loss: 1.1330
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0878
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.1939 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4302 - loss: 1.2060
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4277 - loss: 1.2078
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4265 - loss: 1.2082 - val_accuracy: 0.4557 - val_loss: 1.1349
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2894
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.1854 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4371 - loss: 1.1909
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.1950
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4351 - loss: 1.1959 - val_accuracy: 0.4481 - val_loss: 1.1455
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2007
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4340 - loss: 1.1869 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4340 - loss: 1.1911
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4339 - loss: 1.1925
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4335 - loss: 1.1929
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4335 - loss: 1.1929 - val_accuracy: 0.4790 - val_loss: 1.1043
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2071
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4245 - loss: 1.1902 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4306 - loss: 1.1855
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.1861
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4340 - loss: 1.1858 - val_accuracy: 0.4826 - val_loss: 1.1170
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2895
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1877 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1888
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4504 - loss: 1.1872
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4481 - loss: 1.1850
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4480 - loss: 1.1848 - val_accuracy: 0.4773 - val_loss: 1.1076
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2805
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.1790 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4468 - loss: 1.1792
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4475 - loss: 1.1781
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4482 - loss: 1.1766 - val_accuracy: 0.4547 - val_loss: 1.1120
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0914
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4666 - loss: 1.1590 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4615 - loss: 1.1614
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4589 - loss: 1.1626
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4572 - loss: 1.1629 - val_accuracy: 0.4901 - val_loss: 1.1035
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1239
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.1287 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1352
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1416
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4708 - loss: 1.1451 - val_accuracy: 0.5033 - val_loss: 1.0625
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2017
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4821 - loss: 1.1237 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1337
[1m113/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1383
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1403
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4683 - loss: 1.1406 - val_accuracy: 0.4997 - val_loss: 1.0612
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0736
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4806 - loss: 1.1358 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1381
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4711 - loss: 1.1384
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4685 - loss: 1.1383
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4680 - loss: 1.1381 - val_accuracy: 0.4599 - val_loss: 1.1126
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.3109
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1438 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4635 - loss: 1.1460
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1471
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1466
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4626 - loss: 1.1465 - val_accuracy: 0.5089 - val_loss: 1.0469
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3906 - loss: 1.1640
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4536 - loss: 1.1268 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1307
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4657 - loss: 1.1323
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1324
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4685 - loss: 1.1324 - val_accuracy: 0.4970 - val_loss: 1.0615
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2366
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4748 - loss: 1.1371 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1314
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4829 - loss: 1.1303
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4830 - loss: 1.1303 - val_accuracy: 0.5154 - val_loss: 1.0370
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.3131
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1270 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1249
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.1253
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4819 - loss: 1.1241 - val_accuracy: 0.5108 - val_loss: 1.0456
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.2105
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1377 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4776 - loss: 1.1268
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1213
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4811 - loss: 1.1195 - val_accuracy: 0.5177 - val_loss: 1.0404
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5625 - loss: 1.1122
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5031 - loss: 1.0953 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.0924
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.0938
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5009 - loss: 1.0952 - val_accuracy: 0.5204 - val_loss: 1.0347
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 0.9896
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1041 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4837 - loss: 1.1056
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4866 - loss: 1.1048
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1035
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4890 - loss: 1.1034 - val_accuracy: 0.5378 - val_loss: 1.0194
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1452
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.0965 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.0949
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.0942
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4970 - loss: 1.0940 - val_accuracy: 0.5187 - val_loss: 1.0152
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1248
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.1033 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0953
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.0932
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5103 - loss: 1.0916 - val_accuracy: 0.5437 - val_loss: 0.9863
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1720
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0713 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0789
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.0814
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5092 - loss: 1.0822 - val_accuracy: 0.5365 - val_loss: 0.9982
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9120
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0588 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5279 - loss: 1.0698
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0740
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5214 - loss: 1.0768 - val_accuracy: 0.5496 - val_loss: 0.9842
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.1932
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.1152 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5108 - loss: 1.1038
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0992
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.0962
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5093 - loss: 1.0961 - val_accuracy: 0.5414 - val_loss: 0.9754
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0715
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0632 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5155 - loss: 1.0638
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0644
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0649
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5150 - loss: 1.0650 - val_accuracy: 0.5375 - val_loss: 0.9769
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0380
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0687 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0687
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0675
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5200 - loss: 1.0667 - val_accuracy: 0.5427 - val_loss: 0.9686
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0518
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0646 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0629
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5180 - loss: 1.0635
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5191 - loss: 1.0623
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5192 - loss: 1.0622 - val_accuracy: 0.5644 - val_loss: 0.9815
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1460
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0776 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0688
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0661
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0644
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5230 - loss: 1.0641 - val_accuracy: 0.5312 - val_loss: 0.9983
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9793
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5447 - loss: 1.0322 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0426
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5393 - loss: 1.0443
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0465
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5380 - loss: 1.0473 - val_accuracy: 0.5614 - val_loss: 0.9509
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1416
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0661 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0601
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0561
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5189 - loss: 1.0539 - val_accuracy: 0.5565 - val_loss: 0.9554
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.0964
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0317 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5217 - loss: 1.0385
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5245 - loss: 1.0382
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5267 - loss: 1.0375
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5268 - loss: 1.0375 - val_accuracy: 0.5568 - val_loss: 0.9486
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9424
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0334 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5347 - loss: 1.0320
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0319
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5362 - loss: 1.0325 - val_accuracy: 0.5572 - val_loss: 0.9617
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0402
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0112 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0193
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0222
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5356 - loss: 1.0248 - val_accuracy: 0.5443 - val_loss: 1.0149
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5938 - loss: 0.9423
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0156 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0176
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0200
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5412 - loss: 1.0211 - val_accuracy: 0.5851 - val_loss: 0.9461
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9164
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0083 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5468 - loss: 1.0185
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0209
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5449 - loss: 1.0227 - val_accuracy: 0.5811 - val_loss: 0.9358
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0287
[1m 47/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0254 
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0278
[1m136/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0275
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5445 - loss: 1.0269 - val_accuracy: 0.5512 - val_loss: 0.9564
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.0390
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0180 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0138
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0108
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0104
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5449 - loss: 1.0105 - val_accuracy: 0.5749 - val_loss: 0.9218
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9531
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5379 - loss: 0.9936 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0032
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0065
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5401 - loss: 1.0075 - val_accuracy: 0.5769 - val_loss: 0.9205
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 0.9618
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0183 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 1.0129
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0109
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5481 - loss: 1.0102 - val_accuracy: 0.5624 - val_loss: 0.9254
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0996
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0385 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5439 - loss: 1.0284
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0244
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5493 - loss: 1.0209 - val_accuracy: 0.5627 - val_loss: 0.9335
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.6250 - loss: 0.9261
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5615 - loss: 0.9608 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5585 - loss: 0.9720
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5572 - loss: 0.9772
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5573 - loss: 0.9809
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5573 - loss: 0.9812 - val_accuracy: 0.5923 - val_loss: 0.9068
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5469 - loss: 1.0648
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0169 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0129
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0085
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5455 - loss: 1.0050 - val_accuracy: 0.5749 - val_loss: 0.9049
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0426
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5466 - loss: 1.0006 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5512 - loss: 0.9969
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 0.9987
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5533 - loss: 0.9984
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5533 - loss: 0.9984 - val_accuracy: 0.5749 - val_loss: 0.9102
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5625 - loss: 0.9513
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5398 - loss: 0.9958 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5463 - loss: 0.9950
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 0.9946
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5523 - loss: 0.9935 - val_accuracy: 0.5558 - val_loss: 0.9423
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.5781 - loss: 0.9439
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5557 - loss: 0.9915 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5594 - loss: 0.9866
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5598 - loss: 0.9851
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5600 - loss: 0.9844
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5601 - loss: 0.9843 - val_accuracy: 0.5729 - val_loss: 0.9208
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.7031 - loss: 0.8299
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5610 - loss: 0.9729 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 0.9797
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5573 - loss: 0.9817
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5586 - loss: 0.9813 - val_accuracy: 0.5867 - val_loss: 0.8932
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.9690
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5471 - loss: 0.9982 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5529 - loss: 0.9931
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5562 - loss: 0.9895
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5573 - loss: 0.9876
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5573 - loss: 0.9874 - val_accuracy: 0.5762 - val_loss: 0.9093
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.4688 - loss: 1.0520
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5505 - loss: 0.9921 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5518 - loss: 0.9926
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5532 - loss: 0.9904
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5545 - loss: 0.9887 - val_accuracy: 0.5749 - val_loss: 0.9050
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.8798
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5478 - loss: 0.9712 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5510 - loss: 0.9727
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5535 - loss: 0.9741
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5552 - loss: 0.9745 - val_accuracy: 0.5690 - val_loss: 0.9312
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.2169
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5385 - loss: 1.0328 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5459 - loss: 1.0133
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5503 - loss: 1.0015
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5524 - loss: 0.9948 - val_accuracy: 0.5874 - val_loss: 0.8971
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9011
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 0.9803 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 0.9731
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5595 - loss: 0.9707
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5608 - loss: 0.9701 - val_accuracy: 0.5877 - val_loss: 0.8871
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.8215
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5986 - loss: 0.9353 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5852 - loss: 0.9462
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5780 - loss: 0.9511
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5752 - loss: 0.9530
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5751 - loss: 0.9532 - val_accuracy: 0.5976 - val_loss: 0.8777
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6875 - loss: 0.9054
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5775 - loss: 0.9517 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5696 - loss: 0.9547
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5668 - loss: 0.9595
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5669 - loss: 0.9610
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5670 - loss: 0.9611 - val_accuracy: 0.5631 - val_loss: 0.9148
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1007
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0113 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5472 - loss: 0.9933
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 0.9870
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5560 - loss: 0.9810
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5563 - loss: 0.9806 - val_accuracy: 0.5779 - val_loss: 0.8963
Epoch 64/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9552
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5672 - loss: 0.9681 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5638 - loss: 0.9645
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5641 - loss: 0.9628
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5650 - loss: 0.9610 - val_accuracy: 0.5756 - val_loss: 0.9163
Epoch 65/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6875 - loss: 0.8545
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5559 - loss: 0.9954 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5613 - loss: 0.9869
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5633 - loss: 0.9797
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5654 - loss: 0.9730
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5654 - loss: 0.9729 - val_accuracy: 0.5871 - val_loss: 0.8950
Epoch 66/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6719 - loss: 0.8683
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5882 - loss: 0.9253 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5825 - loss: 0.9335
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5801 - loss: 0.9371
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5779 - loss: 0.9413 - val_accuracy: 0.5943 - val_loss: 0.8913

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 500ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 15: 59.48 [%]
F1-score capturado en la ejecución 15: 59.35 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:48[0m 687ms/step
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 750us/step  
[1m143/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 706us/step
[1m223/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 679us/step
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 694us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 742us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 59.72 [%]
Global F1 score (validation) = 57.08 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.4438786  0.44633606 0.01296443 0.09682087]
 [0.2679518  0.20092638 0.24788156 0.28324026]
 [0.2780303  0.21090282 0.22700055 0.28406635]
 ...
 [0.05253679 0.03215633 0.8856306  0.02967633]
 [0.0535128  0.03281232 0.88342094 0.03025389]
 [0.04098911 0.0245308  0.9123581  0.02212204]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 63.38 [%]
Global accuracy score (test) = 57.41 [%]
Global F1 score (train) = 60.49 [%]
Global F1 score (test) = 55.58 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.21      0.29       400
MODERATE-INTENSITY       0.50      0.55      0.52       400
         SEDENTARY       0.56      0.92      0.70       400
VIGOROUS-INTENSITY       0.83      0.63      0.72       345

          accuracy                           0.57      1545
         macro avg       0.58      0.58      0.56      1545
      weighted avg       0.57      0.57      0.55      1545

2025-11-05 11:26:29.835562: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:26:29.846915: 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 00:00:1762338389.860107 3295470 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338389.864178 3295470 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338389.874285 3295470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338389.874306 3295470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338389.874309 3295470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338389.874311 3295470 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:26:29.877398: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338392.118761 3295470 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338393.551722 3295583 service.cc:152] XLA service 0x7c6e2000a420 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338393.551778 3295583 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:26:33.594064: 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 00:00:1762338393.714157 3295583 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338395.854463 3295583 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:28[0m 3s/step - accuracy: 0.2969 - loss: 2.6039
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2958 - loss: 2.2720 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2966 - loss: 2.2246
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2962 - loss: 2.1657
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2965 - loss: 2.1158
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2965 - loss: 2.1091
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2965 - loss: 2.1080 - val_accuracy: 0.3936 - val_loss: 1.2656
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3906 - loss: 1.4906
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3040 - loss: 1.5523 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3051 - loss: 1.5338
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3064 - loss: 1.5189
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3078 - loss: 1.5062
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3080 - loss: 1.5040 - val_accuracy: 0.3909 - val_loss: 1.2692
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.3582
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3282 - loss: 1.3678 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3181 - loss: 1.3745
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3178 - loss: 1.3741
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3185 - loss: 1.3722
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3187 - loss: 1.3720 - val_accuracy: 0.3903 - val_loss: 1.2572
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.3479
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3744 - loss: 1.3163 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.3201
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3639 - loss: 1.3235
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3616 - loss: 1.3248 - val_accuracy: 0.3893 - val_loss: 1.2503
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2464
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3477 - loss: 1.3123 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3483 - loss: 1.3138
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3476 - loss: 1.3143
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3487 - loss: 1.3140 - val_accuracy: 0.3807 - val_loss: 1.2451
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3085
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3606 - loss: 1.3011 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3653 - loss: 1.2958
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3663 - loss: 1.2940
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3672 - loss: 1.2932
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3672 - loss: 1.2931 - val_accuracy: 0.3883 - val_loss: 1.2254
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.3149
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3793 - loss: 1.2830 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3818 - loss: 1.2783
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3846 - loss: 1.2760
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3862 - loss: 1.2743
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3863 - loss: 1.2742 - val_accuracy: 0.3988 - val_loss: 1.2105
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3125 - loss: 1.3397
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3693 - loss: 1.2707 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3732 - loss: 1.2702
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3773 - loss: 1.2672
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3795 - loss: 1.2641 - val_accuracy: 0.4041 - val_loss: 1.1813
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2188 - loss: 1.3279
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3949 - loss: 1.2494 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3983 - loss: 1.2437
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4004 - loss: 1.2404
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4008 - loss: 1.2396
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4008 - loss: 1.2395 - val_accuracy: 0.4238 - val_loss: 1.1666
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1945
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4126 - loss: 1.2349 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4108 - loss: 1.2334
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4110 - loss: 1.2307
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4112 - loss: 1.2289 - val_accuracy: 0.4396 - val_loss: 1.1635
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2814
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4076 - loss: 1.2249 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4115 - loss: 1.2180
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4140 - loss: 1.2159
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4152 - loss: 1.2154 - val_accuracy: 0.4448 - val_loss: 1.1417
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 1.1788
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4179 - loss: 1.2041 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4219 - loss: 1.2023
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.2020
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4254 - loss: 1.2025
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4254 - loss: 1.2025 - val_accuracy: 0.4465 - val_loss: 1.1390
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.2639
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4383 - loss: 1.2133 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4295 - loss: 1.2131
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4278 - loss: 1.2105
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4286 - loss: 1.2072
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4287 - loss: 1.2069 - val_accuracy: 0.4402 - val_loss: 1.1288
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1805
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4703 - loss: 1.1747 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1789
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4521 - loss: 1.1832
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4487 - loss: 1.1840
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4485 - loss: 1.1841 - val_accuracy: 0.4694 - val_loss: 1.0998
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1630
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.1911 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4503 - loss: 1.1833
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4482 - loss: 1.1813
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4478 - loss: 1.1800 - val_accuracy: 0.4790 - val_loss: 1.0992
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1943
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.1958 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4386 - loss: 1.1918
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4413 - loss: 1.1870
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.1835
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4440 - loss: 1.1833 - val_accuracy: 0.4816 - val_loss: 1.1236
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1776
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4459 - loss: 1.1881 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4479 - loss: 1.1810
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.1769
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4515 - loss: 1.1751 - val_accuracy: 0.4790 - val_loss: 1.0873
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1162
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.1395 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1457
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1474
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1480
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4836 - loss: 1.1480 - val_accuracy: 0.4938 - val_loss: 1.0700
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1226
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1568 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1522
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1518
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4651 - loss: 1.1508 - val_accuracy: 0.4872 - val_loss: 1.0664
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1562
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1453 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4743 - loss: 1.1409
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4737 - loss: 1.1391
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4736 - loss: 1.1384 - val_accuracy: 0.4941 - val_loss: 1.0880
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1757
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1620 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1520
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1459
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1437
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4647 - loss: 1.1435 - val_accuracy: 0.5131 - val_loss: 1.0286
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9807
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1204 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4752 - loss: 1.1245
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1250
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4783 - loss: 1.1246
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4784 - loss: 1.1246 - val_accuracy: 0.5332 - val_loss: 1.0328
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0914
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4992 - loss: 1.1055 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1135
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.1156
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4939 - loss: 1.1152 - val_accuracy: 0.5250 - val_loss: 1.0207
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.3342
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1300 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.1201
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1184
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4908 - loss: 1.1176
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4908 - loss: 1.1176 - val_accuracy: 0.5161 - val_loss: 1.0223
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1905
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1235 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1153
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1110
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1105
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4861 - loss: 1.1104 - val_accuracy: 0.5256 - val_loss: 1.0096
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0821
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1029 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.1029
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1044
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5037 - loss: 1.1052 - val_accuracy: 0.5151 - val_loss: 0.9981
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5312 - loss: 1.0110
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5127 - loss: 1.0709 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0729
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0784
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0810
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5072 - loss: 1.0813 - val_accuracy: 0.5315 - val_loss: 0.9983
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1068
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.0912 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0899
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0887
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0873
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5092 - loss: 1.0873 - val_accuracy: 0.5102 - val_loss: 1.0024
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5312 - loss: 1.0430
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1301 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.1220
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5012 - loss: 1.1135
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5029 - loss: 1.1079 - val_accuracy: 0.5519 - val_loss: 0.9724
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1831
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1142 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.1017
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5115 - loss: 1.0951
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0908
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5139 - loss: 1.0900 - val_accuracy: 0.5552 - val_loss: 0.9730
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5469 - loss: 1.0786
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0778 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5184 - loss: 1.0729
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0737
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0741
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5154 - loss: 1.0741 - val_accuracy: 0.5473 - val_loss: 0.9626
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.3107
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0898 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0853
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0840
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0822
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.0820 - val_accuracy: 0.5749 - val_loss: 0.9431
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1701
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5399 - loss: 1.0634 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0589
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0584
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0586
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5337 - loss: 1.0587 - val_accuracy: 0.5391 - val_loss: 0.9484
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1994
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5046 - loss: 1.0959 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0837
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0787
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5181 - loss: 1.0736
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5182 - loss: 1.0735 - val_accuracy: 0.5670 - val_loss: 0.9336
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 28ms/step - accuracy: 0.5625 - loss: 1.0922
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5519 - loss: 1.0216 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0305
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0362
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5380 - loss: 1.0397 - val_accuracy: 0.5785 - val_loss: 0.9264
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9670
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0364 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5427 - loss: 1.0404
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0420
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0437
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5407 - loss: 1.0439 - val_accuracy: 0.5457 - val_loss: 0.9395
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1959
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0774 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0651
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5193 - loss: 1.0609
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5211 - loss: 1.0589 - val_accuracy: 0.5358 - val_loss: 0.9684
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1560
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0723 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5211 - loss: 1.0561
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0507
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0484
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5284 - loss: 1.0483 - val_accuracy: 0.5595 - val_loss: 0.9146
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.9744
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0350 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5353 - loss: 1.0341
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5361 - loss: 1.0358
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5364 - loss: 1.0365 - val_accuracy: 0.5690 - val_loss: 0.9307
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1179
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0492 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5378 - loss: 1.0416
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0379
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0358
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5421 - loss: 1.0358 - val_accuracy: 0.5713 - val_loss: 0.9203
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1391
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5478 - loss: 1.0304 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5471 - loss: 1.0307
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5463 - loss: 1.0281
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5459 - loss: 1.0272 - val_accuracy: 0.5670 - val_loss: 0.9188
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6875 - loss: 0.8887
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.6057 - loss: 0.9806 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5869 - loss: 0.9996
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5752 - loss: 1.0095
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5689 - loss: 1.0148 - val_accuracy: 0.5496 - val_loss: 0.9315
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9950
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0098 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5474 - loss: 1.0171
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0188
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5483 - loss: 1.0193 - val_accuracy: 0.5680 - val_loss: 0.9057
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.9406
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5720 - loss: 1.0084 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5642 - loss: 1.0139
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5595 - loss: 1.0159
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5568 - loss: 1.0164 - val_accuracy: 0.5677 - val_loss: 0.9271
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9776
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0125 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0221
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0227
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0213
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5437 - loss: 1.0212 - val_accuracy: 0.5759 - val_loss: 0.9129
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5469 - loss: 1.0408
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0206 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5419 - loss: 1.0186
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0160
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5459 - loss: 1.0149
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5461 - loss: 1.0148 - val_accuracy: 0.5608 - val_loss: 0.9566
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0485
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0171 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5502 - loss: 1.0106
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5505 - loss: 1.0081
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5509 - loss: 1.0070
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5509 - loss: 1.0070 - val_accuracy: 0.5821 - val_loss: 0.8922
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0613
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5639 - loss: 1.0015 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5583 - loss: 1.0051
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5529 - loss: 1.0099
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5507 - loss: 1.0126 - val_accuracy: 0.5989 - val_loss: 0.8713
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5156 - loss: 0.9302
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5577 - loss: 0.9844 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5523 - loss: 0.9940
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5524 - loss: 0.9957
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5518 - loss: 0.9983
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5518 - loss: 0.9984 - val_accuracy: 0.5604 - val_loss: 0.9211
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.1444
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5656 - loss: 0.9985 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5597 - loss: 1.0060
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5576 - loss: 1.0102
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 1.0109
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5574 - loss: 1.0109 - val_accuracy: 0.5861 - val_loss: 0.8914
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5469 - loss: 1.0251
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5730 - loss: 0.9924 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5724 - loss: 0.9883
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5690 - loss: 0.9887
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5666 - loss: 0.9905
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5665 - loss: 0.9905 - val_accuracy: 0.6028 - val_loss: 0.8760
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.8786
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 0.9999 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5587 - loss: 0.9934
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5590 - loss: 0.9938
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5588 - loss: 0.9953
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5587 - loss: 0.9955 - val_accuracy: 0.5844 - val_loss: 0.8932
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0281
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 0.9715 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5577 - loss: 0.9685
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 0.9749
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5558 - loss: 0.9806
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5558 - loss: 0.9811 - val_accuracy: 0.5641 - val_loss: 0.9108

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 489ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 16: 57.41 [%]
F1-score capturado en la ejecución 16: 55.58 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:54[0m 705ms/step
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 746us/step  
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 700us/step
[1m220/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 687us/step
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 704us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 19ms/step
[1m71/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 722us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 57.36 [%]
Global F1 score (validation) = 57.51 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.36950478 0.33222768 0.10543526 0.19283238]
 [0.4453884  0.51588494 0.00733743 0.0313893 ]
 [0.43761966 0.5008824  0.0092546  0.05224328]
 ...
 [0.09839345 0.06202873 0.7928692  0.04670858]
 [0.06721158 0.04035305 0.8615782  0.03085714]
 [0.06473638 0.03863183 0.8670172  0.02961459]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 62.49 [%]
Global accuracy score (test) = 54.95 [%]
Global F1 score (train) = 62.28 [%]
Global F1 score (test) = 54.91 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.34      0.28      0.31       400
MODERATE-INTENSITY       0.49      0.59      0.54       400
         SEDENTARY       0.58      0.80      0.67       400
VIGOROUS-INTENSITY       0.96      0.52      0.68       345

          accuracy                           0.55      1545
         macro avg       0.59      0.55      0.55      1545
      weighted avg       0.58      0.55      0.54      1545

2025-11-05 11:27:06.672599: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:27:06.683795: 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 00:00:1762338426.696801 3301263 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338426.700897 3301263 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338426.710682 3301263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338426.710701 3301263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338426.710703 3301263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338426.710705 3301263 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:27:06.713834: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338428.955688 3301263 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338430.366108 3301395 service.cc:152] XLA service 0x79f3b0004d90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338430.366144 3301395 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:27:10.405944: 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 00:00:1762338430.529069 3301395 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338432.625540 3301395 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:21[0m 3s/step - accuracy: 0.2656 - loss: 2.7050
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2623 - loss: 2.4903 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2722 - loss: 2.3604
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2761 - loss: 2.2780
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2808 - loss: 2.2038
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2815 - loss: 2.1930
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2816 - loss: 2.1915 - val_accuracy: 0.3945 - val_loss: 1.2620
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2656 - loss: 1.6428
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3015 - loss: 1.6204 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3116 - loss: 1.5801
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3146 - loss: 1.5550
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3162 - loss: 1.5366
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3163 - loss: 1.5355 - val_accuracy: 0.3982 - val_loss: 1.2660
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.3583
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3568 - loss: 1.3749 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.3750
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3438 - loss: 1.3735
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3418 - loss: 1.3723
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3416 - loss: 1.3721 - val_accuracy: 0.3850 - val_loss: 1.2704
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3457
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3323 - loss: 1.3542 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3372 - loss: 1.3490
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3419 - loss: 1.3454
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3445 - loss: 1.3425 - val_accuracy: 0.3906 - val_loss: 1.2639
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.3205
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.3203 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3678 - loss: 1.3180
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3675 - loss: 1.3164
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3661 - loss: 1.3159
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3661 - loss: 1.3159 - val_accuracy: 0.3913 - val_loss: 1.2461
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3594 - loss: 1.2937
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3789 - loss: 1.2959 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3832 - loss: 1.2919
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3843 - loss: 1.2903
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3845 - loss: 1.2892
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3843 - loss: 1.2890 - val_accuracy: 0.4001 - val_loss: 1.2304
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.2953
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3559 - loss: 1.2945 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3601 - loss: 1.2910
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3628 - loss: 1.2891
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3654 - loss: 1.2879 - val_accuracy: 0.4083 - val_loss: 1.2177
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1883
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3856 - loss: 1.2640 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3850 - loss: 1.2671
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3861 - loss: 1.2666
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3872 - loss: 1.2664 - val_accuracy: 0.3962 - val_loss: 1.2048
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3544
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3831 - loss: 1.2707 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3881 - loss: 1.2628
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3899 - loss: 1.2593
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3914 - loss: 1.2573
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3914 - loss: 1.2573 - val_accuracy: 0.4244 - val_loss: 1.1734
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2241
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4092 - loss: 1.2360 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4100 - loss: 1.2324
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4093 - loss: 1.2315
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4092 - loss: 1.2309
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4092 - loss: 1.2309 - val_accuracy: 0.4478 - val_loss: 1.1891
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2034
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4177 - loss: 1.2220 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4119 - loss: 1.2262
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4119 - loss: 1.2243
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4129 - loss: 1.2228 - val_accuracy: 0.4488 - val_loss: 1.1495
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.3132
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4273 - loss: 1.2314 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4219 - loss: 1.2277
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.2254
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4193 - loss: 1.2238
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4193 - loss: 1.2235 - val_accuracy: 0.4353 - val_loss: 1.1379
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2001
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3851 - loss: 1.2407 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3945 - loss: 1.2311
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4001 - loss: 1.2237
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4037 - loss: 1.2196
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4039 - loss: 1.2194 - val_accuracy: 0.4619 - val_loss: 1.1214
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1690
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4109 - loss: 1.1977 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4181 - loss: 1.1964
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.1975
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4224 - loss: 1.1965
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4225 - loss: 1.1964 - val_accuracy: 0.4714 - val_loss: 1.1134
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0624
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4312 - loss: 1.1939 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4340 - loss: 1.1924
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4363 - loss: 1.1899
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4373 - loss: 1.1892
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4374 - loss: 1.1892 - val_accuracy: 0.4924 - val_loss: 1.1179
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1645
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.1759 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.1793
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4423 - loss: 1.1800
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4417 - loss: 1.1805 - val_accuracy: 0.5033 - val_loss: 1.0933
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1309
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4283 - loss: 1.1790 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4331 - loss: 1.1769
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4352 - loss: 1.1756
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4361 - loss: 1.1750 - val_accuracy: 0.4803 - val_loss: 1.0993
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.1783
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.1743 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4521 - loss: 1.1703
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4515 - loss: 1.1698
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.1696
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4510 - loss: 1.1696 - val_accuracy: 0.5072 - val_loss: 1.0806
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4844 - loss: 1.1390
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4506 - loss: 1.1508 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.1571
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.1597
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4491 - loss: 1.1625
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4490 - loss: 1.1626 - val_accuracy: 0.4711 - val_loss: 1.0944
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3906 - loss: 1.1670
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4448 - loss: 1.1813 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1705
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.1677
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4562 - loss: 1.1661 - val_accuracy: 0.4806 - val_loss: 1.0827
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2116
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4392 - loss: 1.1749 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4427 - loss: 1.1663
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.1625
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4453 - loss: 1.1610 - val_accuracy: 0.4537 - val_loss: 1.1455
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0991
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4519 - loss: 1.1588 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4596 - loss: 1.1565
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4606 - loss: 1.1541
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4601 - loss: 1.1537
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4600 - loss: 1.1538 - val_accuracy: 0.4862 - val_loss: 1.0709
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2769
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1605 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1535
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4623 - loss: 1.1528
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4624 - loss: 1.1516 - val_accuracy: 0.4704 - val_loss: 1.1075
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2974
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4803 - loss: 1.1510 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4756 - loss: 1.1495
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1471
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1462
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4740 - loss: 1.1462 - val_accuracy: 0.5204 - val_loss: 1.0562
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.1600
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1532 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4699 - loss: 1.1473
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4721 - loss: 1.1406
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4724 - loss: 1.1383 - val_accuracy: 0.5269 - val_loss: 1.0267
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1771
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.1666 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4581 - loss: 1.1604
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4600 - loss: 1.1560
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4622 - loss: 1.1512 - val_accuracy: 0.4809 - val_loss: 1.1179
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2074
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1359 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1348
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1294
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4722 - loss: 1.1286 - val_accuracy: 0.5089 - val_loss: 1.0448
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0004
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1446 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1359
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4701 - loss: 1.1336
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1316
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4714 - loss: 1.1316 - val_accuracy: 0.5361 - val_loss: 1.0206
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0761
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1028 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4893 - loss: 1.1061
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4881 - loss: 1.1079
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1088
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4884 - loss: 1.1088 - val_accuracy: 0.5197 - val_loss: 1.0378
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1807
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1395 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4839 - loss: 1.1298
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1239
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4875 - loss: 1.1202 - val_accuracy: 0.5283 - val_loss: 1.0143
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1743
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.0861 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.0879
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.0907
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4953 - loss: 1.0938 - val_accuracy: 0.5138 - val_loss: 1.0432
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5625 - loss: 1.0231
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4875 - loss: 1.1124 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1131
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4915 - loss: 1.1099
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1078
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4941 - loss: 1.1076 - val_accuracy: 0.5476 - val_loss: 0.9879
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9509
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5053 - loss: 1.0853 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.0962
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4991 - loss: 1.0992
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.0995 - val_accuracy: 0.5384 - val_loss: 0.9934
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1602
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.0798 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.0907
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4965 - loss: 1.0930
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4977 - loss: 1.0936
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4977 - loss: 1.0935 - val_accuracy: 0.5516 - val_loss: 0.9536
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0636
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.1004 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.0960
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.0930
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.0917
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5013 - loss: 1.0914 - val_accuracy: 0.5089 - val_loss: 1.0184
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2722
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5162 - loss: 1.0848 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0848
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5148 - loss: 1.0868
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5130 - loss: 1.0875 - val_accuracy: 0.5434 - val_loss: 0.9724
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9620
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0524 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0624
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5175 - loss: 1.0660
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0687
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5167 - loss: 1.0691 - val_accuracy: 0.5509 - val_loss: 0.9604
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0596
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0628 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5260 - loss: 1.0655
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5247 - loss: 1.0664
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5235 - loss: 1.0675 - val_accuracy: 0.5191 - val_loss: 1.0155
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4531 - loss: 1.0864
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4940 - loss: 1.0828 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.0783
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5052 - loss: 1.0775
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5078 - loss: 1.0763 - val_accuracy: 0.5476 - val_loss: 0.9756

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 509ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 17: 54.95 [%]
F1-score capturado en la ejecución 17: 54.91 [%]

=== 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)
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:54[0m 705ms/step
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 759us/step  
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 701us/step
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 690us/step
[1m289/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 698us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m75/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 678us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 54.07 [%]
Global F1 score (validation) = 54.84 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.38385227 0.36749697 0.05998811 0.18866262]
 [0.30753598 0.26607302 0.19889426 0.2274968 ]
 [0.40525287 0.40859976 0.02986712 0.15628022]
 ...
 [0.06566735 0.04228664 0.85823107 0.03381493]
 [0.09119205 0.06200448 0.798195   0.04860848]
 [0.11100774 0.07772321 0.7502515  0.06101766]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.89 [%]
Global accuracy score (test) = 55.02 [%]
Global F1 score (train) = 59.49 [%]
Global F1 score (test) = 54.33 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.39      0.40       400
MODERATE-INTENSITY       0.46      0.48      0.47       400
         SEDENTARY       0.62      0.87      0.72       400
VIGOROUS-INTENSITY       0.81      0.45      0.58       345

          accuracy                           0.55      1545
         macro avg       0.58      0.55      0.54      1545
      weighted avg       0.57      0.55      0.54      1545

2025-11-05 11:27:38.953275: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:27:38.964855: 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 00:00:1762338458.978368 3305769 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338458.982758 3305769 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338458.992948 3305769 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338458.992967 3305769 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338458.992970 3305769 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338458.992972 3305769 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:27:38.996210: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338461.242918 3305769 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338462.633666 3305911 service.cc:152] XLA service 0x797f2c01aeb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338462.633695 3305911 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:27:42.667414: 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 00:00:1762338462.789494 3305911 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338464.902614 3305911 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:22[0m 3s/step - accuracy: 0.2969 - loss: 2.7248
[1m 31/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2463 - loss: 2.4556 
[1m 65/167[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2585 - loss: 2.3357
[1m106/167[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2652 - loss: 2.2570
[1m147/167[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2707 - loss: 2.1882
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2730 - loss: 2.1575
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2731 - loss: 2.1561 - val_accuracy: 0.3913 - val_loss: 1.2748
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.2500 - loss: 1.5858
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2965 - loss: 1.5566 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2972 - loss: 1.5370
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2989 - loss: 1.5206
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3005 - loss: 1.5070 - val_accuracy: 0.3837 - val_loss: 1.2752
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3594 - loss: 1.3535
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3279 - loss: 1.3755 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3318 - loss: 1.3715
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3328 - loss: 1.3701
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3336 - loss: 1.3682 - val_accuracy: 0.4041 - val_loss: 1.2605
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2344 - loss: 1.4290
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3259 - loss: 1.3627 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.3523
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3401 - loss: 1.3464
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3421 - loss: 1.3439 - val_accuracy: 0.4087 - val_loss: 1.2537
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.3951
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3585 - loss: 1.3155 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3620 - loss: 1.3125
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3615 - loss: 1.3132
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3612 - loss: 1.3136
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3612 - loss: 1.3134 - val_accuracy: 0.3965 - val_loss: 1.2411
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.2932
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3320 - loss: 1.3043 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3505 - loss: 1.2987
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3547 - loss: 1.2977
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3568 - loss: 1.2972
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3569 - loss: 1.2972 - val_accuracy: 0.3942 - val_loss: 1.2363
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.4375 - loss: 1.2035
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3742 - loss: 1.2959 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3741 - loss: 1.2925
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3749 - loss: 1.2912
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3753 - loss: 1.2905
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3754 - loss: 1.2904 - val_accuracy: 0.4031 - val_loss: 1.2184
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.3201
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3946 - loss: 1.2808 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3913 - loss: 1.2787
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3902 - loss: 1.2772
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3897 - loss: 1.2756
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3896 - loss: 1.2755 - val_accuracy: 0.4064 - val_loss: 1.2028
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2776
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3988 - loss: 1.2614 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3980 - loss: 1.2584
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3952 - loss: 1.2581
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3931 - loss: 1.2572
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3929 - loss: 1.2571 - val_accuracy: 0.4123 - val_loss: 1.1976
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1157
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3939 - loss: 1.2522 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3965 - loss: 1.2512
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3978 - loss: 1.2496
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3982 - loss: 1.2485 - val_accuracy: 0.4169 - val_loss: 1.1685
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1919
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4056 - loss: 1.2273 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4061 - loss: 1.2312
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4061 - loss: 1.2316
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4065 - loss: 1.2311 - val_accuracy: 0.4264 - val_loss: 1.1610
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4219 - loss: 1.1719
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4213 - loss: 1.2063 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4146 - loss: 1.2131
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4149 - loss: 1.2134
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4147 - loss: 1.2138 - val_accuracy: 0.4300 - val_loss: 1.1640
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.2714
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4074 - loss: 1.2104 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4132 - loss: 1.2069
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4172 - loss: 1.2040
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.2036
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4203 - loss: 1.2035 - val_accuracy: 0.4501 - val_loss: 1.1340
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.2726
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4390 - loss: 1.1869 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.1931
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4360 - loss: 1.1951
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4350 - loss: 1.1963 - val_accuracy: 0.4704 - val_loss: 1.1337
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2685
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.1971 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.1968
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.1973
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4283 - loss: 1.1970
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4283 - loss: 1.1970 - val_accuracy: 0.4790 - val_loss: 1.1279
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5000 - loss: 1.2321
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4407 - loss: 1.2101 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.1982
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.1934
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4475 - loss: 1.1915 - val_accuracy: 0.4737 - val_loss: 1.0947
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1669
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4490 - loss: 1.1730 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4468 - loss: 1.1725
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.1725
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4449 - loss: 1.1720 - val_accuracy: 0.4481 - val_loss: 1.1511
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2457
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4396 - loss: 1.1686 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4491 - loss: 1.1634
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.1623
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4525 - loss: 1.1624 - val_accuracy: 0.5099 - val_loss: 1.0804
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4062 - loss: 1.1885
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4288 - loss: 1.1719 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.1704
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4394 - loss: 1.1702
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4417 - loss: 1.1700
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4422 - loss: 1.1700 - val_accuracy: 0.5013 - val_loss: 1.0706
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2069
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1364 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1348
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4644 - loss: 1.1376
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4641 - loss: 1.1405
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4641 - loss: 1.1406 - val_accuracy: 0.5062 - val_loss: 1.0542
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0606
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1311 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4638 - loss: 1.1381
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1435
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4602 - loss: 1.1460 - val_accuracy: 0.5076 - val_loss: 1.0761
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.4531 - loss: 1.2376
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1468 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1433
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1421
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1418
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4782 - loss: 1.1418 - val_accuracy: 0.5066 - val_loss: 1.0807
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1495
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4833 - loss: 1.1508 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1422
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1397
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1381
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4861 - loss: 1.1380 - val_accuracy: 0.4895 - val_loss: 1.0839
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1303
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1342 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4831 - loss: 1.1356
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1359
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1358
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4824 - loss: 1.1358 - val_accuracy: 0.5398 - val_loss: 1.0244
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.1208
[1m 31/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4750 - loss: 1.1256 
[1m 70/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1239
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1210
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1201
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4883 - loss: 1.1198 - val_accuracy: 0.5384 - val_loss: 1.0120
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2649
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0995 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.0996
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.1046
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5011 - loss: 1.1071
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5009 - loss: 1.1072 - val_accuracy: 0.5450 - val_loss: 1.0177
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.1307
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1218 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1194
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4877 - loss: 1.1187
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1185
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4879 - loss: 1.1181 - val_accuracy: 0.5105 - val_loss: 1.0320
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.3542
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5003 - loss: 1.1167 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.1110
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1093
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.1076
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5048 - loss: 1.1076 - val_accuracy: 0.5575 - val_loss: 0.9796
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0647
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1034 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1004
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4945 - loss: 1.1004
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4955 - loss: 1.0999 - val_accuracy: 0.5598 - val_loss: 0.9736
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.6406 - loss: 0.9797
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0532 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.0630
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0686
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5123 - loss: 1.0728
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5122 - loss: 1.0731 - val_accuracy: 0.5509 - val_loss: 1.0006
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1036
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4926 - loss: 1.1010 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.0960
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.0939
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.0933
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4978 - loss: 1.0932 - val_accuracy: 0.5112 - val_loss: 1.0214
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0906
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5289 - loss: 1.0749 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0747
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0757
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5189 - loss: 1.0755 - val_accuracy: 0.5420 - val_loss: 0.9562
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 0.9948
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0470 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5171 - loss: 1.0504
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0533
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5194 - loss: 1.0548
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5195 - loss: 1.0552 - val_accuracy: 0.5627 - val_loss: 0.9521
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1329
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.0851 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0773
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0730
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0705
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5120 - loss: 1.0703 - val_accuracy: 0.5729 - val_loss: 0.9341
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0770
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0716 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5301 - loss: 1.0626
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0602
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5308 - loss: 1.0579 - val_accuracy: 0.5522 - val_loss: 0.9640
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0105
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0631 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0665
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0637
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5264 - loss: 1.0626 - val_accuracy: 0.5549 - val_loss: 0.9598
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.0949
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0800 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0681
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0617
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5270 - loss: 1.0590 - val_accuracy: 0.5779 - val_loss: 0.9419
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5312 - loss: 0.9576
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0397 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0386
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5321 - loss: 1.0375
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5340 - loss: 1.0378 - val_accuracy: 0.5542 - val_loss: 0.9925
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.2173
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0746 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0657
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0609
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5275 - loss: 1.0573 - val_accuracy: 0.5677 - val_loss: 0.9192
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6719 - loss: 0.8931
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0251 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0358
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0400
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5278 - loss: 1.0406
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5279 - loss: 1.0406 - val_accuracy: 0.5759 - val_loss: 0.9279
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5781 - loss: 0.9689
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5604 - loss: 1.0145 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 1.0156
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5479 - loss: 1.0194
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5450 - loss: 1.0234 - val_accuracy: 0.5746 - val_loss: 0.9198
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5156 - loss: 0.9941
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5182 - loss: 1.0283 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0345
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0350
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5319 - loss: 1.0357 - val_accuracy: 0.5624 - val_loss: 0.9478
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.5312 - loss: 1.0623
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0314 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0296
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5307 - loss: 1.0286
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0281
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5325 - loss: 1.0281 - val_accuracy: 0.5601 - val_loss: 0.9560
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0541
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0483 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0355
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0310
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0300
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5449 - loss: 1.0298 - val_accuracy: 0.5818 - val_loss: 0.9192

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 510ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 18: 55.02 [%]
F1-score capturado en la ejecución 18: 54.33 [%]

=== 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)
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:44[0m 675ms/step
[1m 61/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 834us/step  
[1m130/333[0m [32m━━━━━━━[0m[37m━━━━━━━━━━━━━[0m [1m0s[0m 776us/step
[1m209/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 724us/step
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 713us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m69/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 742us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 57.92 [%]
Global F1 score (validation) = 57.44 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.34028214 0.30183202 0.12983674 0.22804914]
 [0.32462272 0.27837604 0.16461988 0.23238133]
 [0.4348625  0.48383415 0.01589192 0.06541152]
 ...
 [0.03642856 0.02018748 0.92306745 0.02031646]
 [0.04254796 0.02407159 0.909163   0.02421746]
 [0.03542292 0.01955383 0.9253441  0.01967911]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 61.0 [%]
Global accuracy score (test) = 54.89 [%]
Global F1 score (train) = 60.64 [%]
Global F1 score (test) = 53.7 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.38      0.26      0.31       400
MODERATE-INTENSITY       0.47      0.57      0.51       400
         SEDENTARY       0.59      0.88      0.71       400
VIGOROUS-INTENSITY       0.90      0.47      0.62       345

          accuracy                           0.55      1545
         macro avg       0.58      0.55      0.54      1545
      weighted avg       0.57      0.55      0.53      1545

2025-11-05 11:28:12.849798: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:28:12.861140: 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 00:00:1762338492.874471 3310774 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338492.878738 3310774 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338492.888637 3310774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338492.888656 3310774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338492.888658 3310774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338492.888660 3310774 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:28:12.891872: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338495.129946 3310774 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338496.512474 3310907 service.cc:152] XLA service 0x75764c01c610 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338496.512555 3310907 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:28:16.548739: 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 00:00:1762338496.668071 3310907 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338498.780976 3310907 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:20[0m 3s/step - accuracy: 0.1719 - loss: 2.6446
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2517 - loss: 2.2878 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2667 - loss: 2.1788
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2739 - loss: 2.1115
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 2.0603
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 13ms/step - accuracy: 0.2784 - loss: 2.0520
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2785 - loss: 2.0509 - val_accuracy: 0.3985 - val_loss: 1.2585
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2500 - loss: 1.6292
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3171 - loss: 1.5415 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3135 - loss: 1.5312
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3124 - loss: 1.5172
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3127 - loss: 1.5048 - val_accuracy: 0.3906 - val_loss: 1.2657
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2188 - loss: 1.3914
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3215 - loss: 1.3909 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3269 - loss: 1.3801
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3295 - loss: 1.3736
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3314 - loss: 1.3691
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3315 - loss: 1.3689 - val_accuracy: 0.3959 - val_loss: 1.2610
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.3220
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.3276 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3410 - loss: 1.3255
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3439 - loss: 1.3243
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.3232
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3471 - loss: 1.3231 - val_accuracy: 0.3972 - val_loss: 1.2448
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3203
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3687 - loss: 1.3081 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3639 - loss: 1.3083
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3629 - loss: 1.3077
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3621 - loss: 1.3078
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3620 - loss: 1.3078 - val_accuracy: 0.3998 - val_loss: 1.2353
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.3305
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3568 - loss: 1.2970 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3613 - loss: 1.2928
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3658 - loss: 1.2895
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3685 - loss: 1.2881
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3687 - loss: 1.2880 - val_accuracy: 0.4011 - val_loss: 1.2185
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2515
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3783 - loss: 1.2742 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3776 - loss: 1.2748
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3761 - loss: 1.2755
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3763 - loss: 1.2748 - val_accuracy: 0.4044 - val_loss: 1.2079
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.3578
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3742 - loss: 1.2646 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3785 - loss: 1.2627
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3791 - loss: 1.2618
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3798 - loss: 1.2619 - val_accuracy: 0.4175 - val_loss: 1.1939
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1464
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3943 - loss: 1.2540 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3954 - loss: 1.2563
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3960 - loss: 1.2554
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3954 - loss: 1.2545
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3953 - loss: 1.2543 - val_accuracy: 0.4205 - val_loss: 1.1887
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.2050
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4177 - loss: 1.2396 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4109 - loss: 1.2389
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4073 - loss: 1.2385
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4048 - loss: 1.2381
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4047 - loss: 1.2381 - val_accuracy: 0.4136 - val_loss: 1.1837
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3438 - loss: 1.3108
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3975 - loss: 1.2216 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3977 - loss: 1.2196
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3995 - loss: 1.2197
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4009 - loss: 1.2202
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4009 - loss: 1.2202 - val_accuracy: 0.4432 - val_loss: 1.1515
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1131
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4177 - loss: 1.2249 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4123 - loss: 1.2286
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4118 - loss: 1.2263
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4118 - loss: 1.2246 - val_accuracy: 0.4777 - val_loss: 1.1373
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2606
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3936 - loss: 1.2221 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3995 - loss: 1.2155
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4040 - loss: 1.2121
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4063 - loss: 1.2110 - val_accuracy: 0.4438 - val_loss: 1.1475
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1956
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4066 - loss: 1.2041 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4118 - loss: 1.2005
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4127 - loss: 1.1996
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4133 - loss: 1.1983
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4133 - loss: 1.1983 - val_accuracy: 0.4688 - val_loss: 1.1312
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1518
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4327 - loss: 1.1852 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4273 - loss: 1.1907
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4264 - loss: 1.1904
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4264 - loss: 1.1893
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4264 - loss: 1.1893 - val_accuracy: 0.4330 - val_loss: 1.1390
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.1817
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.2055 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4288 - loss: 1.1980
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4298 - loss: 1.1968
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4292 - loss: 1.1961
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4292 - loss: 1.1960 - val_accuracy: 0.3127 - val_loss: 1.4028
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2656 - loss: 1.6611
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4049 - loss: 1.2369 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4126 - loss: 1.2165
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4154 - loss: 1.2081
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4176 - loss: 1.2031 - val_accuracy: 0.4777 - val_loss: 1.1178
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1492
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1657 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1714
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4584 - loss: 1.1739
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1755
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4536 - loss: 1.1756 - val_accuracy: 0.4846 - val_loss: 1.1018
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1071
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4360 - loss: 1.1634 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4332 - loss: 1.1707
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.1736
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4310 - loss: 1.1741 - val_accuracy: 0.4846 - val_loss: 1.0994
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2104
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4482 - loss: 1.1603 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4431 - loss: 1.1619
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4418 - loss: 1.1642
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4408 - loss: 1.1664 - val_accuracy: 0.4734 - val_loss: 1.0999
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.2231
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4348 - loss: 1.1616 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.1597
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4381 - loss: 1.1611
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.1635
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4389 - loss: 1.1637 - val_accuracy: 0.4520 - val_loss: 1.1121
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3281 - loss: 1.3006
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4246 - loss: 1.1942 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4372 - loss: 1.1802
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4426 - loss: 1.1741
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.1723
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4439 - loss: 1.1722 - val_accuracy: 0.4481 - val_loss: 1.0958
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0241
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1530 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4570 - loss: 1.1558
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4534 - loss: 1.1582
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4521 - loss: 1.1590
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4518 - loss: 1.1591 - val_accuracy: 0.4790 - val_loss: 1.0838
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0661
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4629 - loss: 1.1492 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4522 - loss: 1.1523
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4488 - loss: 1.1545
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4474 - loss: 1.1552
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4472 - loss: 1.1553 - val_accuracy: 0.4629 - val_loss: 1.0832
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.4844 - loss: 1.1449
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1620 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4533 - loss: 1.1608
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1597
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4504 - loss: 1.1590
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4502 - loss: 1.1590 - val_accuracy: 0.4639 - val_loss: 1.1618
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.3434
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4440 - loss: 1.1786 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.1645
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.1610
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4485 - loss: 1.1596 - val_accuracy: 0.4642 - val_loss: 1.0787
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1159
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.1381 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4508 - loss: 1.1413
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4505 - loss: 1.1432
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4514 - loss: 1.1434
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4515 - loss: 1.1436 - val_accuracy: 0.4928 - val_loss: 1.0827
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1784
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4528 - loss: 1.1617 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1528
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1508
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1502
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4596 - loss: 1.1501 - val_accuracy: 0.4747 - val_loss: 1.0691
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1057
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1170 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4636 - loss: 1.1323
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4606 - loss: 1.1366
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4589 - loss: 1.1379 - val_accuracy: 0.5046 - val_loss: 1.0589
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.1107
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4623 - loss: 1.1593 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4594 - loss: 1.1546
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4575 - loss: 1.1511
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4570 - loss: 1.1495 - val_accuracy: 0.4862 - val_loss: 1.0566
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1950
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4330 - loss: 1.1676 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.1575
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4474 - loss: 1.1509
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4501 - loss: 1.1470 - val_accuracy: 0.4721 - val_loss: 1.1241
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3750 - loss: 1.3526
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4548 - loss: 1.1662 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1496
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1460
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4612 - loss: 1.1438 - val_accuracy: 0.4852 - val_loss: 1.0699
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0481
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4674 - loss: 1.1361 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4663 - loss: 1.1383
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4660 - loss: 1.1382
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4654 - loss: 1.1382
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4654 - loss: 1.1381 - val_accuracy: 0.4777 - val_loss: 1.0714
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.0368
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1215 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4698 - loss: 1.1297
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4679 - loss: 1.1315
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4670 - loss: 1.1323 - val_accuracy: 0.4961 - val_loss: 1.0730
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.1964
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1381 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1341
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4643 - loss: 1.1320
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4652 - loss: 1.1308
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4652 - loss: 1.1308 - val_accuracy: 0.5085 - val_loss: 1.0373
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.1145
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1237 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4653 - loss: 1.1208
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4673 - loss: 1.1204
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4684 - loss: 1.1210
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4685 - loss: 1.1212 - val_accuracy: 0.5089 - val_loss: 1.0421
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0892
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.1181 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4622 - loss: 1.1161
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4649 - loss: 1.1162
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4673 - loss: 1.1152 - val_accuracy: 0.4957 - val_loss: 1.0594
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1985
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4572 - loss: 1.1421 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4637 - loss: 1.1365
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4667 - loss: 1.1328
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1304
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4683 - loss: 1.1301 - val_accuracy: 0.5237 - val_loss: 1.1035
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.3451
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4562 - loss: 1.1443 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1381
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4633 - loss: 1.1343
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4652 - loss: 1.1320 - val_accuracy: 0.5338 - val_loss: 1.0323
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0490
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5050 - loss: 1.0924 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1035
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1061
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4895 - loss: 1.1070 - val_accuracy: 0.5233 - val_loss: 1.0245
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2202
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1224 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1126
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1109
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4719 - loss: 1.1105 - val_accuracy: 0.5187 - val_loss: 1.0370
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0558
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1145 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4863 - loss: 1.1173
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4860 - loss: 1.1165
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4861 - loss: 1.1164 - val_accuracy: 0.5345 - val_loss: 1.0272
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 30ms/step - accuracy: 0.5625 - loss: 1.0782
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.0894 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.0944
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4778 - loss: 1.0990
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1021
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4782 - loss: 1.1024 - val_accuracy: 0.5227 - val_loss: 1.0208
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6562 - loss: 0.9177
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5061 - loss: 1.0983 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.1008
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1013
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4954 - loss: 1.1018
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4952 - loss: 1.1019 - val_accuracy: 0.5585 - val_loss: 1.0074
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4844 - loss: 1.0127
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5044 - loss: 1.0967 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.0980
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4941 - loss: 1.0994
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4930 - loss: 1.0999 - val_accuracy: 0.5375 - val_loss: 1.0044
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.2033
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.1029 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5026 - loss: 1.0990
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.0989
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5015 - loss: 1.0991
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5015 - loss: 1.0991 - val_accuracy: 0.4888 - val_loss: 1.0840
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.2301
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1040 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.0971
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4898 - loss: 1.0962
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4893 - loss: 1.0966 - val_accuracy: 0.5509 - val_loss: 1.0333
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1105
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.1030 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.0999
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4924 - loss: 1.0966
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4929 - loss: 1.0957
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4931 - loss: 1.0957 - val_accuracy: 0.5526 - val_loss: 0.9976
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5625 - loss: 0.9661
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0549 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5117 - loss: 1.0640
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0689
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0718
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5098 - loss: 1.0718 - val_accuracy: 0.5575 - val_loss: 1.0352
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1605
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4878 - loss: 1.1061 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1035
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4896 - loss: 1.1008
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4912 - loss: 1.0986
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4914 - loss: 1.0983 - val_accuracy: 0.5108 - val_loss: 1.0764
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.2925
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.1043 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1032
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4937 - loss: 1.1019
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4928 - loss: 1.1003 - val_accuracy: 0.5407 - val_loss: 0.9964
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0631
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5091 - loss: 1.0787 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.0776
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5080 - loss: 1.0779
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5074 - loss: 1.0788
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5074 - loss: 1.0789 - val_accuracy: 0.5427 - val_loss: 1.0092
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.5156 - loss: 1.0911
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4916 - loss: 1.0976 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.0912
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.0870
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4994 - loss: 1.0846
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4994 - loss: 1.0846 - val_accuracy: 0.5358 - val_loss: 0.9881
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0330
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5239 - loss: 1.0490 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5164 - loss: 1.0601
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0663
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5098 - loss: 1.0699 - val_accuracy: 0.5420 - val_loss: 0.9756
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0674
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4941 - loss: 1.0996 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5006 - loss: 1.0865
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5034 - loss: 1.0838
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5046 - loss: 1.0820 - val_accuracy: 0.5759 - val_loss: 0.9642
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1277
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0606 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0615
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5226 - loss: 1.0631
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0643
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5220 - loss: 1.0643 - val_accuracy: 0.5841 - val_loss: 0.9481
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5000 - loss: 1.1155
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0880 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5136 - loss: 1.0811
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5145 - loss: 1.0752
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0724
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5149 - loss: 1.0723 - val_accuracy: 0.5529 - val_loss: 1.0217
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 1.0469
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0549 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5344 - loss: 1.0494
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0487
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5317 - loss: 1.0496 - val_accuracy: 0.5604 - val_loss: 0.9989
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4844 - loss: 1.2154
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5248 - loss: 1.0847 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0754
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5208 - loss: 1.0730
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0695
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5210 - loss: 1.0693 - val_accuracy: 0.5614 - val_loss: 0.9589
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5938 - loss: 0.9890
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0703 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5207 - loss: 1.0624
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.0610
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5253 - loss: 1.0601
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5254 - loss: 1.0600 - val_accuracy: 0.5907 - val_loss: 0.9538
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0907
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5089 - loss: 1.0747 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.0747
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5056 - loss: 1.0698
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5070 - loss: 1.0657
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5073 - loss: 1.0654 - val_accuracy: 0.5867 - val_loss: 0.9218
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 0.9299
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0241 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0383
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0426
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0439
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5297 - loss: 1.0440 - val_accuracy: 0.5664 - val_loss: 0.9460
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0157
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0556 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0513
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0484
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5302 - loss: 1.0478 - val_accuracy: 0.5677 - val_loss: 0.9428
Epoch 64/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 1.0105
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0305 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0250
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0250
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5417 - loss: 1.0260
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5416 - loss: 1.0261 - val_accuracy: 0.5463 - val_loss: 0.9609
Epoch 65/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1558
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5337 - loss: 1.0628 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0475
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5407 - loss: 1.0421
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0399
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5406 - loss: 1.0398 - val_accuracy: 0.5752 - val_loss: 0.9240
Epoch 66/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 1.0147
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5470 - loss: 1.0324 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0344
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0345
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0348
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5384 - loss: 1.0348 - val_accuracy: 0.5805 - val_loss: 0.9189
Epoch 67/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1667
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0435 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0338
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5447 - loss: 1.0325
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0314
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5443 - loss: 1.0313 - val_accuracy: 0.5680 - val_loss: 0.9422
Epoch 68/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1033
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0587 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0499
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0456
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5282 - loss: 1.0416 - val_accuracy: 0.5552 - val_loss: 0.9552
Epoch 69/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.1465
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5502 - loss: 1.0268 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5511 - loss: 1.0247
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5504 - loss: 1.0243
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0239
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5493 - loss: 1.0239 - val_accuracy: 0.5851 - val_loss: 0.8963
Epoch 70/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9819
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5530 - loss: 1.0273 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5479 - loss: 1.0283
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0272
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5466 - loss: 1.0258
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5466 - loss: 1.0258 - val_accuracy: 0.5834 - val_loss: 0.9214
Epoch 71/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.1273
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5501 - loss: 1.0152 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5533 - loss: 1.0073
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5509 - loss: 1.0095
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5503 - loss: 1.0102 - val_accuracy: 0.5795 - val_loss: 0.9078
Epoch 72/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9183
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0259 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5462 - loss: 1.0208
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0191
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5500 - loss: 1.0177 - val_accuracy: 0.5667 - val_loss: 0.9006
Epoch 73/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5938 - loss: 1.0745
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5648 - loss: 1.0032 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5604 - loss: 1.0024
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5575 - loss: 1.0044
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5549 - loss: 1.0081
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5546 - loss: 1.0086 - val_accuracy: 0.5923 - val_loss: 0.9002
Epoch 74/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1345
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0067 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5460 - loss: 1.0026
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0026
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5491 - loss: 1.0031 - val_accuracy: 0.5861 - val_loss: 0.8918
Epoch 75/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1593
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5344 - loss: 1.0185 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5446 - loss: 1.0105
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0105
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0123
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5483 - loss: 1.0124 - val_accuracy: 0.5963 - val_loss: 0.8755
Epoch 76/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1264
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0164 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0130
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5393 - loss: 1.0120
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5413 - loss: 1.0111 - val_accuracy: 0.5949 - val_loss: 0.8985
Epoch 77/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.9397
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5619 - loss: 0.9812 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5627 - loss: 0.9850
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5625 - loss: 0.9874
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5614 - loss: 0.9903
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5613 - loss: 0.9905 - val_accuracy: 0.5943 - val_loss: 0.8703
Epoch 78/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0221
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0190 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5501 - loss: 1.0125
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5523 - loss: 1.0104
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5535 - loss: 1.0079 - val_accuracy: 0.6015 - val_loss: 0.8642
Epoch 79/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9849
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 0.9895 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 0.9907
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 0.9911
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5494 - loss: 0.9922
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5495 - loss: 0.9922 - val_accuracy: 0.6018 - val_loss: 0.8605
Epoch 80/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.9751
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 1.0133 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 1.0145
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 1.0112
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5548 - loss: 1.0076
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5548 - loss: 1.0075 - val_accuracy: 0.5871 - val_loss: 0.8928
Epoch 81/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5469 - loss: 1.0452
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5638 - loss: 0.9829 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5628 - loss: 0.9901
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5625 - loss: 0.9920
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5617 - loss: 0.9935
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5617 - loss: 0.9935 - val_accuracy: 0.5982 - val_loss: 0.8755
Epoch 82/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 0.9641
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5761 - loss: 0.9713 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5702 - loss: 0.9865
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5681 - loss: 0.9928
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5668 - loss: 0.9953 - val_accuracy: 0.5963 - val_loss: 0.8855
Epoch 83/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 0.9101
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5784 - loss: 0.9775 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5726 - loss: 0.9850
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5700 - loss: 0.9869
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5681 - loss: 0.9890 - val_accuracy: 0.5857 - val_loss: 0.8873
Epoch 84/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0204
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5540 - loss: 0.9993 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5570 - loss: 0.9897
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5599 - loss: 0.9849
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5608 - loss: 0.9830 - val_accuracy: 0.6035 - val_loss: 0.8728

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 504ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 19: 54.89 [%]
F1-score capturado en la ejecución 19: 53.7 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- 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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:38[0m 657ms/step
[1m 69/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 742us/step  
[1m142/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 712us/step
[1m219/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 693us/step
[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 712us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m74/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 691us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 59.56 [%]
Global F1 score (validation) = 59.56 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.36746433 0.41043395 0.01500992 0.20709178]
 [0.3104121  0.263671   0.18337642 0.24254057]
 [0.4487334  0.5060112  0.00439174 0.04086365]
 ...
 [0.10953704 0.07132364 0.7551715  0.0639678 ]
 [0.13410129 0.08989919 0.6963513  0.07964828]
 [0.12821972 0.08542269 0.71017903 0.07617862]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 63.58 [%]
Global accuracy score (test) = 58.32 [%]
Global F1 score (train) = 63.41 [%]
Global F1 score (test) = 58.56 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.39      0.41       400
MODERATE-INTENSITY       0.47      0.49      0.48       400
         SEDENTARY       0.64      0.85      0.73       400
VIGOROUS-INTENSITY       0.91      0.60      0.72       345

          accuracy                           0.58      1545
         macro avg       0.61      0.58      0.59      1545
      weighted avg       0.60      0.58      0.58      1545

2025-11-05 11:28:59.953613: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:28:59.965187: 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 00:00:1762338539.978630 3319416 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338539.983021 3319416 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338539.992815 3319416 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338539.992836 3319416 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338539.992838 3319416 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338539.992839 3319416 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:28:59.996010: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338542.284080 3319416 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338543.672462 3319551 service.cc:152] XLA service 0x776c7400aa00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338543.672533 3319551 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:29:03.719464: 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 00:00:1762338543.841565 3319551 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338545.933144 3319551 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:22[0m 3s/step - accuracy: 0.2969 - loss: 2.7072
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2871 - loss: 2.4018 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2885 - loss: 2.3154
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2889 - loss: 2.2365
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2894 - loss: 2.1791
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2895 - loss: 2.1711
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2896 - loss: 2.1697 - val_accuracy: 0.3830 - val_loss: 1.2586
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.4832
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3192 - loss: 1.5915 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.5669
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3196 - loss: 1.5474
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3185 - loss: 1.5338 - val_accuracy: 0.3821 - val_loss: 1.2693
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3681
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3163 - loss: 1.3867 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3230 - loss: 1.3810
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3255 - loss: 1.3783
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3277 - loss: 1.3749
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3278 - loss: 1.3746 - val_accuracy: 0.3890 - val_loss: 1.2625
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.3136
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.3255 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3601 - loss: 1.3295
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.3302
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3575 - loss: 1.3303
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3574 - loss: 1.3302 - val_accuracy: 0.3945 - val_loss: 1.2537
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.3305
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3456 - loss: 1.3214 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3523 - loss: 1.3192
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3553 - loss: 1.3183
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3572 - loss: 1.3174 - val_accuracy: 0.4028 - val_loss: 1.2358
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.2656 - loss: 1.2558
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.2985 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3608 - loss: 1.2967
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3646 - loss: 1.2958
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3671 - loss: 1.2946 - val_accuracy: 0.4037 - val_loss: 1.2267
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.3038
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3972 - loss: 1.2653 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3909 - loss: 1.2710
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.2725
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3853 - loss: 1.2728 - val_accuracy: 0.4070 - val_loss: 1.2034
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3594 - loss: 1.2633
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3969 - loss: 1.2498 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3920 - loss: 1.2498
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3910 - loss: 1.2507
[1m153/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3899 - loss: 1.2521
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3896 - loss: 1.2527 - val_accuracy: 0.4146 - val_loss: 1.1975
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.3326
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3947 - loss: 1.2678 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3913 - loss: 1.2621
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3909 - loss: 1.2586
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3920 - loss: 1.2550 - val_accuracy: 0.4205 - val_loss: 1.1783
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2560
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3969 - loss: 1.2492 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3979 - loss: 1.2451
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4006 - loss: 1.2411
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4010 - loss: 1.2396 - val_accuracy: 0.4619 - val_loss: 1.1643
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 1.1869
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4110 - loss: 1.1945 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4108 - loss: 1.2036
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4114 - loss: 1.2063
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4117 - loss: 1.2074 - val_accuracy: 0.4468 - val_loss: 1.1409
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0958
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4465 - loss: 1.1875 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4393 - loss: 1.1946
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4350 - loss: 1.1978
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4319 - loss: 1.2007
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4317 - loss: 1.2009 - val_accuracy: 0.4573 - val_loss: 1.1342
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2100
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3937 - loss: 1.2118 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3983 - loss: 1.2135
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4022 - loss: 1.2131
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4058 - loss: 1.2108
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4060 - loss: 1.2107 - val_accuracy: 0.4557 - val_loss: 1.1344
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.4531 - loss: 1.1877
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4224 - loss: 1.1874 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4218 - loss: 1.1908
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4219 - loss: 1.1929
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4225 - loss: 1.1933
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4226 - loss: 1.1933 - val_accuracy: 0.4435 - val_loss: 1.1467
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.3202
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4185 - loss: 1.1974 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4173 - loss: 1.1944
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4186 - loss: 1.1920
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4198 - loss: 1.1905
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4199 - loss: 1.1904 - val_accuracy: 0.4625 - val_loss: 1.0988
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2100
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4559 - loss: 1.1635 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4450 - loss: 1.1691
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4388 - loss: 1.1723
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4362 - loss: 1.1742
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4359 - loss: 1.1743 - val_accuracy: 0.4570 - val_loss: 1.1156
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3906 - loss: 1.1861
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4309 - loss: 1.1758 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4389 - loss: 1.1726
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4403 - loss: 1.1717
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.1720
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4398 - loss: 1.1722 - val_accuracy: 0.4589 - val_loss: 1.1045
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3750 - loss: 1.1183
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4301 - loss: 1.1603 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4374 - loss: 1.1650
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4392 - loss: 1.1669
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4405 - loss: 1.1666
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4407 - loss: 1.1666 - val_accuracy: 0.4688 - val_loss: 1.0977
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1986
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.1621 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4435 - loss: 1.1580
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.1585
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.1605
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4432 - loss: 1.1605 - val_accuracy: 0.4570 - val_loss: 1.0991
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.2710
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4205 - loss: 1.1932 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4283 - loss: 1.1823
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4341 - loss: 1.1751
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4370 - loss: 1.1723
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4373 - loss: 1.1720 - val_accuracy: 0.4711 - val_loss: 1.0939
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1011
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.1442 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.1438
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4488 - loss: 1.1454
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4479 - loss: 1.1479 - val_accuracy: 0.4839 - val_loss: 1.0940
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0629
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4477 - loss: 1.1597 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.1581
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4532 - loss: 1.1567
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1555
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4538 - loss: 1.1555 - val_accuracy: 0.4428 - val_loss: 1.1400
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1552
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4507 - loss: 1.1460 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4549 - loss: 1.1466
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1455
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4581 - loss: 1.1454 - val_accuracy: 0.4757 - val_loss: 1.0792
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0441
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4553 - loss: 1.1262 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4546 - loss: 1.1343
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1360
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1367
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4565 - loss: 1.1368 - val_accuracy: 0.4642 - val_loss: 1.1040
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2217
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4436 - loss: 1.1590 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4521 - loss: 1.1502
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1464
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4571 - loss: 1.1463
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4571 - loss: 1.1463 - val_accuracy: 0.5043 - val_loss: 1.0665
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5156 - loss: 1.0909
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4672 - loss: 1.1451 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1390
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1383
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4680 - loss: 1.1382 - val_accuracy: 0.4557 - val_loss: 1.1065
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.0254
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1190 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4645 - loss: 1.1252
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4665 - loss: 1.1285
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4672 - loss: 1.1299 - val_accuracy: 0.5030 - val_loss: 1.0618
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1088
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.1138 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1195
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4722 - loss: 1.1218
[1m156/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4702 - loss: 1.1233
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4699 - loss: 1.1235 - val_accuracy: 0.5112 - val_loss: 1.0536
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1359
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4578 - loss: 1.1386 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1351
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1316
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4657 - loss: 1.1299 - val_accuracy: 0.4714 - val_loss: 1.1161
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0649
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1268 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1194
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4779 - loss: 1.1191
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1188
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4772 - loss: 1.1186 - val_accuracy: 0.5184 - val_loss: 1.0597
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.4528
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4693 - loss: 1.1582 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1420
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1339
[1m151/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1298
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4754 - loss: 1.1280 - val_accuracy: 0.5204 - val_loss: 1.0463
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9100
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1025 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1020
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4802 - loss: 1.1046
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1064
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4795 - loss: 1.1065 - val_accuracy: 0.5191 - val_loss: 1.0559
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.1858
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4790 - loss: 1.0989 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1002
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4830 - loss: 1.0994
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4841 - loss: 1.0995 - val_accuracy: 0.5338 - val_loss: 1.0323
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.1310
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1090 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4735 - loss: 1.1065
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1071
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4791 - loss: 1.1072 - val_accuracy: 0.5187 - val_loss: 1.0522
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5469 - loss: 1.1572
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5048 - loss: 1.0986 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.1018
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.1020
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4946 - loss: 1.1014
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4946 - loss: 1.1014 - val_accuracy: 0.5368 - val_loss: 1.0371
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0596
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0969 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0963
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0965
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5056 - loss: 1.0966 - val_accuracy: 0.5223 - val_loss: 1.0273
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9985
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4996 - loss: 1.0950 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.0948
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4968 - loss: 1.0969
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.0961
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4976 - loss: 1.0958 - val_accuracy: 0.5273 - val_loss: 1.0153
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1611
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.0983 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.0946
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.0905
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.0889
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5036 - loss: 1.0889 - val_accuracy: 0.5578 - val_loss: 0.9946
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 1.2351
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.0773 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4980 - loss: 1.0772
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.0784
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4999 - loss: 1.0795
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5001 - loss: 1.0795 - val_accuracy: 0.5506 - val_loss: 0.9920
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1057
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4944 - loss: 1.0897 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.0820
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5028 - loss: 1.0809
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.0812
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5044 - loss: 1.0813 - val_accuracy: 0.4629 - val_loss: 1.1379
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2978
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0711 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5186 - loss: 1.0704
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.0725
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5141 - loss: 1.0732 - val_accuracy: 0.5365 - val_loss: 1.0158
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1503
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0695 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0640
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0649
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5174 - loss: 1.0660
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.0660 - val_accuracy: 0.5401 - val_loss: 0.9920
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9860
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0518 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5151 - loss: 1.0616
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5147 - loss: 1.0652
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5145 - loss: 1.0676 - val_accuracy: 0.5562 - val_loss: 0.9793
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 0.9991
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0576 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5126 - loss: 1.0609
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0624
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5145 - loss: 1.0625 - val_accuracy: 0.5581 - val_loss: 0.9621
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9603
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0471 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5163 - loss: 1.0534
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5178 - loss: 1.0561
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5188 - loss: 1.0573
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5188 - loss: 1.0574 - val_accuracy: 0.5223 - val_loss: 1.0242
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0818
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0572 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0570
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5243 - loss: 1.0577
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0580
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5227 - loss: 1.0581 - val_accuracy: 0.5621 - val_loss: 0.9605
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9217
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5348 - loss: 1.0270 
[1m 76/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5336 - loss: 1.0348
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5310 - loss: 1.0395
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5299 - loss: 1.0430
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5296 - loss: 1.0437 - val_accuracy: 0.5677 - val_loss: 0.9689
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4688 - loss: 1.0785
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0588 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0507
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5276 - loss: 1.0476
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5284 - loss: 1.0461 - val_accuracy: 0.5749 - val_loss: 0.9684
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1070
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0557 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0552
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0498
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5273 - loss: 1.0471
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5275 - loss: 1.0469 - val_accuracy: 0.5650 - val_loss: 0.9690
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9741
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5518 - loss: 1.0316 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0368
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0396
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5383 - loss: 1.0407
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5380 - loss: 1.0407 - val_accuracy: 0.5861 - val_loss: 0.9225
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4531 - loss: 1.1604
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5257 - loss: 1.0515 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0462
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5306 - loss: 1.0445
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0437
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5309 - loss: 1.0436 - val_accuracy: 0.5539 - val_loss: 0.9562
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5781 - loss: 0.9029
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5487 - loss: 1.0186 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5443 - loss: 1.0228
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5410 - loss: 1.0264
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0273
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5395 - loss: 1.0274 - val_accuracy: 0.5627 - val_loss: 0.9473
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 1.0228
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5531 - loss: 1.0071 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0191
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5332 - loss: 1.0264
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5313 - loss: 1.0287 - val_accuracy: 0.5322 - val_loss: 0.9946
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.3220
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5292 - loss: 1.0466 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0302
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0292
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5370 - loss: 1.0279 - val_accuracy: 0.5923 - val_loss: 0.9228
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4688 - loss: 1.0409
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5315 - loss: 1.0269 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0225
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5365 - loss: 1.0196
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5369 - loss: 1.0197
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5371 - loss: 1.0196 - val_accuracy: 0.5943 - val_loss: 0.9172
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9379
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0198 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5334 - loss: 1.0184
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5355 - loss: 1.0210
[1m155/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5368 - loss: 1.0225
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5371 - loss: 1.0228 - val_accuracy: 0.5943 - val_loss: 0.9153
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0659
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5448 - loss: 1.0202 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5424 - loss: 1.0131
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5409 - loss: 1.0128
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0147
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5406 - loss: 1.0148 - val_accuracy: 0.5693 - val_loss: 0.9252
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2137
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5546 - loss: 1.0295 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 1.0237
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 1.0236
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5478 - loss: 1.0213 - val_accuracy: 0.5907 - val_loss: 0.9121
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1469
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5544 - loss: 1.0244 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5451 - loss: 1.0251
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0246
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0234
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5399 - loss: 1.0234 - val_accuracy: 0.5946 - val_loss: 0.9042
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 0.8437
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 0.9994 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0021
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0021
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5438 - loss: 1.0024 - val_accuracy: 0.5966 - val_loss: 0.9267
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5469 - loss: 1.0334
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5576 - loss: 1.0042 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 0.9980
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5569 - loss: 0.9961
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5560 - loss: 0.9970 - val_accuracy: 0.5742 - val_loss: 0.9307
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1121
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0015 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5340 - loss: 0.9997
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5382 - loss: 1.0000
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5405 - loss: 0.9997 - val_accuracy: 0.6061 - val_loss: 0.9028
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0313
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5565 - loss: 1.0074 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5567 - loss: 1.0009
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5561 - loss: 0.9991
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5551 - loss: 0.9990 - val_accuracy: 0.6025 - val_loss: 0.8984
Epoch 64/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.2247
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5610 - loss: 1.0208 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5572 - loss: 1.0109
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5558 - loss: 1.0075
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5558 - loss: 1.0053 - val_accuracy: 0.5690 - val_loss: 0.9236
Epoch 65/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9238
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5479 - loss: 0.9961 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 0.9970
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 0.9977
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5576 - loss: 0.9978 - val_accuracy: 0.6041 - val_loss: 0.8817
Epoch 66/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0407
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 0.9928 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5567 - loss: 0.9936
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 0.9927
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5556 - loss: 0.9920
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5555 - loss: 0.9921 - val_accuracy: 0.5907 - val_loss: 0.9119
Epoch 67/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.6250 - loss: 0.9852
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5751 - loss: 0.9809 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5674 - loss: 0.9852
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5642 - loss: 0.9858
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5628 - loss: 0.9860 - val_accuracy: 0.6068 - val_loss: 0.8929
Epoch 68/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 1.0811
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 0.9909 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5569 - loss: 0.9865
[1m115/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5561 - loss: 0.9864
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5546 - loss: 0.9891
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5544 - loss: 0.9894 - val_accuracy: 0.6028 - val_loss: 0.8789
Epoch 69/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 0.8926
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5423 - loss: 0.9984 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5486 - loss: 0.9875
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5520 - loss: 0.9858
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5548 - loss: 0.9862
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5551 - loss: 0.9862 - val_accuracy: 0.5969 - val_loss: 0.9002
Epoch 70/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6094 - loss: 0.8725
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5635 - loss: 0.9710 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5663 - loss: 0.9697
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5657 - loss: 0.9711
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5643 - loss: 0.9731 - val_accuracy: 0.5992 - val_loss: 0.8798
Epoch 71/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9092
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0132 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5472 - loss: 1.0037
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5510 - loss: 0.9987
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5534 - loss: 0.9951
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5537 - loss: 0.9945 - val_accuracy: 0.6183 - val_loss: 0.8622
Epoch 72/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5781 - loss: 0.9173
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5613 - loss: 0.9726 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5583 - loss: 0.9743
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 0.9751
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5596 - loss: 0.9749
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5596 - loss: 0.9749 - val_accuracy: 0.6038 - val_loss: 0.8789
Epoch 73/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 0.9084
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5722 - loss: 0.9570 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5697 - loss: 0.9612
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5676 - loss: 0.9642
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5668 - loss: 0.9659 - val_accuracy: 0.5897 - val_loss: 0.8773
Epoch 74/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9853
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5745 - loss: 0.9564 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5702 - loss: 0.9602
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5669 - loss: 0.9651
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5643 - loss: 0.9682
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5640 - loss: 0.9689 - val_accuracy: 0.5772 - val_loss: 0.9102
Epoch 75/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5000 - loss: 1.0615
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5591 - loss: 0.9661 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5631 - loss: 0.9657
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5641 - loss: 0.9670
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5643 - loss: 0.9685
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5643 - loss: 0.9687 - val_accuracy: 0.6015 - val_loss: 0.8912
Epoch 76/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5625 - loss: 1.1434
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5569 - loss: 1.0148 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5644 - loss: 0.9970
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5669 - loss: 0.9896
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5676 - loss: 0.9852 - val_accuracy: 0.6078 - val_loss: 0.8683

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 502ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 20: 58.32 [%]
F1-score capturado en la ejecución 20: 58.56 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:48[0m 689ms/step
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 750us/step  
[1m136/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 744us/step
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 719us/step
[1m283/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 719us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m71/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 720us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 60.48 [%]
Global F1 score (validation) = 58.7 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.33548018 0.30317658 0.07742368 0.28391954]
 [0.33440414 0.34818035 0.02383872 0.29357684]
 [0.33629522 0.29282957 0.09830401 0.2725712 ]
 ...
 [0.05542613 0.03012605 0.8822678  0.03218004]
 [0.03410225 0.01744132 0.9297967  0.01865968]
 [0.09252947 0.05399077 0.7962128  0.05726691]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 64.0 [%]
Global accuracy score (test) = 56.57 [%]
Global F1 score (train) = 62.14 [%]
Global F1 score (test) = 55.49 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.43      0.24      0.31       400
MODERATE-INTENSITY       0.49      0.57      0.53       400
         SEDENTARY       0.56      0.85      0.68       400
VIGOROUS-INTENSITY       0.86      0.60      0.71       345

          accuracy                           0.57      1545
         macro avg       0.58      0.57      0.55      1545
      weighted avg       0.57      0.57      0.55      1545

2025-11-05 11:29:44.578016: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:29:44.589335: 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 00:00:1762338584.603042 3327390 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338584.607348 3327390 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338584.617305 3327390 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338584.617325 3327390 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338584.617327 3327390 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338584.617329 3327390 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:29:44.620583: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338586.884101 3327390 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338588.287891 3327528 service.cc:152] XLA service 0x7cecb401ba10 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338588.287922 3327528 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:29:48.321316: 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 00:00:1762338588.445160 3327528 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338590.575794 3327528 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:26[0m 3s/step - accuracy: 0.2812 - loss: 2.6652
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2519 - loss: 2.4324 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2620 - loss: 2.3415
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2663 - loss: 2.2664
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2696 - loss: 2.2081
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2702 - loss: 2.1972
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2702 - loss: 2.1959 - val_accuracy: 0.3545 - val_loss: 1.2730
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2656 - loss: 1.6569
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3064 - loss: 1.6061 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3060 - loss: 1.5841
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3068 - loss: 1.5649
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3080 - loss: 1.5507
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3082 - loss: 1.5485 - val_accuracy: 0.3732 - val_loss: 1.2891
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.3762
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3051 - loss: 1.4054 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3121 - loss: 1.3957
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3171 - loss: 1.3896
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3198 - loss: 1.3856
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3199 - loss: 1.3854 - val_accuracy: 0.3798 - val_loss: 1.2768
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2697
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3351 - loss: 1.3423 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3340 - loss: 1.3416
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3379 - loss: 1.3402
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3403 - loss: 1.3391 - val_accuracy: 0.3840 - val_loss: 1.2681
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.3991
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3588 - loss: 1.3242 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3594 - loss: 1.3216
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3599 - loss: 1.3207
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3607 - loss: 1.3196
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3607 - loss: 1.3195 - val_accuracy: 0.3876 - val_loss: 1.2518
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.3230
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3623 - loss: 1.3264 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3605 - loss: 1.3238
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3623 - loss: 1.3199
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3636 - loss: 1.3163
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3636 - loss: 1.3162 - val_accuracy: 0.3913 - val_loss: 1.2415
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.4130
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3694 - loss: 1.3021 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3744 - loss: 1.2940
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3763 - loss: 1.2915
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3777 - loss: 1.2900 - val_accuracy: 0.3909 - val_loss: 1.2260
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2001
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3802 - loss: 1.2768 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3817 - loss: 1.2766
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3829 - loss: 1.2744
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3833 - loss: 1.2725 - val_accuracy: 0.4139 - val_loss: 1.2147
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5000 - loss: 1.2555
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4114 - loss: 1.2395 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4105 - loss: 1.2354
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4080 - loss: 1.2365
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4059 - loss: 1.2389 - val_accuracy: 0.4162 - val_loss: 1.2010
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2217
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3965 - loss: 1.2375 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4015 - loss: 1.2369
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4028 - loss: 1.2368
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4031 - loss: 1.2366
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4031 - loss: 1.2366 - val_accuracy: 0.4445 - val_loss: 1.1717
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 1.2873
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4040 - loss: 1.2365 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4025 - loss: 1.2390
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4027 - loss: 1.2377
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4035 - loss: 1.2362 - val_accuracy: 0.4281 - val_loss: 1.1596
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1659
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4012 - loss: 1.2064 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4014 - loss: 1.2149
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4023 - loss: 1.2178
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4032 - loss: 1.2182 - val_accuracy: 0.4287 - val_loss: 1.1577
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1554
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4241 - loss: 1.1947 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4238 - loss: 1.1987
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4245 - loss: 1.2002
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4237 - loss: 1.2020 - val_accuracy: 0.4632 - val_loss: 1.1882
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0907
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4242 - loss: 1.2105 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4242 - loss: 1.2083
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4249 - loss: 1.2073
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4251 - loss: 1.2069
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4252 - loss: 1.2068 - val_accuracy: 0.4809 - val_loss: 1.1393
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1432
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4383 - loss: 1.1820 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4345 - loss: 1.1864
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4327 - loss: 1.1883
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4316 - loss: 1.1895 - val_accuracy: 0.4869 - val_loss: 1.1260
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0432
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.1820 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.1852
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4353 - loss: 1.1880
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4337 - loss: 1.1891 - val_accuracy: 0.4915 - val_loss: 1.1148
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1725
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.2048 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4252 - loss: 1.1914
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4277 - loss: 1.1878
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4287 - loss: 1.1874 - val_accuracy: 0.4721 - val_loss: 1.1331
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.1846
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4298 - loss: 1.1846 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4350 - loss: 1.1762
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.1737
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4367 - loss: 1.1731 - val_accuracy: 0.4800 - val_loss: 1.1339
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1857
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4486 - loss: 1.1668 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.1731
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.1739
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4396 - loss: 1.1733
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4396 - loss: 1.1733 - val_accuracy: 0.4790 - val_loss: 1.1212
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2462
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4566 - loss: 1.1687 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4539 - loss: 1.1708
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4524 - loss: 1.1712
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4516 - loss: 1.1712 - val_accuracy: 0.5066 - val_loss: 1.0942
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1343
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1540 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1603
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4468 - loss: 1.1622
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4460 - loss: 1.1624 - val_accuracy: 0.4813 - val_loss: 1.1969
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3125 - loss: 1.3858
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4637 - loss: 1.1569 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4551 - loss: 1.1554
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4515 - loss: 1.1551
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4491 - loss: 1.1562 - val_accuracy: 0.4842 - val_loss: 1.0911
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2067
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4585 - loss: 1.1755 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4608 - loss: 1.1657
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4599 - loss: 1.1621
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4592 - loss: 1.1597
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4592 - loss: 1.1596 - val_accuracy: 0.4832 - val_loss: 1.0999
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0314
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1026 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1177
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4729 - loss: 1.1254
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4693 - loss: 1.1299 - val_accuracy: 0.4915 - val_loss: 1.0781
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2075
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4446 - loss: 1.1465 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4516 - loss: 1.1433
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4538 - loss: 1.1410
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4554 - loss: 1.1391
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4555 - loss: 1.1390 - val_accuracy: 0.4888 - val_loss: 1.0941
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 1.2744
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.1675 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.1591
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4486 - loss: 1.1568
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4505 - loss: 1.1550 - val_accuracy: 0.5072 - val_loss: 1.0756
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.2004
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1473 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4718 - loss: 1.1428
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1410
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4697 - loss: 1.1401 - val_accuracy: 0.5026 - val_loss: 1.0679
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1651
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.1586 
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4597 - loss: 1.1545
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4624 - loss: 1.1511
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4640 - loss: 1.1482 - val_accuracy: 0.4938 - val_loss: 1.0653
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1119
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4628 - loss: 1.1174 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4657 - loss: 1.1209
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4682 - loss: 1.1226
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1243
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4686 - loss: 1.1244 - val_accuracy: 0.5283 - val_loss: 1.0447
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1533
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4801 - loss: 1.1241 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4804 - loss: 1.1240
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4800 - loss: 1.1235
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4798 - loss: 1.1235 - val_accuracy: 0.4931 - val_loss: 1.0620
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.0958
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4541 - loss: 1.1245 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4616 - loss: 1.1256
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4650 - loss: 1.1257
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4677 - loss: 1.1256 - val_accuracy: 0.4987 - val_loss: 1.0599
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.1786
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4642 - loss: 1.1484 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4720 - loss: 1.1371
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1322
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4758 - loss: 1.1293 - val_accuracy: 0.5329 - val_loss: 1.0164
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4531 - loss: 1.0250
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4686 - loss: 1.1222 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4753 - loss: 1.1188
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1172
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4794 - loss: 1.1158 - val_accuracy: 0.5292 - val_loss: 1.0384
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1665
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4984 - loss: 1.0858 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4978 - loss: 1.0896
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.0909
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4964 - loss: 1.0923 - val_accuracy: 0.5112 - val_loss: 1.0338
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4688 - loss: 1.2034
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1113 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.1091
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1077
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4876 - loss: 1.1069
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4876 - loss: 1.1069 - val_accuracy: 0.5191 - val_loss: 1.0300
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0780
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4850 - loss: 1.1170 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4841 - loss: 1.1176
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1171
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4877 - loss: 1.1154 - val_accuracy: 0.5273 - val_loss: 1.0298
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1740
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4883 - loss: 1.1067 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1002
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4909 - loss: 1.1007
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4914 - loss: 1.1011
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1012 - val_accuracy: 0.5296 - val_loss: 1.0154
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.1474
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4935 - loss: 1.0923 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4943 - loss: 1.0936
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.0947
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4981 - loss: 1.0936 - val_accuracy: 0.5581 - val_loss: 0.9762
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9767
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0578 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5069 - loss: 1.0728
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5059 - loss: 1.0773
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5047 - loss: 1.0796 - val_accuracy: 0.5089 - val_loss: 1.0364
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0172
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0904 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0873
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0849
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5111 - loss: 1.0841
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5109 - loss: 1.0840 - val_accuracy: 0.5335 - val_loss: 1.0176
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1336
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4936 - loss: 1.0882 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.0787
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5029 - loss: 1.0750
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5043 - loss: 1.0745 - val_accuracy: 0.5335 - val_loss: 1.0056
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9674
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5445 - loss: 1.0488 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0616
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5274 - loss: 1.0670
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5243 - loss: 1.0694 - val_accuracy: 0.5644 - val_loss: 0.9796
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0757
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1115 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5085 - loss: 1.0933
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0842
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5191 - loss: 1.0796 - val_accuracy: 0.5542 - val_loss: 0.9797

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 489ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 21: 56.57 [%]
F1-score capturado en la ejecución 21: 55.49 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:45[0m 679ms/step
[1m 74/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 686us/step  
[1m147/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 689us/step
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 700us/step
[1m286/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 707us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m73/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 700us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 54.5 [%]
Global F1 score (validation) = 52.3 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.27209184 0.20919421 0.33271497 0.18599902]
 [0.337307   0.28321558 0.16891453 0.21056287]
 [0.2883635  0.22704734 0.28841418 0.19617495]
 ...
 [0.04171997 0.02310957 0.9155046  0.01966585]
 [0.05100685 0.02909342 0.8948487  0.02505102]
 [0.03735198 0.02051632 0.92464757 0.01748412]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 59.86 [%]
Global accuracy score (test) = 51.46 [%]
Global F1 score (train) = 58.33 [%]
Global F1 score (test) = 50.32 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.27      0.32       400
MODERATE-INTENSITY       0.45      0.44      0.44       400
         SEDENTARY       0.52      0.85      0.64       400
VIGOROUS-INTENSITY       0.79      0.49      0.61       345

          accuracy                           0.51      1545
         macro avg       0.54      0.51      0.50      1545
      weighted avg       0.53      0.51      0.50      1545

2025-11-05 11:30:18.185899: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:30:18.197326: 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 00:00:1762338618.210727 3332310 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338618.215057 3332310 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338618.225598 3332310 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338618.225619 3332310 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338618.225622 3332310 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338618.225623 3332310 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:30:18.228960: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338620.506197 3332310 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338621.902939 3332425 service.cc:152] XLA service 0x704a8c0037f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338621.902974 3332425 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:30:21.937548: 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 00:00:1762338622.061189 3332425 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338624.207827 3332425 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:29[0m 3s/step - accuracy: 0.2344 - loss: 3.0551
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2421 - loss: 2.6487 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2578 - loss: 2.4507
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2665 - loss: 2.3437
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2728 - loss: 2.2627
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2731 - loss: 2.2578
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2733 - loss: 2.2561 - val_accuracy: 0.3890 - val_loss: 1.2814
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.4954
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.5759 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3013 - loss: 1.5732
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3003 - loss: 1.5599
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3019 - loss: 1.5439 - val_accuracy: 0.3929 - val_loss: 1.2698
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.3131
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3429 - loss: 1.3789 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3376 - loss: 1.3809
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3348 - loss: 1.3791
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3342 - loss: 1.3769 - val_accuracy: 0.3867 - val_loss: 1.2682
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3594 - loss: 1.4228
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3392 - loss: 1.3656 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3444 - loss: 1.3533
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3460 - loss: 1.3480
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3481 - loss: 1.3442
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3484 - loss: 1.3438 - val_accuracy: 0.3853 - val_loss: 1.2490
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2481
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3534 - loss: 1.3037 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.3043
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3587 - loss: 1.3067
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3586 - loss: 1.3073 - val_accuracy: 0.3955 - val_loss: 1.2403
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1788
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3793 - loss: 1.2815 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3716 - loss: 1.2918
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3702 - loss: 1.2929
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3699 - loss: 1.2923
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3699 - loss: 1.2922 - val_accuracy: 0.3972 - val_loss: 1.2241
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2661
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3854 - loss: 1.2724 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3842 - loss: 1.2748
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3842 - loss: 1.2741
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3836 - loss: 1.2740 - val_accuracy: 0.4080 - val_loss: 1.2217
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.1887
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3752 - loss: 1.2648 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3829 - loss: 1.2606
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3869 - loss: 1.2580
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3878 - loss: 1.2566
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3878 - loss: 1.2565 - val_accuracy: 0.4238 - val_loss: 1.2016
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5312 - loss: 1.1997
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4212 - loss: 1.2240 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4124 - loss: 1.2318
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4094 - loss: 1.2352
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4081 - loss: 1.2369 - val_accuracy: 0.4350 - val_loss: 1.1813
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1947
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4160 - loss: 1.2122 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.2223
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4067 - loss: 1.2284
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4051 - loss: 1.2304 - val_accuracy: 0.4451 - val_loss: 1.1766
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3750 - loss: 1.2854
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4099 - loss: 1.2398 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4117 - loss: 1.2353
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4128 - loss: 1.2329
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4147 - loss: 1.2304 - val_accuracy: 0.4580 - val_loss: 1.1450
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1602
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.1993 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4367 - loss: 1.2074
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4305 - loss: 1.2132
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4274 - loss: 1.2146
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4273 - loss: 1.2147 - val_accuracy: 0.4836 - val_loss: 1.1243
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2591
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4007 - loss: 1.2357 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4091 - loss: 1.2260
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4120 - loss: 1.2222
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4135 - loss: 1.2198
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4137 - loss: 1.2194 - val_accuracy: 0.4392 - val_loss: 1.1637
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.4735
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4285 - loss: 1.2356 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4279 - loss: 1.2275
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4306 - loss: 1.2221
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4319 - loss: 1.2184 - val_accuracy: 0.4809 - val_loss: 1.1287
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5156 - loss: 1.0727
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4622 - loss: 1.1668 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4497 - loss: 1.1776
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4425 - loss: 1.1828
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4399 - loss: 1.1850
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4397 - loss: 1.1851 - val_accuracy: 0.4593 - val_loss: 1.1430
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2690
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4188 - loss: 1.1885 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4247 - loss: 1.1847
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4273 - loss: 1.1842
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4285 - loss: 1.1846 - val_accuracy: 0.4773 - val_loss: 1.1292
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.1443
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4319 - loss: 1.1723 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.1686
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4355 - loss: 1.1718
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4353 - loss: 1.1744 - val_accuracy: 0.4921 - val_loss: 1.1037
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2626
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.1887 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4469 - loss: 1.1833
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1787
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.1755
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4510 - loss: 1.1755 - val_accuracy: 0.4974 - val_loss: 1.1066
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2272
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4509 - loss: 1.1603 
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4540 - loss: 1.1645
[1m135/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1654
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4542 - loss: 1.1655 - val_accuracy: 0.5069 - val_loss: 1.0825
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.2521
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4408 - loss: 1.1700 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4422 - loss: 1.1702
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4430 - loss: 1.1694
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4439 - loss: 1.1689 - val_accuracy: 0.4898 - val_loss: 1.0963
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2544
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4591 - loss: 1.1615 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4614 - loss: 1.1605
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1604
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4627 - loss: 1.1601 - val_accuracy: 0.5092 - val_loss: 1.0862
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1960
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4550 - loss: 1.1813 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1751
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4579 - loss: 1.1710
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4583 - loss: 1.1690 - val_accuracy: 0.5253 - val_loss: 1.0759
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1427
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4322 - loss: 1.1736 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4419 - loss: 1.1662
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4481 - loss: 1.1624
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4528 - loss: 1.1591 - val_accuracy: 0.5072 - val_loss: 1.0765
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1973
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1413 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1414
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1406
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4750 - loss: 1.1405
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4750 - loss: 1.1405 - val_accuracy: 0.5197 - val_loss: 1.0646
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.0660
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4873 - loss: 1.1113 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1245
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1288
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1295
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4795 - loss: 1.1295 - val_accuracy: 0.5332 - val_loss: 1.0492
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.2252
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4675 - loss: 1.1326 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4782 - loss: 1.1266
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4820 - loss: 1.1257
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4837 - loss: 1.1263
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4839 - loss: 1.1263 - val_accuracy: 0.5069 - val_loss: 1.0711
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0847
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1097 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1116
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.1149
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4939 - loss: 1.1168
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4939 - loss: 1.1169 - val_accuracy: 0.5092 - val_loss: 1.0403
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1218
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1341 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4755 - loss: 1.1304
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1316
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1311
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1309 - val_accuracy: 0.5312 - val_loss: 1.0232
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.1224
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.1089 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.1151
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4972 - loss: 1.1177
[1m152/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.1187
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4949 - loss: 1.1186 - val_accuracy: 0.5338 - val_loss: 1.0127
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0439
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.0915 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4998 - loss: 1.1005
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4995 - loss: 1.1031
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4992 - loss: 1.1048 - val_accuracy: 0.5453 - val_loss: 1.0305
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 0.9840
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4983 - loss: 1.0881 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.0968
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.0997
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4975 - loss: 1.1006 - val_accuracy: 0.5503 - val_loss: 0.9928
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1740
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5060 - loss: 1.1185 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.1085
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5099 - loss: 1.1053
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.1030
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5100 - loss: 1.1028 - val_accuracy: 0.5378 - val_loss: 1.0040
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5469 - loss: 1.0946
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5366 - loss: 1.0677 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5249 - loss: 1.0801
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0859
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5177 - loss: 1.0880
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5173 - loss: 1.0883 - val_accuracy: 0.5575 - val_loss: 0.9744
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.0857
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5057 - loss: 1.0776 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5088 - loss: 1.0791
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0828
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5093 - loss: 1.0833
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5094 - loss: 1.0833 - val_accuracy: 0.5411 - val_loss: 0.9782
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1416
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5013 - loss: 1.0914 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.0880
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0885
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0869
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5100 - loss: 1.0867 - val_accuracy: 0.5332 - val_loss: 1.0233
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3906 - loss: 1.1198
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.0758 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0726
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0714
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5175 - loss: 1.0722 - val_accuracy: 0.5618 - val_loss: 0.9630
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.1971
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0712 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5195 - loss: 1.0641
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5230 - loss: 1.0623
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5250 - loss: 1.0611
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5250 - loss: 1.0610 - val_accuracy: 0.5463 - val_loss: 0.9665
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 0.9631
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0472 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5312 - loss: 1.0537
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0549
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5284 - loss: 1.0557
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5284 - loss: 1.0557 - val_accuracy: 0.5825 - val_loss: 0.9567
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9979
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0592 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0557
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0555
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5275 - loss: 1.0551
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5276 - loss: 1.0550 - val_accuracy: 0.5713 - val_loss: 0.9574
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0750
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5241 - loss: 1.0569 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5233 - loss: 1.0570
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0552
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0545
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5293 - loss: 1.0545 - val_accuracy: 0.5585 - val_loss: 0.9625
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2016
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5187 - loss: 1.0556 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5265 - loss: 1.0484
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0482
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5309 - loss: 1.0481 - val_accuracy: 0.5759 - val_loss: 0.9202
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0607
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0404 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0350
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0346
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0356
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5433 - loss: 1.0356 - val_accuracy: 0.5828 - val_loss: 0.9273
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0724
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0511 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0445
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0406
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5407 - loss: 1.0395 - val_accuracy: 0.5670 - val_loss: 0.9320
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0488
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0147 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5421 - loss: 1.0177
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0227
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0253
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5439 - loss: 1.0258 - val_accuracy: 0.5670 - val_loss: 0.9219
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 1.0470
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5681 - loss: 1.0147 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5580 - loss: 1.0201
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 1.0236
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 1.0266
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5518 - loss: 1.0270 - val_accuracy: 0.5713 - val_loss: 0.9327
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.1305
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5372 - loss: 1.0421 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5384 - loss: 1.0352
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0321
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0293
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5403 - loss: 1.0292 - val_accuracy: 0.5723 - val_loss: 0.9261

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 469ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 22: 51.46 [%]
F1-score capturado en la ejecución 22: 50.32 [%]

=== 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)
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:47[0m 685ms/step
[1m 72/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 708us/step  
[1m149/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 680us/step
[1m218/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 696us/step
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 694us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 750us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 56.7 [%]
Global F1 score (validation) = 55.3 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.3761692  0.37618935 0.04392346 0.20371793]
 [0.4082356  0.39913735 0.0460901  0.14653702]
 [0.28258678 0.21602586 0.2775415  0.22384588]
 ...
 [0.10271528 0.05811178 0.7944513  0.04472169]
 [0.08851578 0.04873162 0.8234533  0.03929924]
 [0.08755233 0.04810797 0.825337   0.03900272]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 61.91 [%]
Global accuracy score (test) = 56.05 [%]
Global F1 score (train) = 61.05 [%]
Global F1 score (test) = 56.06 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.39      0.40       400
MODERATE-INTENSITY       0.47      0.46      0.47       400
         SEDENTARY       0.58      0.85      0.69       400
VIGOROUS-INTENSITY       0.92      0.54      0.68       345

          accuracy                           0.56      1545
         macro avg       0.60      0.56      0.56      1545
      weighted avg       0.59      0.56      0.56      1545

2025-11-05 11:30:52.682190: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:30:52.693594: 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 00:00:1762338652.706905 3337471 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338652.710956 3337471 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338652.721037 3337471 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338652.721059 3337471 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338652.721061 3337471 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338652.721063 3337471 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:30:52.724253: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338654.962948 3337471 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338656.347943 3337580 service.cc:152] XLA service 0x7817d4009960 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338656.347981 3337580 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:30:56.383877: 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 00:00:1762338656.506396 3337580 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338658.622884 3337580 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:24[0m 3s/step - accuracy: 0.3125 - loss: 2.7025
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2823 - loss: 2.5291 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2892 - loss: 2.4288
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2926 - loss: 2.3554
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2937 - loss: 2.2971
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2937 - loss: 2.2958 - val_accuracy: 0.3949 - val_loss: 1.2641
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.3438 - loss: 1.5750
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3115 - loss: 1.6492 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3136 - loss: 1.6382
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3167 - loss: 1.6183
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3177 - loss: 1.6035
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3178 - loss: 1.6011 - val_accuracy: 0.3932 - val_loss: 1.2558
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.3756
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3130 - loss: 1.4225 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3162 - loss: 1.4165
[1m114/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3201 - loss: 1.4085
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3227 - loss: 1.4018
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3232 - loss: 1.4003 - val_accuracy: 0.3807 - val_loss: 1.2638
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.3507
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.3379 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.3350
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3469 - loss: 1.3333
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3486 - loss: 1.3315 - val_accuracy: 0.3870 - val_loss: 1.2601
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3438 - loss: 1.3455
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.3255 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3606 - loss: 1.3220
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3638 - loss: 1.3172
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3650 - loss: 1.3148 - val_accuracy: 0.3801 - val_loss: 1.2531
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2969 - loss: 1.3693
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3729 - loss: 1.2945 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3762 - loss: 1.2897
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3776 - loss: 1.2878
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3777 - loss: 1.2871 - val_accuracy: 0.3857 - val_loss: 1.2356
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.3165
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3961 - loss: 1.2684 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3923 - loss: 1.2721
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3907 - loss: 1.2741
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3894 - loss: 1.2746 - val_accuracy: 0.4005 - val_loss: 1.2219
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.2210
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4013 - loss: 1.2632 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3949 - loss: 1.2639
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3926 - loss: 1.2627
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3925 - loss: 1.2617
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3926 - loss: 1.2616 - val_accuracy: 0.4110 - val_loss: 1.1975
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.2490
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3899 - loss: 1.2362 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3927 - loss: 1.2398
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3928 - loss: 1.2413
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3929 - loss: 1.2423
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3929 - loss: 1.2423 - val_accuracy: 0.4093 - val_loss: 1.1979
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.2657
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3840 - loss: 1.2487 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3935 - loss: 1.2402
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3966 - loss: 1.2350
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3975 - loss: 1.2331
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3975 - loss: 1.2330 - val_accuracy: 0.4589 - val_loss: 1.1539
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2421
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4145 - loss: 1.2175 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4122 - loss: 1.2174
[1m136/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4116 - loss: 1.2160
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4113 - loss: 1.2158 - val_accuracy: 0.4527 - val_loss: 1.1680
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1872
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4278 - loss: 1.2006 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4236 - loss: 1.2047
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4231 - loss: 1.2066
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4234 - loss: 1.2069 - val_accuracy: 0.4596 - val_loss: 1.1405
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4375 - loss: 1.2318
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4216 - loss: 1.1911 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.1952
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4199 - loss: 1.1959
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4203 - loss: 1.1958 - val_accuracy: 0.4668 - val_loss: 1.1247
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1083
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4394 - loss: 1.1761 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4343 - loss: 1.1851
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.1890
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4315 - loss: 1.1908
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4314 - loss: 1.1909 - val_accuracy: 0.4708 - val_loss: 1.1383
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.1968
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4284 - loss: 1.1881 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4256 - loss: 1.1888
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4274 - loss: 1.1875
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4298 - loss: 1.1868 - val_accuracy: 0.4478 - val_loss: 1.1402
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.2828
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4355 - loss: 1.2056 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4414 - loss: 1.1882
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4409 - loss: 1.1845
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4414 - loss: 1.1830
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4415 - loss: 1.1829 - val_accuracy: 0.4796 - val_loss: 1.1148
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2625
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4514 - loss: 1.1729 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.1708
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4502 - loss: 1.1707
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4497 - loss: 1.1707 - val_accuracy: 0.4652 - val_loss: 1.1183
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2818
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4308 - loss: 1.2000 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4397 - loss: 1.1922
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4443 - loss: 1.1859
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4477 - loss: 1.1809 - val_accuracy: 0.4373 - val_loss: 1.1235
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2393
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.1599 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4512 - loss: 1.1581
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4529 - loss: 1.1589
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4539 - loss: 1.1583 - val_accuracy: 0.4951 - val_loss: 1.0846
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1889
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4260 - loss: 1.1770 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4365 - loss: 1.1711
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4412 - loss: 1.1692
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.1672
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4443 - loss: 1.1671 - val_accuracy: 0.4786 - val_loss: 1.0911
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1015
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4874 - loss: 1.1344 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4773 - loss: 1.1427
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4747 - loss: 1.1433
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4734 - loss: 1.1434
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4734 - loss: 1.1434 - val_accuracy: 0.4803 - val_loss: 1.0708
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2453
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4634 - loss: 1.1605 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4696 - loss: 1.1553
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4712 - loss: 1.1534
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4726 - loss: 1.1508 - val_accuracy: 0.5151 - val_loss: 1.0655
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4375 - loss: 1.1655
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4704 - loss: 1.1468 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4707 - loss: 1.1424
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1393
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.1369 - val_accuracy: 0.5112 - val_loss: 1.0447
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 1.0594
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1272 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1288
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1294
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4872 - loss: 1.1294 - val_accuracy: 0.5292 - val_loss: 1.0252
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 1.0068
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4979 - loss: 1.1106 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4997 - loss: 1.1042
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4970 - loss: 1.1055
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4946 - loss: 1.1078 - val_accuracy: 0.5016 - val_loss: 1.0627
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2115
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4857 - loss: 1.1039 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1073
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1107
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4845 - loss: 1.1109 - val_accuracy: 0.5158 - val_loss: 1.0289
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1291
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1264 
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4873 - loss: 1.1187
[1m136/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4903 - loss: 1.1143
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4910 - loss: 1.1138 - val_accuracy: 0.5319 - val_loss: 1.0009
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.0843
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4837 - loss: 1.1070 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4868 - loss: 1.1090
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1064
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4894 - loss: 1.1047 - val_accuracy: 0.5197 - val_loss: 1.0217
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.0894
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.0911 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.0932
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4975 - loss: 1.0951
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4988 - loss: 1.0952 - val_accuracy: 0.5194 - val_loss: 1.0294
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1280
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4923 - loss: 1.1126 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.1054
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4985 - loss: 1.1014
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4993 - loss: 1.0990
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4993 - loss: 1.0987 - val_accuracy: 0.5361 - val_loss: 0.9881
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0790
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5040 - loss: 1.1174 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1080
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5017 - loss: 1.1019
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5027 - loss: 1.0980
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5028 - loss: 1.0976 - val_accuracy: 0.5352 - val_loss: 1.0330
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1898
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5060 - loss: 1.1185 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5092 - loss: 1.1050
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0960
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5134 - loss: 1.0916 - val_accuracy: 0.4954 - val_loss: 1.0573
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4531 - loss: 1.2218
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0688 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0664
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5277 - loss: 1.0680
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5269 - loss: 1.0691 - val_accuracy: 0.5420 - val_loss: 0.9878
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 0.9991
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5246 - loss: 1.0718 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5218 - loss: 1.0719
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.0727
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5169 - loss: 1.0728 - val_accuracy: 0.5447 - val_loss: 0.9818
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2400
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5158 - loss: 1.0725 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5142 - loss: 1.0696
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0679
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5158 - loss: 1.0663 - val_accuracy: 0.5532 - val_loss: 0.9574
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0023
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5189 - loss: 1.0575 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5240 - loss: 1.0576
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0592
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5231 - loss: 1.0596 - val_accuracy: 0.5677 - val_loss: 0.9496
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5938 - loss: 0.9590
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0582 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0570
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0557
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5245 - loss: 1.0548 - val_accuracy: 0.5345 - val_loss: 0.9876
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0781
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.0702 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5082 - loss: 1.0709
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0661
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5182 - loss: 1.0633 - val_accuracy: 0.5703 - val_loss: 0.9293
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 1.0105
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 1.0486 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5465 - loss: 1.0445
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0435
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5401 - loss: 1.0425
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5400 - loss: 1.0425 - val_accuracy: 0.5723 - val_loss: 0.9257
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9701
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0132 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5381 - loss: 1.0211
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5359 - loss: 1.0272
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5348 - loss: 1.0303 - val_accuracy: 0.5828 - val_loss: 0.9185
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1897
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4982 - loss: 1.0799 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5139 - loss: 1.0607
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5206 - loss: 1.0534
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5225 - loss: 1.0506
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5226 - loss: 1.0505 - val_accuracy: 0.5673 - val_loss: 0.9225
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.9642
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0547 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5281 - loss: 1.0495
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5291 - loss: 1.0471
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5301 - loss: 1.0435 - val_accuracy: 0.5920 - val_loss: 0.9159
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1151
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0379 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0306
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 1.0279
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5463 - loss: 1.0263 - val_accuracy: 0.5664 - val_loss: 0.9178
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4844 - loss: 1.0996
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5229 - loss: 1.0268 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5294 - loss: 1.0271
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0232
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5360 - loss: 1.0224 - val_accuracy: 0.5844 - val_loss: 0.9145
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.8870
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5639 - loss: 1.0181 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 1.0198
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5527 - loss: 1.0202
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5509 - loss: 1.0205 - val_accuracy: 0.5867 - val_loss: 0.8959
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5938 - loss: 1.0642
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5545 - loss: 1.0160 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5526 - loss: 1.0157
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5491 - loss: 1.0189
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0200
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5476 - loss: 1.0200 - val_accuracy: 0.5733 - val_loss: 0.9072
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0283
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 1.0049 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5526 - loss: 1.0107
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5522 - loss: 1.0114
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5520 - loss: 1.0112 - val_accuracy: 0.5963 - val_loss: 0.9098
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0576
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5449 - loss: 1.0331 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0253
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5467 - loss: 1.0202
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5470 - loss: 1.0182 - val_accuracy: 0.5315 - val_loss: 1.0013
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.2253
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5706 - loss: 1.0156 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5657 - loss: 1.0160
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5616 - loss: 1.0168
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5587 - loss: 1.0167 - val_accuracy: 0.5499 - val_loss: 0.9607
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1648
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5146 - loss: 1.0623 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0451
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5297 - loss: 1.0342
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0273
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5353 - loss: 1.0263 - val_accuracy: 0.5880 - val_loss: 0.9097

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 514ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 23: 56.05 [%]
F1-score capturado en la ejecución 23: 56.06 [%]

=== 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)
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:42[0m 670ms/step
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 703us/step  
[1m145/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 700us/step
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 718us/step
[1m284/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 711us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m62/96[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 827us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 59.49 [%]
Global F1 score (validation) = 57.52 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.40745887 0.42793745 0.0302655  0.1343381 ]
 [0.42025852 0.49221948 0.01012061 0.07740144]
 [0.40743375 0.41238958 0.04626986 0.13390677]
 ...
 [0.03225437 0.01867687 0.9321356  0.01693321]
 [0.03084278 0.01776599 0.93521047 0.0161808 ]
 [0.03484708 0.02034869 0.9265035  0.0183008 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 60.63 [%]
Global accuracy score (test) = 52.82 [%]
Global F1 score (train) = 58.41 [%]
Global F1 score (test) = 51.02 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.36      0.18      0.24       400
MODERATE-INTENSITY       0.46      0.52      0.49       400
         SEDENTARY       0.51      0.90      0.65       400
VIGOROUS-INTENSITY       0.92      0.51      0.66       345

          accuracy                           0.53      1545
         macro avg       0.56      0.53      0.51      1545
      weighted avg       0.55      0.53      0.51      1545

2025-11-05 11:31:28.446154: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:31:28.457738: 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 00:00:1762338688.470911 3342995 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338688.475138 3342995 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338688.490812 3342995 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338688.490838 3342995 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338688.490840 3342995 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338688.490841 3342995 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:31:28.494015: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338690.730043 3342995 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338692.094520 3343112 service.cc:152] XLA service 0x7a4bbc01b760 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338692.094564 3343112 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:31:32.136163: 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 00:00:1762338692.262313 3343112 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338694.430725 3343112 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:31[0m 3s/step - accuracy: 0.2344 - loss: 2.8101
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2556 - loss: 2.4993 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2659 - loss: 2.3541
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2729 - loss: 2.2515
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2763 - loss: 2.1798
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2763 - loss: 2.1783 - val_accuracy: 0.3853 - val_loss: 1.2514
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3438 - loss: 1.4835
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3097 - loss: 1.5589 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3082 - loss: 1.5406
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3098 - loss: 1.5239
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3122 - loss: 1.5087 - val_accuracy: 0.3982 - val_loss: 1.2603
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2547
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3314 - loss: 1.3821 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3309 - loss: 1.3764
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3299 - loss: 1.3732
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3297 - loss: 1.3703 - val_accuracy: 0.3844 - val_loss: 1.2543
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4531 - loss: 1.3660
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3721 - loss: 1.3203 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3624 - loss: 1.3218
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3578 - loss: 1.3224
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3565 - loss: 1.3223 - val_accuracy: 0.3945 - val_loss: 1.2481
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2498
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3579 - loss: 1.3027 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.3006
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3640 - loss: 1.3016
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3648 - loss: 1.3013 - val_accuracy: 0.4011 - val_loss: 1.2370
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.2689
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3690 - loss: 1.2947 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3692 - loss: 1.2919
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3684 - loss: 1.2907
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3687 - loss: 1.2895 - val_accuracy: 0.3853 - val_loss: 1.2199
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.2758
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3708 - loss: 1.2629 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3725 - loss: 1.2640
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3754 - loss: 1.2645
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3773 - loss: 1.2646 - val_accuracy: 0.4060 - val_loss: 1.2093
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m5s[0m 30ms/step - accuracy: 0.3906 - loss: 1.2145
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4055 - loss: 1.2479 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4005 - loss: 1.2482
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3999 - loss: 1.2468
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4003 - loss: 1.2446 - val_accuracy: 0.4271 - val_loss: 1.1759
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.2841
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3876 - loss: 1.2358 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3913 - loss: 1.2373
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3953 - loss: 1.2344
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3981 - loss: 1.2325 - val_accuracy: 0.4501 - val_loss: 1.1600
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.2549
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4042 - loss: 1.2400 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4052 - loss: 1.2322
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4068 - loss: 1.2297
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4084 - loss: 1.2271 - val_accuracy: 0.4432 - val_loss: 1.1512
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.3094
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4318 - loss: 1.2003 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4295 - loss: 1.2029
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4275 - loss: 1.2046
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4256 - loss: 1.2061
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4255 - loss: 1.2063 - val_accuracy: 0.4468 - val_loss: 1.1377
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.2325
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4327 - loss: 1.2241 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4315 - loss: 1.2162
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4308 - loss: 1.2129
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.2106
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4312 - loss: 1.2103 - val_accuracy: 0.4580 - val_loss: 1.1306
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3906 - loss: 1.1474
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4510 - loss: 1.1647 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4453 - loss: 1.1742
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4421 - loss: 1.1782
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4404 - loss: 1.1802
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4403 - loss: 1.1804 - val_accuracy: 0.4432 - val_loss: 1.1709
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.2494
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4221 - loss: 1.1884 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4269 - loss: 1.1862
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4291 - loss: 1.1858
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4303 - loss: 1.1862
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4303 - loss: 1.1862 - val_accuracy: 0.4497 - val_loss: 1.1507
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1894
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4301 - loss: 1.1806 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4387 - loss: 1.1773
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4417 - loss: 1.1763
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4422 - loss: 1.1764 - val_accuracy: 0.4803 - val_loss: 1.1091
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3750 - loss: 1.2428
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4500 - loss: 1.1737 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4509 - loss: 1.1680
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4495 - loss: 1.1668
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4491 - loss: 1.1660 - val_accuracy: 0.4599 - val_loss: 1.1415
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4844 - loss: 1.2170
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4543 - loss: 1.1682 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4527 - loss: 1.1646
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1646
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4509 - loss: 1.1641
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4509 - loss: 1.1641 - val_accuracy: 0.4606 - val_loss: 1.1520
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.3465
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4469 - loss: 1.1906 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4478 - loss: 1.1793
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4497 - loss: 1.1748
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4520 - loss: 1.1715
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4521 - loss: 1.1712 - val_accuracy: 0.4862 - val_loss: 1.0868
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2201
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4728 - loss: 1.1649 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4692 - loss: 1.1620
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1583
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4685 - loss: 1.1556 - val_accuracy: 0.4888 - val_loss: 1.0670
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0333
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4626 - loss: 1.1758 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4630 - loss: 1.1640
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4637 - loss: 1.1587
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4645 - loss: 1.1555 - val_accuracy: 0.5115 - val_loss: 1.0546
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2233
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1618 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1524
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4713 - loss: 1.1480
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4710 - loss: 1.1463
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4710 - loss: 1.1462 - val_accuracy: 0.4823 - val_loss: 1.1096
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.2126
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4887 - loss: 1.1433 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1452
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4738 - loss: 1.1450
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4725 - loss: 1.1427
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4725 - loss: 1.1426 - val_accuracy: 0.5457 - val_loss: 1.0159
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2190
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4757 - loss: 1.1138 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1190
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1220
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4766 - loss: 1.1228 - val_accuracy: 0.4984 - val_loss: 1.1032
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.1510
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5153 - loss: 1.1227 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.1130
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5032 - loss: 1.1123
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4999 - loss: 1.1135 - val_accuracy: 0.4921 - val_loss: 1.0938
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2214
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1025 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4875 - loss: 1.1039
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4864 - loss: 1.1074
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4856 - loss: 1.1097
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4856 - loss: 1.1098 - val_accuracy: 0.5223 - val_loss: 1.0294
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5312 - loss: 1.1006
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1316 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1254
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4846 - loss: 1.1214
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4866 - loss: 1.1184 - val_accuracy: 0.5145 - val_loss: 1.0624
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0243
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5005 - loss: 1.0623 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4988 - loss: 1.0762
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4971 - loss: 1.0844
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4969 - loss: 1.0872 - val_accuracy: 0.5394 - val_loss: 1.0092
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1294
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5014 - loss: 1.0803 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5025 - loss: 1.0820
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5019 - loss: 1.0859
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.0879 - val_accuracy: 0.5338 - val_loss: 1.0160
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3438 - loss: 1.2170
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1094 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.0987
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4973 - loss: 1.0954
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4990 - loss: 1.0942 - val_accuracy: 0.5273 - val_loss: 1.0061
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0866
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5118 - loss: 1.0578 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0648
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5094 - loss: 1.0686
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5100 - loss: 1.0704
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5100 - loss: 1.0708 - val_accuracy: 0.5716 - val_loss: 0.9513
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9416
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5251 - loss: 1.0741 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5185 - loss: 1.0779
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5168 - loss: 1.0764
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5157 - loss: 1.0758 - val_accuracy: 0.5818 - val_loss: 0.9399
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5781 - loss: 0.9478
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5236 - loss: 1.0538 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5172 - loss: 1.0657
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0704
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5128 - loss: 1.0725
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5126 - loss: 1.0728 - val_accuracy: 0.5210 - val_loss: 1.0149
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1742
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4922 - loss: 1.1251 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.0984
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5045 - loss: 1.0906
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5064 - loss: 1.0859
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5067 - loss: 1.0854 - val_accuracy: 0.5700 - val_loss: 0.9563
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.5938 - loss: 1.0469
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5319 - loss: 1.0584 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5272 - loss: 1.0581
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5256 - loss: 1.0582
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5249 - loss: 1.0586 - val_accuracy: 0.5802 - val_loss: 0.9329
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9774
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0396 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5263 - loss: 1.0497
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0545
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5258 - loss: 1.0561
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5258 - loss: 1.0562 - val_accuracy: 0.5466 - val_loss: 0.9591
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0520
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5296 - loss: 1.0367 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5262 - loss: 1.0437
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5271 - loss: 1.0438
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5281 - loss: 1.0435 - val_accuracy: 0.5621 - val_loss: 0.9436
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 0.9717
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.0588 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5035 - loss: 1.0589
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0576
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5111 - loss: 1.0567 - val_accuracy: 0.5562 - val_loss: 0.9751
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.2302
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5235 - loss: 1.0679 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5232 - loss: 1.0600
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5231 - loss: 1.0567
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5237 - loss: 1.0544 - val_accuracy: 0.5739 - val_loss: 0.9438
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1729
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5214 - loss: 1.0430 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0417
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5287 - loss: 1.0407
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5294 - loss: 1.0404 - val_accuracy: 0.5811 - val_loss: 0.9262
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.5625 - loss: 1.0828
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0564 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0551
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5377 - loss: 1.0519
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5379 - loss: 1.0501 - val_accuracy: 0.5621 - val_loss: 0.9583
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5000 - loss: 1.1014
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5216 - loss: 1.0434 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0369
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5326 - loss: 1.0341
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5338 - loss: 1.0330 - val_accuracy: 0.6051 - val_loss: 0.8970
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0863
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5494 - loss: 1.0440 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5516 - loss: 1.0292
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5490 - loss: 1.0279
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5477 - loss: 1.0286 - val_accuracy: 0.5917 - val_loss: 0.9084
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.7188 - loss: 0.8055
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5358 - loss: 1.0304 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0313
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0277
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5373 - loss: 1.0259 - val_accuracy: 0.5943 - val_loss: 0.8983
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0939
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5359 - loss: 1.0469 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5387 - loss: 1.0355
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0316
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5399 - loss: 1.0306 - val_accuracy: 0.5976 - val_loss: 0.8931
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5781 - loss: 1.0035
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0076 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5405 - loss: 1.0104
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0113
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5401 - loss: 1.0130
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5401 - loss: 1.0131 - val_accuracy: 0.5621 - val_loss: 0.9292
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5781 - loss: 0.9879
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5455 - loss: 1.0126 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5362 - loss: 1.0195
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5345 - loss: 1.0198
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5345 - loss: 1.0203 - val_accuracy: 0.5986 - val_loss: 0.8832
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0208
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0145 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0146
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0152
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5434 - loss: 1.0156
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5434 - loss: 1.0156 - val_accuracy: 0.5539 - val_loss: 0.8938
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5000 - loss: 1.0207
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5433 - loss: 1.0011 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0059
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0071
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5442 - loss: 1.0074
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5442 - loss: 1.0074 - val_accuracy: 0.6035 - val_loss: 0.8692
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.8851
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 0.9989 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0013
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5419 - loss: 1.0032
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5432 - loss: 1.0035 - val_accuracy: 0.5825 - val_loss: 0.8854
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1833
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0414 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0279
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5443 - loss: 1.0225
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5449 - loss: 1.0184 - val_accuracy: 0.5953 - val_loss: 0.8833
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0418
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 0.9994 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5391 - loss: 1.0003
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5394 - loss: 1.0021
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5397 - loss: 1.0041 - val_accuracy: 0.6028 - val_loss: 0.8839
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0789
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0061 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5453 - loss: 1.0043
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5466 - loss: 1.0041
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5477 - loss: 1.0034
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5478 - loss: 1.0036 - val_accuracy: 0.5959 - val_loss: 0.8915
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.5000 - loss: 1.1750
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0387 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5388 - loss: 1.0248
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0162
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5476 - loss: 1.0112
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5477 - loss: 1.0110 - val_accuracy: 0.5470 - val_loss: 0.9406

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 507ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 24: 52.82 [%]
F1-score capturado en la ejecución 24: 51.02 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:50[0m 694ms/step
[1m 73/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 697us/step  
[1m150/333[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 675us/step
[1m215/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 705us/step
[1m288/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 703us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m74/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 687us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 54.57 [%]
Global F1 score (validation) = 52.69 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.16399185 0.1162077  0.58610284 0.13369763]
 [0.17103179 0.1227053  0.5592474  0.14701548]
 [0.22349037 0.16888875 0.40962294 0.19799802]
 ...
 [0.09051442 0.0586978  0.80129415 0.04949365]
 [0.05378166 0.03276896 0.8845086  0.02894073]
 [0.05264677 0.03199426 0.8871193  0.02823972]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 60.71 [%]
Global accuracy score (test) = 53.66 [%]
Global F1 score (train) = 58.75 [%]
Global F1 score (test) = 52.41 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.40      0.23      0.29       400
MODERATE-INTENSITY       0.46      0.49      0.48       400
         SEDENTARY       0.53      0.85      0.65       400
VIGOROUS-INTENSITY       0.82      0.58      0.68       345

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

2025-11-05 11:32:05.153506: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:32:05.164678: 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 00:00:1762338725.178440 3348811 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338725.182423 3348811 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338725.192387 3348811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338725.192408 3348811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338725.192410 3348811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338725.192412 3348811 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:32:05.195402: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338727.452337 3348811 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338728.823186 3348946 service.cc:152] XLA service 0x773eb400a4d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338728.823213 3348946 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:32:08.856156: 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 00:00:1762338728.978544 3348946 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338731.081877 3348946 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:16[0m 3s/step - accuracy: 0.3125 - loss: 2.2882
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2940 - loss: 2.2699 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2985 - loss: 2.1984
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2992 - loss: 2.1369
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2998 - loss: 2.0910
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2998 - loss: 2.0898 - val_accuracy: 0.3788 - val_loss: 1.2546
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.6376
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2969 - loss: 1.5451 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3013 - loss: 1.5288
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3052 - loss: 1.5128
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3085 - loss: 1.4975 - val_accuracy: 0.3867 - val_loss: 1.2593
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.3333
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3333 - loss: 1.3648 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3361 - loss: 1.3615
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3380 - loss: 1.3582
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3390 - loss: 1.3556 - val_accuracy: 0.3765 - val_loss: 1.2586
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2882
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.3297 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3499 - loss: 1.3256
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3500 - loss: 1.3250
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3503 - loss: 1.3240
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3503 - loss: 1.3240 - val_accuracy: 0.3867 - val_loss: 1.2527
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.3125 - loss: 1.3230
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3529 - loss: 1.3181 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3584 - loss: 1.3160
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3617 - loss: 1.3140
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3645 - loss: 1.3113 - val_accuracy: 0.3890 - val_loss: 1.2346
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3208
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3723 - loss: 1.2818 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3736 - loss: 1.2834
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3733 - loss: 1.2820
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3737 - loss: 1.2809
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3737 - loss: 1.2809 - val_accuracy: 0.3985 - val_loss: 1.2214
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.2660
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3948 - loss: 1.2585 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3891 - loss: 1.2591
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3875 - loss: 1.2590
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3864 - loss: 1.2591 - val_accuracy: 0.4044 - val_loss: 1.2019
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4375 - loss: 1.3623
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3949 - loss: 1.2836 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3925 - loss: 1.2714
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3925 - loss: 1.2640
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3924 - loss: 1.2591 - val_accuracy: 0.3936 - val_loss: 1.1840
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4219 - loss: 1.1977
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4271 - loss: 1.2181 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4211 - loss: 1.2217
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4175 - loss: 1.2247
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4158 - loss: 1.2251 - val_accuracy: 0.4376 - val_loss: 1.1578
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1165
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4130 - loss: 1.2018 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4118 - loss: 1.2084
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4125 - loss: 1.2108
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4132 - loss: 1.2113
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4133 - loss: 1.2113 - val_accuracy: 0.4550 - val_loss: 1.1364
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4062 - loss: 1.1879
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4225 - loss: 1.2085 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4208 - loss: 1.2092
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4196 - loss: 1.2087
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4187 - loss: 1.2086 - val_accuracy: 0.4412 - val_loss: 1.1785
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.3259
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4032 - loss: 1.2219 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4103 - loss: 1.2166
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4142 - loss: 1.2123
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4165 - loss: 1.2097
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4166 - loss: 1.2097 - val_accuracy: 0.4333 - val_loss: 1.1417
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1281
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4416 - loss: 1.1707 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.1790
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.1823
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.1838
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4329 - loss: 1.1839 - val_accuracy: 0.4461 - val_loss: 1.1252
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1315
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4107 - loss: 1.1940 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4208 - loss: 1.1910
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4230 - loss: 1.1897
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4249 - loss: 1.1887
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4249 - loss: 1.1886 - val_accuracy: 0.4330 - val_loss: 1.1319
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1059
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4640 - loss: 1.1556 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1677
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4470 - loss: 1.1714
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4449 - loss: 1.1726 - val_accuracy: 0.4704 - val_loss: 1.0985
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.5000 - loss: 1.0847
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4455 - loss: 1.1630 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4444 - loss: 1.1638
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4442 - loss: 1.1631
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4441 - loss: 1.1642 - val_accuracy: 0.4494 - val_loss: 1.1207
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.2410
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4358 - loss: 1.1873 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.1828
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4337 - loss: 1.1801
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4349 - loss: 1.1778
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4350 - loss: 1.1778 - val_accuracy: 0.4290 - val_loss: 1.1694
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2113
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4489 - loss: 1.1508 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4432 - loss: 1.1557
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.1565
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.1565
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4437 - loss: 1.1565 - val_accuracy: 0.4356 - val_loss: 1.0960
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.4021
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4275 - loss: 1.2023 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4341 - loss: 1.1851
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4369 - loss: 1.1788
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4386 - loss: 1.1753 - val_accuracy: 0.4481 - val_loss: 1.1021
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1986
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4293 - loss: 1.1699 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.1680
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4386 - loss: 1.1666
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4403 - loss: 1.1652 - val_accuracy: 0.4875 - val_loss: 1.0673
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.4844 - loss: 1.1196
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4553 - loss: 1.1520 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4558 - loss: 1.1516
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4552 - loss: 1.1529
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4545 - loss: 1.1532
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4545 - loss: 1.1533 - val_accuracy: 0.4484 - val_loss: 1.0944
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4375 - loss: 1.1971
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4421 - loss: 1.1662 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4438 - loss: 1.1585
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4456 - loss: 1.1566
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.1559
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4465 - loss: 1.1558 - val_accuracy: 0.4885 - val_loss: 1.0696
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.4062 - loss: 1.1933
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4345 - loss: 1.1512 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4487 - loss: 1.1431
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4556 - loss: 1.1409
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4577 - loss: 1.1404 - val_accuracy: 0.4832 - val_loss: 1.0613
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5312 - loss: 1.1278
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4724 - loss: 1.1418 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4657 - loss: 1.1430
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4633 - loss: 1.1413
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4622 - loss: 1.1404
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4621 - loss: 1.1403 - val_accuracy: 0.4961 - val_loss: 1.0699
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1649
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4467 - loss: 1.1738 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4511 - loss: 1.1598
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4531 - loss: 1.1535
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4553 - loss: 1.1499
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4554 - loss: 1.1497 - val_accuracy: 0.4836 - val_loss: 1.0711
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0482
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4678 - loss: 1.1212 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4631 - loss: 1.1276
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4625 - loss: 1.1300
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4627 - loss: 1.1312 - val_accuracy: 0.4747 - val_loss: 1.0848
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1872
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4623 - loss: 1.1298 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4609 - loss: 1.1325
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1313
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4625 - loss: 1.1296 - val_accuracy: 0.4780 - val_loss: 1.0535
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0873
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4657 - loss: 1.1227 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4658 - loss: 1.1241
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4647 - loss: 1.1263
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4645 - loss: 1.1268 - val_accuracy: 0.4961 - val_loss: 1.0535
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9518
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4745 - loss: 1.1057 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1137
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1160
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4760 - loss: 1.1170 - val_accuracy: 0.5089 - val_loss: 1.0486
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0741
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4911 - loss: 1.1208 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1205
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4875 - loss: 1.1198
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4859 - loss: 1.1192 - val_accuracy: 0.4773 - val_loss: 1.0810
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.5625 - loss: 0.9715
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4784 - loss: 1.1112 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4835 - loss: 1.1081
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4858 - loss: 1.1066
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4851 - loss: 1.1076
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4850 - loss: 1.1077 - val_accuracy: 0.4997 - val_loss: 1.0525
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.1653
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4659 - loss: 1.1156 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1152
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4768 - loss: 1.1154
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1143 - val_accuracy: 0.5125 - val_loss: 1.0287
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0961
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1237 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4840 - loss: 1.1177
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4861 - loss: 1.1150
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4868 - loss: 1.1141 - val_accuracy: 0.5164 - val_loss: 1.0115
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0940
[1m 47/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.1127 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5010 - loss: 1.1128
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1134
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4944 - loss: 1.1133 - val_accuracy: 0.5131 - val_loss: 1.0260
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1583
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1260 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4793 - loss: 1.1191
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1159
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4822 - loss: 1.1146 - val_accuracy: 0.5260 - val_loss: 1.0197
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 1.1130
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4891 - loss: 1.1056 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4927 - loss: 1.1063
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4919 - loss: 1.1081
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4915 - loss: 1.1081 - val_accuracy: 0.5131 - val_loss: 1.0139
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4219 - loss: 1.1808
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.0963 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4938 - loss: 1.0983
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.0975
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4966 - loss: 1.0971
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4967 - loss: 1.0971 - val_accuracy: 0.5230 - val_loss: 1.0120
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0852
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5176 - loss: 1.0661 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5110 - loss: 1.0755
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5077 - loss: 1.0797
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5051 - loss: 1.0830 - val_accuracy: 0.5007 - val_loss: 1.0461

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 487ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 25: 53.66 [%]
F1-score capturado en la ejecución 25: 52.41 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:44[0m 676ms/step
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 759us/step  
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 723us/step
[1m212/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 714us/step
[1m281/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 717us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m75/96[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 678us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 49.7 [%]
Global F1 score (validation) = 47.79 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.30119857 0.23810774 0.26066723 0.20002644]
 [0.32352287 0.26638335 0.19170474 0.21838902]
 [0.29496083 0.23192716 0.27490044 0.19821155]
 ...
 [0.03656933 0.019798   0.9243044  0.01932823]
 [0.04974119 0.02789869 0.89525306 0.02710699]
 [0.04021966 0.02200128 0.91638684 0.02139221]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.3 [%]
Global accuracy score (test) = 51.46 [%]
Global F1 score (train) = 55.42 [%]
Global F1 score (test) = 50.33 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.42      0.43      0.43       400
MODERATE-INTENSITY       0.44      0.38      0.40       400
         SEDENTARY       0.54      0.85      0.66       400
VIGOROUS-INTENSITY       0.83      0.39      0.53       345

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

2025-11-05 11:32:36.939690: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:32:36.950872: 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 00:00:1762338756.963913 3353247 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338756.968016 3353247 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338756.978000 3353247 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338756.978021 3353247 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338756.978022 3353247 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338756.978023 3353247 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:32:36.981331: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338759.210172 3353247 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338760.573328 3353376 service.cc:152] XLA service 0x70151001d6b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338760.573373 3353376 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:32:40.618821: 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 00:00:1762338760.744818 3353376 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338762.836879 3353376 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:19[0m 3s/step - accuracy: 0.2344 - loss: 2.4108
[1m 33/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2727 - loss: 2.3585 
[1m 73/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2778 - loss: 2.2675
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2819 - loss: 2.1940
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.2831 - loss: 2.1379
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2834 - loss: 2.1249
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2834 - loss: 2.1237 - val_accuracy: 0.3867 - val_loss: 1.2499
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.2188 - loss: 1.6505
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3110 - loss: 1.5401 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3128 - loss: 1.5281
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3136 - loss: 1.5156
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3139 - loss: 1.5040 - val_accuracy: 0.3880 - val_loss: 1.2645
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.4564
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3236 - loss: 1.3738 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3297 - loss: 1.3662
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3315 - loss: 1.3635
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3317 - loss: 1.3619
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3317 - loss: 1.3617 - val_accuracy: 0.3883 - val_loss: 1.2633
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.4069
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3455 - loss: 1.3319 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3475 - loss: 1.3288
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3485 - loss: 1.3278
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3490 - loss: 1.3270 - val_accuracy: 0.3982 - val_loss: 1.2631
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4531 - loss: 1.3492
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3538 - loss: 1.3380 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3558 - loss: 1.3275
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3582 - loss: 1.3230
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3601 - loss: 1.3197
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3603 - loss: 1.3194 - val_accuracy: 0.3972 - val_loss: 1.2481
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.3503
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3705 - loss: 1.3117 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3694 - loss: 1.3063
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3697 - loss: 1.3023
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3693 - loss: 1.3003
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3693 - loss: 1.3001 - val_accuracy: 0.4080 - val_loss: 1.2349
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2798
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3770 - loss: 1.2798 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3770 - loss: 1.2775
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3788 - loss: 1.2776
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3793 - loss: 1.2777
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3793 - loss: 1.2777 - val_accuracy: 0.3949 - val_loss: 1.2104
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.3013
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3856 - loss: 1.2833 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3885 - loss: 1.2725
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3879 - loss: 1.2693
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3878 - loss: 1.2668 - val_accuracy: 0.4018 - val_loss: 1.1919
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 1.2462
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3820 - loss: 1.2513 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3909 - loss: 1.2475
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3948 - loss: 1.2460
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3964 - loss: 1.2446
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3964 - loss: 1.2445 - val_accuracy: 0.4175 - val_loss: 1.1667
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2866
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3897 - loss: 1.2647 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3953 - loss: 1.2550
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3969 - loss: 1.2499
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.3966 - loss: 1.2466
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3967 - loss: 1.2458 - val_accuracy: 0.4537 - val_loss: 1.1372
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.2820
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4114 - loss: 1.2415 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4072 - loss: 1.2395
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4085 - loss: 1.2367
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4096 - loss: 1.2339
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4097 - loss: 1.2337 - val_accuracy: 0.4553 - val_loss: 1.1301
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.2548
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4162 - loss: 1.2076 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4176 - loss: 1.2050
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4186 - loss: 1.2065
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4194 - loss: 1.2084
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4194 - loss: 1.2086 - val_accuracy: 0.4754 - val_loss: 1.1223
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2077
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4297 - loss: 1.1984 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4286 - loss: 1.1978
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4267 - loss: 1.1986
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4264 - loss: 1.1988 - val_accuracy: 0.4596 - val_loss: 1.1209
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.2334
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4047 - loss: 1.2065 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4103 - loss: 1.1996
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4133 - loss: 1.1975
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4161 - loss: 1.1959 - val_accuracy: 0.4819 - val_loss: 1.1019
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.1984
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4225 - loss: 1.1880 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4235 - loss: 1.1885
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4255 - loss: 1.1886
[1m154/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4266 - loss: 1.1885
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4269 - loss: 1.1885 - val_accuracy: 0.4602 - val_loss: 1.1226
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1594
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4380 - loss: 1.1844 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4405 - loss: 1.1794
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4406 - loss: 1.1764
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4396 - loss: 1.1769 - val_accuracy: 0.4908 - val_loss: 1.1149
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3750 - loss: 1.2446
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4478 - loss: 1.1727 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.1710
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4429 - loss: 1.1716
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4427 - loss: 1.1727 - val_accuracy: 0.4747 - val_loss: 1.0895
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1061
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4494 - loss: 1.1463 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4479 - loss: 1.1542
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4480 - loss: 1.1563
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4477 - loss: 1.1578 - val_accuracy: 0.4855 - val_loss: 1.0787
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.1223
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4582 - loss: 1.1461 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4588 - loss: 1.1493
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4577 - loss: 1.1516
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4560 - loss: 1.1545
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4559 - loss: 1.1545 - val_accuracy: 0.4977 - val_loss: 1.0660
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.0834
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4402 - loss: 1.1651 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.1619
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4476 - loss: 1.1601
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4493 - loss: 1.1590 - val_accuracy: 0.4957 - val_loss: 1.0532
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2854
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4280 - loss: 1.1808 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4392 - loss: 1.1722
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4453 - loss: 1.1678
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4481 - loss: 1.1652 - val_accuracy: 0.4294 - val_loss: 1.1731
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1750
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4578 - loss: 1.1719 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4584 - loss: 1.1622
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4595 - loss: 1.1594
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4593 - loss: 1.1580 - val_accuracy: 0.5079 - val_loss: 1.0593
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0998
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4892 - loss: 1.1312 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4813 - loss: 1.1353
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1363
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4752 - loss: 1.1371 - val_accuracy: 0.5000 - val_loss: 1.0660
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1439
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1295 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1335
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1347
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4769 - loss: 1.1368
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4768 - loss: 1.1369 - val_accuracy: 0.5076 - val_loss: 1.0485
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.1059
[1m 29/167[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4619 - loss: 1.1391 
[1m 68/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.4683 - loss: 1.1357
[1m112/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4697 - loss: 1.1357
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4708 - loss: 1.1352
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4711 - loss: 1.1350 - val_accuracy: 0.5128 - val_loss: 1.0530
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0185
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4763 - loss: 1.1177 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4792 - loss: 1.1229
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4777 - loss: 1.1269
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4788 - loss: 1.1270
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4789 - loss: 1.1270 - val_accuracy: 0.5233 - val_loss: 1.0071
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9824
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4705 - loss: 1.1235 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4715 - loss: 1.1236
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4741 - loss: 1.1221
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4762 - loss: 1.1202 - val_accuracy: 0.4951 - val_loss: 1.0318
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 27ms/step - accuracy: 0.5625 - loss: 1.0062
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1359 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1326
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4827 - loss: 1.1283
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4838 - loss: 1.1256
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4839 - loss: 1.1254 - val_accuracy: 0.4898 - val_loss: 1.0543
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0648
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4834 - loss: 1.1069 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1126
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1147
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4877 - loss: 1.1151 - val_accuracy: 0.5269 - val_loss: 1.0117
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1495
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4957 - loss: 1.0938 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4962 - loss: 1.0982
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1009
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4922 - loss: 1.1021 - val_accuracy: 0.5256 - val_loss: 1.0135
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4688 - loss: 1.0554
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1041 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4961 - loss: 1.1093
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1091
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.1089
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4968 - loss: 1.1088 - val_accuracy: 0.5214 - val_loss: 1.0075

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 488ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 26: 51.46 [%]
F1-score capturado en la ejecución 26: 50.33 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:44[0m 675ms/step
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 752us/step  
[1m144/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 703us/step
[1m211/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 718us/step
[1m287/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 704us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m66/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 777us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 52.33 [%]
Global F1 score (validation) = 51.68 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.34378746 0.30371785 0.13043696 0.22205779]
 [0.40031958 0.3883954  0.02726799 0.18401699]
 [0.33594334 0.29025432 0.16814643 0.20565592]
 ...
 [0.04604615 0.03186834 0.8876069  0.03447851]
 [0.10090773 0.07497332 0.7423955  0.08172345]
 [0.07586332 0.05461919 0.80960053 0.0599169 ]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 56.3 [%]
Global accuracy score (test) = 49.71 [%]
Global F1 score (train) = 56.46 [%]
Global F1 score (test) = 48.02 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.39      0.45      0.42       400
MODERATE-INTENSITY       0.40      0.39      0.39       400
         SEDENTARY       0.57      0.83      0.68       400
VIGOROUS-INTENSITY       0.85      0.29      0.43       345

          accuracy                           0.50      1545
         macro avg       0.55      0.49      0.48      1545
      weighted avg       0.54      0.50      0.48      1545

2025-11-05 11:33:06.403314: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:33:06.414597: 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 00:00:1762338786.427726 3357027 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338786.431844 3357027 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338786.441626 3357027 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338786.441642 3357027 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338786.441644 3357027 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338786.441645 3357027 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:33:06.444808: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338788.676474 3357027 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13756 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:65:00.0, compute capability: 8.9
1 GPU(s) detected and VRAM set to crossover mode..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338790.032982 3357147 service.cc:152] XLA service 0x70197401bb50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338790.033025 3357147 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:33:10.075381: 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 00:00:1762338790.197210 3357147 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338792.287640 3357147 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:17[0m 3s/step - accuracy: 0.2812 - loss: 2.6593
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2646 - loss: 2.4071 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2721 - loss: 2.2987
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2769 - loss: 2.2191
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2800 - loss: 2.1575
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2802 - loss: 2.1518
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2803 - loss: 2.1504 - val_accuracy: 0.3919 - val_loss: 1.2777
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.3118
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3138 - loss: 1.4963 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3146 - loss: 1.4916
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3137 - loss: 1.4858
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3133 - loss: 1.4792 - val_accuracy: 0.3814 - val_loss: 1.2649
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3438 - loss: 1.4260
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3247 - loss: 1.3756 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3217 - loss: 1.3751
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3234 - loss: 1.3735
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3257 - loss: 1.3708 - val_accuracy: 0.3768 - val_loss: 1.2682
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2656 - loss: 1.3507
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3362 - loss: 1.3399 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3445 - loss: 1.3393
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3473 - loss: 1.3383
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3487 - loss: 1.3368
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3488 - loss: 1.3367 - val_accuracy: 0.3945 - val_loss: 1.2588
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.3427
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3484 - loss: 1.3294 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3519 - loss: 1.3237
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3546 - loss: 1.3214
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3568 - loss: 1.3191 - val_accuracy: 0.3860 - val_loss: 1.2505
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.3926
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3602 - loss: 1.3026 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3599 - loss: 1.2996
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3614 - loss: 1.2979
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3625 - loss: 1.2970
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3625 - loss: 1.2970 - val_accuracy: 0.3824 - val_loss: 1.2352
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3125 - loss: 1.3611
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3681 - loss: 1.2935 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3749 - loss: 1.2842
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3750 - loss: 1.2822
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3750 - loss: 1.2814
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3749 - loss: 1.2813 - val_accuracy: 0.3949 - val_loss: 1.2254
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2789
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3705 - loss: 1.2582 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3793 - loss: 1.2586
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3828 - loss: 1.2592
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3847 - loss: 1.2596
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3847 - loss: 1.2596 - val_accuracy: 0.3982 - val_loss: 1.2085
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.2377
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3973 - loss: 1.2392 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3921 - loss: 1.2404
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3908 - loss: 1.2421
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3909 - loss: 1.2428
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3909 - loss: 1.2428 - val_accuracy: 0.4097 - val_loss: 1.1881
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.2535
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4169 - loss: 1.2395 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4165 - loss: 1.2319
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4144 - loss: 1.2310
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4134 - loss: 1.2308 - val_accuracy: 0.4382 - val_loss: 1.1986
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2812 - loss: 1.2443
[1m 47/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3890 - loss: 1.2375 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3982 - loss: 1.2329
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4019 - loss: 1.2314
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4037 - loss: 1.2297 - val_accuracy: 0.4379 - val_loss: 1.1750
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2548
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3984 - loss: 1.2257 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4025 - loss: 1.2271
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4050 - loss: 1.2254
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4069 - loss: 1.2235 - val_accuracy: 0.4717 - val_loss: 1.1298
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.2991
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4030 - loss: 1.2281 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4087 - loss: 1.2212
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4127 - loss: 1.2186
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4150 - loss: 1.2165 - val_accuracy: 0.4737 - val_loss: 1.1336
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2345
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4311 - loss: 1.1893 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4289 - loss: 1.1911
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4313 - loss: 1.1901
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4320 - loss: 1.1914 - val_accuracy: 0.4566 - val_loss: 1.1325
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3125 - loss: 1.3673
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4137 - loss: 1.2050 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4188 - loss: 1.1998
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4206 - loss: 1.1973
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4227 - loss: 1.1957
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4231 - loss: 1.1954 - val_accuracy: 0.4885 - val_loss: 1.1032
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2943
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4354 - loss: 1.2005 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4412 - loss: 1.1919
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4434 - loss: 1.1878
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4438 - loss: 1.1860 - val_accuracy: 0.4987 - val_loss: 1.0861
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.2117
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4278 - loss: 1.2013 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4296 - loss: 1.2011
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4333 - loss: 1.1961
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.1925
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4365 - loss: 1.1922 - val_accuracy: 0.4392 - val_loss: 1.1118
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.1992
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4299 - loss: 1.2012 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.1937
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4437 - loss: 1.1890
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4454 - loss: 1.1855
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4456 - loss: 1.1852 - val_accuracy: 0.5200 - val_loss: 1.0734
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.1627
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4562 - loss: 1.1707 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4607 - loss: 1.1591
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4613 - loss: 1.1569
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4615 - loss: 1.1562 - val_accuracy: 0.4901 - val_loss: 1.0883
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1207
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4504 - loss: 1.1697 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1637
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4604 - loss: 1.1633
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4613 - loss: 1.1632 - val_accuracy: 0.5043 - val_loss: 1.0678
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0942
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1353 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4749 - loss: 1.1343
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1364
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4731 - loss: 1.1374
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4731 - loss: 1.1376 - val_accuracy: 0.5204 - val_loss: 1.0465
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2663
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4661 - loss: 1.1501 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4740 - loss: 1.1464
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4766 - loss: 1.1442
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4775 - loss: 1.1428 - val_accuracy: 0.5240 - val_loss: 1.0324
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.2003
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4910 - loss: 1.1354 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4905 - loss: 1.1310
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4889 - loss: 1.1317
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4880 - loss: 1.1321
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4880 - loss: 1.1321 - val_accuracy: 0.5296 - val_loss: 1.0243
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1976
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4695 - loss: 1.1288 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4772 - loss: 1.1246
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4799 - loss: 1.1255
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4810 - loss: 1.1260 - val_accuracy: 0.5466 - val_loss: 1.0174
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4844 - loss: 1.2377
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4933 - loss: 1.1376 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1317
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4882 - loss: 1.1284
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4892 - loss: 1.1262 - val_accuracy: 0.5082 - val_loss: 1.0500
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1455
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4848 - loss: 1.1251 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.1290
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1281
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4901 - loss: 1.1264
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4903 - loss: 1.1262 - val_accuracy: 0.5378 - val_loss: 1.0116
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.1902
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4807 - loss: 1.1302 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4884 - loss: 1.1251
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4897 - loss: 1.1214
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4905 - loss: 1.1196 - val_accuracy: 0.5112 - val_loss: 1.0361
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.1403
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5021 - loss: 1.1099 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1107
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5004 - loss: 1.1100
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5007 - loss: 1.1094 - val_accuracy: 0.5263 - val_loss: 1.0185
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2039
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5016 - loss: 1.1187 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5022 - loss: 1.1128
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1099
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5042 - loss: 1.1090
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5043 - loss: 1.1090 - val_accuracy: 0.5391 - val_loss: 0.9906
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0313
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5223 - loss: 1.0811 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5167 - loss: 1.0922
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5149 - loss: 1.0933
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5136 - loss: 1.0931 - val_accuracy: 0.5526 - val_loss: 0.9969
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9776
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0773 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5254 - loss: 1.0841
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5203 - loss: 1.0871
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5174 - loss: 1.0879 - val_accuracy: 0.5158 - val_loss: 1.0274
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.4167
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.1218 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.1022
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0958
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5117 - loss: 1.0928 - val_accuracy: 0.5384 - val_loss: 0.9920
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2934
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5062 - loss: 1.0894 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0844
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5129 - loss: 1.0826
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5143 - loss: 1.0814 - val_accuracy: 0.5631 - val_loss: 0.9648
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5000 - loss: 1.1463
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5105 - loss: 1.0823 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5119 - loss: 1.0768
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5140 - loss: 1.0735
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5150 - loss: 1.0722 - val_accuracy: 0.5506 - val_loss: 0.9719
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0735
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5192 - loss: 1.0853 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5255 - loss: 1.0755
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5270 - loss: 1.0705
[1m160/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5282 - loss: 1.0671
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5283 - loss: 1.0667 - val_accuracy: 0.5535 - val_loss: 0.9593
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 0.9911
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0582 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5210 - loss: 1.0578
[1m116/167[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0563
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5228 - loss: 1.0570
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5230 - loss: 1.0571 - val_accuracy: 0.5539 - val_loss: 0.9466
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.2690
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5037 - loss: 1.1032 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5173 - loss: 1.0833
[1m119/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0755
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5242 - loss: 1.0715
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5245 - loss: 1.0708 - val_accuracy: 0.5683 - val_loss: 0.9324
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0374
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0425 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5222 - loss: 1.0509
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0544
[1m158/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.5238 - loss: 1.0557
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5242 - loss: 1.0560 - val_accuracy: 0.5736 - val_loss: 0.9466
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 1.0860
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5302 - loss: 1.0576 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0562
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5313 - loss: 1.0538
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0510
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5327 - loss: 1.0507 - val_accuracy: 0.5614 - val_loss: 0.9549
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9360
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5652 - loss: 1.0128 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5574 - loss: 1.0250
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 1.0300
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5516 - loss: 1.0321 - val_accuracy: 0.5443 - val_loss: 0.9521
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4062 - loss: 1.2719
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0638 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5323 - loss: 1.0591
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5325 - loss: 1.0571
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5330 - loss: 1.0541 - val_accuracy: 0.5657 - val_loss: 0.9228
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0728
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5508 - loss: 1.0311 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5507 - loss: 1.0288
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5495 - loss: 1.0281
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5492 - loss: 1.0286 - val_accuracy: 0.5736 - val_loss: 0.9478
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 0.9481
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0404 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5404 - loss: 1.0382
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0371
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5399 - loss: 1.0368 - val_accuracy: 0.5854 - val_loss: 0.9003
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1219
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5318 - loss: 1.0322 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5364 - loss: 1.0308
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5397 - loss: 1.0296
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5411 - loss: 1.0287 - val_accuracy: 0.5864 - val_loss: 0.9071
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9753
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5549 - loss: 1.0141 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5497 - loss: 1.0179
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5487 - loss: 1.0182
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5490 - loss: 1.0181 - val_accuracy: 0.5844 - val_loss: 0.8998
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.0090
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5473 - loss: 1.0165 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5464 - loss: 1.0198
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5459 - loss: 1.0202
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5462 - loss: 1.0204 - val_accuracy: 0.5857 - val_loss: 0.9052
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 0.9401
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5422 - loss: 1.0207 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5474 - loss: 1.0159
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0137
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5513 - loss: 1.0129
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5515 - loss: 1.0130 - val_accuracy: 0.5874 - val_loss: 0.8962
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.0758
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0222 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5412 - loss: 1.0268
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5429 - loss: 1.0242
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5445 - loss: 1.0222 - val_accuracy: 0.5874 - val_loss: 0.8836
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.0371
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5642 - loss: 1.0285 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5668 - loss: 1.0190
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5637 - loss: 1.0167
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5615 - loss: 1.0157 - val_accuracy: 0.5716 - val_loss: 0.9121
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0680
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5564 - loss: 1.0379 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5533 - loss: 1.0292
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5536 - loss: 1.0255
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5546 - loss: 1.0223
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5546 - loss: 1.0222 - val_accuracy: 0.5660 - val_loss: 0.9031
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 0.9589
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0183 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5474 - loss: 1.0183
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5498 - loss: 1.0175
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5504 - loss: 1.0163 - val_accuracy: 0.4648 - val_loss: 1.1875
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.3074
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5221 - loss: 1.0621 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0393
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0317
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5456 - loss: 1.0274 - val_accuracy: 0.5808 - val_loss: 0.9028
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9872
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5396 - loss: 1.0147 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5450 - loss: 1.0054
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0006
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5491 - loss: 0.9985 - val_accuracy: 0.5861 - val_loss: 0.8964

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m24s[0m 505ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 11ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 11ms/step
Saved model to disk.

Accuracy capturado en la ejecución 27: 49.71 [%]
F1-score capturado en la ejecución 27: 48.02 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:53[0m 704ms/step
[1m 62/333[0m [32m━━━[0m[37m━━━━━━━━━━━━━━━━━[0m [1m0s[0m 825us/step  
[1m137/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 738us/step
[1m206/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 737us/step
[1m280/333[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 721us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 16ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m70/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 725us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 58.61 [%]
Global F1 score (validation) = 58.56 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.30079708 0.2340859  0.22899508 0.23612195]
 [0.285339   0.21781717 0.2693062  0.22753763]
 [0.38197133 0.33342022 0.07964946 0.20495905]
 ...
 [0.04125523 0.02455017 0.9136105  0.02058417]
 [0.03460662 0.02056938 0.92712104 0.01770294]
 [0.03483479 0.02072219 0.92659205 0.01785101]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 62.13 [%]
Global accuracy score (test) = 55.99 [%]
Global F1 score (train) = 61.96 [%]
Global F1 score (test) = 55.55 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.32      0.36       400
MODERATE-INTENSITY       0.49      0.47      0.48       400
         SEDENTARY       0.56      0.88      0.68       400
VIGOROUS-INTENSITY       0.90      0.57      0.70       345

          accuracy                           0.56      1545
         macro avg       0.59      0.56      0.56      1545
      weighted avg       0.58      0.56      0.55      1545

2025-11-05 11:33:43.025938: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:33:43.037797: 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 00:00:1762338823.052207 3362813 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338823.056494 3362813 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338823.067270 3362813 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338823.067291 3362813 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338823.067292 3362813 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338823.067294 3362813 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:33:43.070482: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338825.287572 3362813 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338826.633711 3362949 service.cc:152] XLA service 0x7763e801b820 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338826.633741 3362949 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:33:46.666911: 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 00:00:1762338826.783636 3362949 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338828.898025 3362949 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:17[0m 3s/step - accuracy: 0.1406 - loss: 3.0432
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2606 - loss: 2.2097 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2716 - loss: 2.1131
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2757 - loss: 2.0560
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2792 - loss: 2.0080
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2793 - loss: 2.0069 - val_accuracy: 0.3827 - val_loss: 1.2545
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.4570
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.5037 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3191 - loss: 1.4935
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3200 - loss: 1.4819
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.3216 - loss: 1.4702
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3216 - loss: 1.4699 - val_accuracy: 0.4001 - val_loss: 1.2526
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2500 - loss: 1.3795
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3275 - loss: 1.3710 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3322 - loss: 1.3671
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3362 - loss: 1.3622
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3385 - loss: 1.3583 - val_accuracy: 0.3847 - val_loss: 1.2591
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.3125 - loss: 1.3773
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3502 - loss: 1.3369 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3526 - loss: 1.3352
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3541 - loss: 1.3324
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3562 - loss: 1.3294 - val_accuracy: 0.4031 - val_loss: 1.2454
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.3053
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3784 - loss: 1.2984 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3729 - loss: 1.3008
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3691 - loss: 1.3005
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3673 - loss: 1.2999
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3671 - loss: 1.2999 - val_accuracy: 0.3876 - val_loss: 1.2346
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3281 - loss: 1.2765
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3842 - loss: 1.2844 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3820 - loss: 1.2847
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3803 - loss: 1.2846
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3794 - loss: 1.2840 - val_accuracy: 0.3899 - val_loss: 1.2268
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3438 - loss: 1.2809
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3661 - loss: 1.2698 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3716 - loss: 1.2690
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3741 - loss: 1.2685
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3757 - loss: 1.2681
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3759 - loss: 1.2681 - val_accuracy: 0.3890 - val_loss: 1.2017
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4062 - loss: 1.1335
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4036 - loss: 1.2295 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3979 - loss: 1.2435
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3960 - loss: 1.2487
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3946 - loss: 1.2503 - val_accuracy: 0.4097 - val_loss: 1.1925
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4688 - loss: 1.1803
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3842 - loss: 1.2513 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3863 - loss: 1.2484
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3895 - loss: 1.2443
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3917 - loss: 1.2420
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3918 - loss: 1.2419 - val_accuracy: 0.4169 - val_loss: 1.1683
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1791
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3841 - loss: 1.2365 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3910 - loss: 1.2350
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3942 - loss: 1.2342
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3956 - loss: 1.2332 - val_accuracy: 0.4382 - val_loss: 1.1390
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1296
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4261 - loss: 1.1954 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4192 - loss: 1.1980
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4167 - loss: 1.2003
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4162 - loss: 1.2016 - val_accuracy: 0.4258 - val_loss: 1.1573
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3594 - loss: 1.3277
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4122 - loss: 1.2268 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4155 - loss: 1.2202
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4164 - loss: 1.2152
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4166 - loss: 1.2125
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4166 - loss: 1.2124 - val_accuracy: 0.4455 - val_loss: 1.1259
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4219 - loss: 1.1796
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4210 - loss: 1.1927 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4176 - loss: 1.1957
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4174 - loss: 1.1972
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4181 - loss: 1.1966
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4182 - loss: 1.1964 - val_accuracy: 0.4330 - val_loss: 1.1414
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.2129
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4189 - loss: 1.1793 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4258 - loss: 1.1791
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4277 - loss: 1.1799
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4279 - loss: 1.1811
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4279 - loss: 1.1813 - val_accuracy: 0.4547 - val_loss: 1.1083
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1789
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4279 - loss: 1.1709 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4302 - loss: 1.1720
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4298 - loss: 1.1737
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4302 - loss: 1.1743
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4302 - loss: 1.1744 - val_accuracy: 0.4645 - val_loss: 1.1221
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.3189
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4292 - loss: 1.1497 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4338 - loss: 1.1573
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4361 - loss: 1.1634
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4374 - loss: 1.1672
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4375 - loss: 1.1674 - val_accuracy: 0.4455 - val_loss: 1.1139
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1465
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4627 - loss: 1.1532 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4487 - loss: 1.1629
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4424 - loss: 1.1687
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4397 - loss: 1.1704
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4395 - loss: 1.1705 - val_accuracy: 0.4359 - val_loss: 1.1567
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4844 - loss: 1.1336
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4411 - loss: 1.1885 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4392 - loss: 1.1795
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4410 - loss: 1.1753
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4424 - loss: 1.1735 - val_accuracy: 0.4740 - val_loss: 1.0905
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3438 - loss: 1.2430
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4344 - loss: 1.1805 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4445 - loss: 1.1644
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.1609
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4477 - loss: 1.1595
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4477 - loss: 1.1595 - val_accuracy: 0.4892 - val_loss: 1.0844
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3906 - loss: 1.1912
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4384 - loss: 1.1683 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4429 - loss: 1.1650
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4446 - loss: 1.1624
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4456 - loss: 1.1599 - val_accuracy: 0.4921 - val_loss: 1.0870
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5469 - loss: 0.9570
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4662 - loss: 1.1333 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4638 - loss: 1.1362
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4622 - loss: 1.1392
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1416
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4610 - loss: 1.1418 - val_accuracy: 0.4478 - val_loss: 1.1020
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.4844 - loss: 1.2367
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4240 - loss: 1.1821 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4329 - loss: 1.1686
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4401 - loss: 1.1601
[1m157/167[0m [32m━━━━━━━━━━━━━━━━━━[0m[37m━━[0m [1m0s[0m 1ms/step - accuracy: 0.4428 - loss: 1.1571
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4434 - loss: 1.1565 - val_accuracy: 0.4537 - val_loss: 1.0986
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1699
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4416 - loss: 1.1678 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4464 - loss: 1.1575
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.1530
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4497 - loss: 1.1498
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4498 - loss: 1.1496 - val_accuracy: 0.4763 - val_loss: 1.0710
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1934
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1099 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4760 - loss: 1.1169
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4736 - loss: 1.1234
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4714 - loss: 1.1271
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4712 - loss: 1.1274 - val_accuracy: 0.4754 - val_loss: 1.0902
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.2576
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4808 - loss: 1.1275 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1270
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4791 - loss: 1.1249
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4775 - loss: 1.1252 - val_accuracy: 0.5046 - val_loss: 1.0534
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1649
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4886 - loss: 1.1294 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4842 - loss: 1.1277
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4832 - loss: 1.1266
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4825 - loss: 1.1260 - val_accuracy: 0.4675 - val_loss: 1.1067
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5625 - loss: 1.0310
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4630 - loss: 1.1180 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1251
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4688 - loss: 1.1247
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4702 - loss: 1.1239 - val_accuracy: 0.5046 - val_loss: 1.0444
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.1924
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4955 - loss: 1.1281 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4894 - loss: 1.1240
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4871 - loss: 1.1217
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4852 - loss: 1.1201
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4851 - loss: 1.1201 - val_accuracy: 0.5184 - val_loss: 1.0330
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1122
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5038 - loss: 1.0953 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4963 - loss: 1.0974
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4918 - loss: 1.1006
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4895 - loss: 1.1020 - val_accuracy: 0.4898 - val_loss: 1.0986
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1038
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.1091 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4767 - loss: 1.1092
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4785 - loss: 1.1086
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4810 - loss: 1.1076
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4813 - loss: 1.1075 - val_accuracy: 0.4964 - val_loss: 1.0742
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1619
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4798 - loss: 1.1153 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1107
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4869 - loss: 1.1071
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4888 - loss: 1.1046
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4890 - loss: 1.1044 - val_accuracy: 0.5246 - val_loss: 1.0175
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0698
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4668 - loss: 1.1038 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4751 - loss: 1.1004
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4795 - loss: 1.0998
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.0993
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4828 - loss: 1.0992 - val_accuracy: 0.5315 - val_loss: 1.0223
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0932
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5116 - loss: 1.0560 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5086 - loss: 1.0707
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5075 - loss: 1.0750
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5063 - loss: 1.0773 - val_accuracy: 0.5365 - val_loss: 0.9815
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5938 - loss: 0.9874
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4981 - loss: 1.0896 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4952 - loss: 1.0866
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4964 - loss: 1.0849
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4967 - loss: 1.0851
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4968 - loss: 1.0851 - val_accuracy: 0.5306 - val_loss: 1.0094
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1721
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5159 - loss: 1.0829 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5141 - loss: 1.0822
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0826
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5118 - loss: 1.0825 - val_accuracy: 0.5069 - val_loss: 1.0211
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1200
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4865 - loss: 1.0944 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4969 - loss: 1.0808
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4990 - loss: 1.0777
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4997 - loss: 1.0777 - val_accuracy: 0.5345 - val_loss: 1.0039
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.5312 - loss: 1.0064
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0728 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5244 - loss: 1.0675
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5220 - loss: 1.0672
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5213 - loss: 1.0665
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5212 - loss: 1.0665 - val_accuracy: 0.5420 - val_loss: 0.9981
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.0596
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5150 - loss: 1.0834 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5166 - loss: 1.0727
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0696
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5169 - loss: 1.0687
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5169 - loss: 1.0687 - val_accuracy: 0.5486 - val_loss: 0.9681
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5312 - loss: 1.0086
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4825 - loss: 1.0902 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4872 - loss: 1.0866
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4920 - loss: 1.0809
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4949 - loss: 1.0774
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4951 - loss: 1.0771 - val_accuracy: 0.5509 - val_loss: 0.9730
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0257
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4931 - loss: 1.0677 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5049 - loss: 1.0591
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5096 - loss: 1.0558
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5115 - loss: 1.0553 - val_accuracy: 0.5246 - val_loss: 1.0003
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0920
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0558 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5324 - loss: 1.0504
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5290 - loss: 1.0502
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5274 - loss: 1.0513 - val_accuracy: 0.5118 - val_loss: 1.0408
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.1059
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5078 - loss: 1.0822 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5143 - loss: 1.0719
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5156 - loss: 1.0694
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5170 - loss: 1.0677 - val_accuracy: 0.5608 - val_loss: 0.9586
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0672
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4936 - loss: 1.0944 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5090 - loss: 1.0775
[1m132/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0695
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5181 - loss: 1.0655 - val_accuracy: 0.5483 - val_loss: 0.9671
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.1065
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0534 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5402 - loss: 1.0477
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5375 - loss: 1.0451
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5355 - loss: 1.0442 - val_accuracy: 0.5562 - val_loss: 0.9784
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5781 - loss: 1.0668
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5420 - loss: 1.0328 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5392 - loss: 1.0344
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5356 - loss: 1.0375
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5335 - loss: 1.0396 - val_accuracy: 0.5079 - val_loss: 1.0472
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0707
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5224 - loss: 1.0446 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0447
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5341 - loss: 1.0414
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5356 - loss: 1.0398 - val_accuracy: 0.5815 - val_loss: 0.9366
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 1.0518
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5311 - loss: 1.0408 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5339 - loss: 1.0369
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0341
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5373 - loss: 1.0323 - val_accuracy: 0.5742 - val_loss: 0.9391
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1579
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0355 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0301
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5338 - loss: 1.0286
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5353 - loss: 1.0281 - val_accuracy: 0.5644 - val_loss: 0.9297
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5000 - loss: 0.9932
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5400 - loss: 1.0053 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5432 - loss: 1.0018
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0036
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0068
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5438 - loss: 1.0069 - val_accuracy: 0.5933 - val_loss: 0.9145
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1253
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5435 - loss: 1.0396 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5478 - loss: 1.0231
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5481 - loss: 1.0175
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5474 - loss: 1.0173 - val_accuracy: 0.5841 - val_loss: 0.9177
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6094 - loss: 0.9363
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 0.9892 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5414 - loss: 0.9962
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5411 - loss: 1.0017
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5415 - loss: 1.0042 - val_accuracy: 0.5857 - val_loss: 0.9015
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9322
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5351 - loss: 1.0097 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5389 - loss: 1.0069
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5408 - loss: 1.0102
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5418 - loss: 1.0121 - val_accuracy: 0.5841 - val_loss: 0.8894
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.6406 - loss: 0.7714
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5512 - loss: 0.9963 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5493 - loss: 1.0006
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5485 - loss: 1.0040
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5479 - loss: 1.0072 - val_accuracy: 0.5848 - val_loss: 0.9156
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0302
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5457 - loss: 1.0080 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5483 - loss: 1.0097
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5480 - loss: 1.0123
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5480 - loss: 1.0132 - val_accuracy: 0.5647 - val_loss: 0.9486
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.2189
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5373 - loss: 1.0383 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5418 - loss: 1.0213
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5441 - loss: 1.0158
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5450 - loss: 1.0134 - val_accuracy: 0.5910 - val_loss: 0.9281
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6250 - loss: 1.0134
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5637 - loss: 0.9746 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5632 - loss: 0.9784
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5628 - loss: 0.9805
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5622 - loss: 0.9826 - val_accuracy: 0.5877 - val_loss: 0.9167
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6406 - loss: 0.9589
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5437 - loss: 1.0340 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5458 - loss: 1.0220
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5475 - loss: 1.0154
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5479 - loss: 1.0133 - val_accuracy: 0.6002 - val_loss: 0.8882
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4688 - loss: 1.1258
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5438 - loss: 1.0159 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5525 - loss: 0.9999
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 0.9978
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5538 - loss: 0.9977
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5538 - loss: 0.9977 - val_accuracy: 0.5457 - val_loss: 0.9503
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1326
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5343 - loss: 1.0158 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0175
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5395 - loss: 1.0127
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5421 - loss: 1.0078 - val_accuracy: 0.5752 - val_loss: 0.9318
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5469 - loss: 0.9574
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5539 - loss: 0.9903 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5546 - loss: 0.9918
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5536 - loss: 0.9934
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5531 - loss: 0.9943 - val_accuracy: 0.5913 - val_loss: 0.8904
Epoch 61/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0904
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5482 - loss: 1.0055 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5548 - loss: 0.9938
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5559 - loss: 0.9920
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5571 - loss: 0.9903
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5571 - loss: 0.9903 - val_accuracy: 0.5713 - val_loss: 0.9245
Epoch 62/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 26ms/step - accuracy: 0.5781 - loss: 0.8973
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5558 - loss: 0.9828 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5547 - loss: 0.9872
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5544 - loss: 0.9875
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5551 - loss: 0.9872
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5552 - loss: 0.9872 - val_accuracy: 0.5976 - val_loss: 0.8744
Epoch 63/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.0781
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5699 - loss: 0.9829 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5676 - loss: 0.9862
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5659 - loss: 0.9840
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5650 - loss: 0.9822
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5649 - loss: 0.9821 - val_accuracy: 0.5917 - val_loss: 0.8962
Epoch 64/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6250 - loss: 0.8723
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5517 - loss: 1.0048 
[1m 90/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5502 - loss: 1.0052
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5504 - loss: 1.0025
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5516 - loss: 0.9991 - val_accuracy: 0.6045 - val_loss: 0.8741
Epoch 65/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.9653
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5677 - loss: 0.9783 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5651 - loss: 0.9783
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5632 - loss: 0.9786
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5624 - loss: 0.9796 - val_accuracy: 0.5769 - val_loss: 0.9195
Epoch 66/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0587
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5598 - loss: 0.9900 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5586 - loss: 0.9835
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5593 - loss: 0.9790
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5597 - loss: 0.9774 - val_accuracy: 0.5667 - val_loss: 0.9046
Epoch 67/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6875 - loss: 0.8907
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5807 - loss: 0.9841 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5770 - loss: 0.9799
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5751 - loss: 0.9793
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5738 - loss: 0.9795
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5738 - loss: 0.9795 - val_accuracy: 0.5854 - val_loss: 0.8865
Epoch 68/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 0.8322
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5729 - loss: 0.9437 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5720 - loss: 0.9505
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5720 - loss: 0.9545
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5713 - loss: 0.9584
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5712 - loss: 0.9585 - val_accuracy: 0.5979 - val_loss: 0.8742
Epoch 69/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.8996
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5712 - loss: 0.9623 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5658 - loss: 0.9659
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5647 - loss: 0.9651
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5641 - loss: 0.9653
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5640 - loss: 0.9653 - val_accuracy: 0.5818 - val_loss: 0.8910

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m22s[0m 475ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 28: 55.99 [%]
F1-score capturado en la ejecución 28: 55.55 [%]

=== 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}
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:43[0m 674ms/step
[1m 67/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 763us/step  
[1m140/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 727us/step
[1m216/333[0m [32m━━━━━━━━━━━━[0m[37m━━━━━━━━[0m [1m0s[0m 703us/step
[1m291/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 695us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 16ms/step
[1m67/96[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 758us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 5ms/step
Global accuracy score (validation) = 58.77 [%]
Global F1 score (validation) = 58.72 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.42304662 0.39802665 0.03810171 0.14082503]
 [0.43092597 0.43025085 0.02494904 0.11387411]
 [0.3605813  0.29350862 0.15199922 0.19391094]
 ...
 [0.01420952 0.00745884 0.9709527  0.00737899]
 [0.02060765 0.01126655 0.95757073 0.01055512]
 [0.02036638 0.01116486 0.95788765 0.01058113]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 61.15 [%]
Global accuracy score (test) = 56.12 [%]
Global F1 score (train) = 61.17 [%]
Global F1 score (test) = 56.06 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.41      0.34      0.37       400
MODERATE-INTENSITY       0.50      0.54      0.52       400
         SEDENTARY       0.56      0.84      0.67       400
VIGOROUS-INTENSITY       0.98      0.52      0.68       345

          accuracy                           0.56      1545
         macro avg       0.61      0.56      0.56      1545
      weighted avg       0.60      0.56      0.56      1545

2025-11-05 11:34:24.772244: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-05 11:34:24.783974: 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 00:00:1762338864.798050 3370112 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762338864.802392 3370112 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762338864.812450 3370112 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338864.812468 3370112 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338864.812469 3370112 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762338864.812470 3370112 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-05 11:34:24.815402: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.
  warnings.warn(
I0000 00:00:1762338867.051682 3370112 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..
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/145
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1762338868.454247 3370220 service.cc:152] XLA service 0x73851000b8b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1762338868.454302 3370220 service.cc:160]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9
2025-11-05 11:34:28.489907: 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 00:00:1762338868.621560 3370220 cuda_dnn.cc:529] Loaded cuDNN version 91002
I0000 00:00:1762338870.740157 3370220 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m8:25[0m 3s/step - accuracy: 0.3750 - loss: 2.4677
[1m 34/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 2ms/step - accuracy: 0.2906 - loss: 2.3446 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2857 - loss: 2.2580
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.2855 - loss: 2.1970
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.2868 - loss: 2.1410
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 12ms/step - accuracy: 0.2871 - loss: 2.1310
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m7s[0m 22ms/step - accuracy: 0.2871 - loss: 2.1298 - val_accuracy: 0.3972 - val_loss: 1.2579
Epoch 2/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.2500 - loss: 1.4938
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3078 - loss: 1.5799 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3113 - loss: 1.5615
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3123 - loss: 1.5459
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3137 - loss: 1.5311
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3139 - loss: 1.5295 - val_accuracy: 0.4034 - val_loss: 1.2679
Epoch 3/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.3974
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3280 - loss: 1.3656 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3316 - loss: 1.3648
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3311 - loss: 1.3661
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3314 - loss: 1.3661 - val_accuracy: 0.3863 - val_loss: 1.2655
Epoch 4/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3125 - loss: 1.3875
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3452 - loss: 1.3394 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3489 - loss: 1.3345
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3511 - loss: 1.3319
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3522 - loss: 1.3304
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3522 - loss: 1.3303 - val_accuracy: 0.4120 - val_loss: 1.2461
Epoch 5/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3594 - loss: 1.3871
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3560 - loss: 1.3438 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3592 - loss: 1.3309
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3606 - loss: 1.3241
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3609 - loss: 1.3199 - val_accuracy: 0.3913 - val_loss: 1.2366
Epoch 6/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 24ms/step - accuracy: 0.3281 - loss: 1.2733
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3849 - loss: 1.2771 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3762 - loss: 1.2857
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3746 - loss: 1.2887
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3743 - loss: 1.2888 - val_accuracy: 0.3926 - val_loss: 1.2287
Epoch 7/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.2812 - loss: 1.2682
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3651 - loss: 1.2741 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3707 - loss: 1.2751
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3721 - loss: 1.2750
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.3733 - loss: 1.2750
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3734 - loss: 1.2749 - val_accuracy: 0.4077 - val_loss: 1.2135
Epoch 8/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3281 - loss: 1.3728
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3816 - loss: 1.2643 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3852 - loss: 1.2633
[1m134/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3844 - loss: 1.2630
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3853 - loss: 1.2616 - val_accuracy: 0.4386 - val_loss: 1.1855
Epoch 9/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.3750 - loss: 1.2738
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4043 - loss: 1.2419 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4058 - loss: 1.2391
[1m120/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4047 - loss: 1.2402
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4051 - loss: 1.2393
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4051 - loss: 1.2392 - val_accuracy: 0.4310 - val_loss: 1.1695
Epoch 10/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2350
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3826 - loss: 1.2444 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3889 - loss: 1.2396
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3935 - loss: 1.2360
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3959 - loss: 1.2338 - val_accuracy: 0.4520 - val_loss: 1.1724
Epoch 11/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.3225
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3812 - loss: 1.2453 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3931 - loss: 1.2334
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.3963 - loss: 1.2299
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.3986 - loss: 1.2278 - val_accuracy: 0.4560 - val_loss: 1.1439
Epoch 12/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3750 - loss: 1.2316
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4183 - loss: 1.2091 
[1m 78/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4186 - loss: 1.2120
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4171 - loss: 1.2141
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4163 - loss: 1.2146
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4163 - loss: 1.2146 - val_accuracy: 0.4379 - val_loss: 1.1493
Epoch 13/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.3750 - loss: 1.2682
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4061 - loss: 1.2207 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4134 - loss: 1.2139
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4151 - loss: 1.2122
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4163 - loss: 1.2105 - val_accuracy: 0.4005 - val_loss: 1.2448
Epoch 14/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.2969 - loss: 1.2486
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4324 - loss: 1.1814 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4343 - loss: 1.1848
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4323 - loss: 1.1878
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4312 - loss: 1.1899
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4312 - loss: 1.1899 - val_accuracy: 0.4655 - val_loss: 1.1336
Epoch 15/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5156 - loss: 1.0881
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4416 - loss: 1.2044 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4382 - loss: 1.1975
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4377 - loss: 1.1938
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4368 - loss: 1.1928
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4368 - loss: 1.1928 - val_accuracy: 0.4202 - val_loss: 1.1546
Epoch 16/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.3750 - loss: 1.1534
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4177 - loss: 1.1822 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4265 - loss: 1.1801
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4293 - loss: 1.1795
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4312 - loss: 1.1790 - val_accuracy: 0.4576 - val_loss: 1.1284
Epoch 17/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.3750 - loss: 1.2302
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4513 - loss: 1.1723 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4483 - loss: 1.1780
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4473 - loss: 1.1774
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4463 - loss: 1.1774
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4462 - loss: 1.1774 - val_accuracy: 0.5059 - val_loss: 1.0918
Epoch 18/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.2186
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4447 - loss: 1.2001 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4439 - loss: 1.1876
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4441 - loss: 1.1816
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4446 - loss: 1.1791
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4446 - loss: 1.1790 - val_accuracy: 0.4895 - val_loss: 1.0941
Epoch 19/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.3594 - loss: 1.2067
[1m 36/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4587 - loss: 1.1771 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4572 - loss: 1.1707
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4555 - loss: 1.1703
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4550 - loss: 1.1684
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4550 - loss: 1.1684 - val_accuracy: 0.4931 - val_loss: 1.0941
Epoch 20/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.0844
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4499 - loss: 1.1741 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4537 - loss: 1.1709
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4567 - loss: 1.1658
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4578 - loss: 1.1638 - val_accuracy: 0.5026 - val_loss: 1.0981
Epoch 21/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4531 - loss: 1.1591
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4854 - loss: 1.1424 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4797 - loss: 1.1437
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4761 - loss: 1.1451
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4748 - loss: 1.1461 - val_accuracy: 0.5325 - val_loss: 1.0484
Epoch 22/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4219 - loss: 1.1729
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4565 - loss: 1.1402 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4611 - loss: 1.1423
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4621 - loss: 1.1440
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4639 - loss: 1.1436
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4640 - loss: 1.1436 - val_accuracy: 0.5332 - val_loss: 1.0419
Epoch 23/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.0830
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5008 - loss: 1.1020 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4899 - loss: 1.1202
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4870 - loss: 1.1250
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.4853 - loss: 1.1273
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4852 - loss: 1.1273 - val_accuracy: 0.5151 - val_loss: 1.0621
Epoch 24/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.2831
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4781 - loss: 1.1510 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1412
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4824 - loss: 1.1358
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4829 - loss: 1.1333 - val_accuracy: 0.5246 - val_loss: 1.0483
Epoch 25/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4375 - loss: 1.1811
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4690 - loss: 1.1426 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4762 - loss: 1.1341
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4775 - loss: 1.1321
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4787 - loss: 1.1316
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4788 - loss: 1.1315 - val_accuracy: 0.5306 - val_loss: 1.0468
Epoch 26/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 25ms/step - accuracy: 0.5938 - loss: 1.0209
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4780 - loss: 1.1189 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4786 - loss: 1.1231
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4809 - loss: 1.1227
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4817 - loss: 1.1221 - val_accuracy: 0.5260 - val_loss: 1.0343
Epoch 27/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0119
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5152 - loss: 1.0844 
[1m 77/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5079 - loss: 1.0922
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5023 - loss: 1.0976
[1m159/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4989 - loss: 1.1018
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4984 - loss: 1.1025 - val_accuracy: 0.5010 - val_loss: 1.0586
Epoch 28/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4062 - loss: 1.2114
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4862 - loss: 1.1162 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4906 - loss: 1.1137
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4913 - loss: 1.1163
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4918 - loss: 1.1167 - val_accuracy: 0.5312 - val_loss: 1.0208
Epoch 29/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5156 - loss: 1.0762
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.1173 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4958 - loss: 1.1141
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4974 - loss: 1.1112
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4979 - loss: 1.1097 - val_accuracy: 0.5279 - val_loss: 1.0149
Epoch 30/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5000 - loss: 1.0908
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4951 - loss: 1.0949 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4953 - loss: 1.0985
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.0990
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.4956 - loss: 1.0994
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.4956 - loss: 1.0995 - val_accuracy: 0.5621 - val_loss: 0.9934
Epoch 31/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 1.0030
[1m 39/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5170 - loss: 1.0950 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5124 - loss: 1.0933
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5113 - loss: 1.0916
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5102 - loss: 1.0915
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5102 - loss: 1.0915 - val_accuracy: 0.5062 - val_loss: 1.0503
Epoch 32/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4844 - loss: 1.1034
[1m 35/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5009 - loss: 1.1078 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5065 - loss: 1.0980
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5084 - loss: 1.0952
[1m161/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5098 - loss: 1.0944
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5101 - loss: 1.0941 - val_accuracy: 0.5246 - val_loss: 0.9953
Epoch 33/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.0227
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5154 - loss: 1.0941 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5120 - loss: 1.0958
[1m126/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5107 - loss: 1.0968
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5101 - loss: 1.0962
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5101 - loss: 1.0962 - val_accuracy: 0.5378 - val_loss: 0.9975
Epoch 34/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.5000 - loss: 1.2268
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5087 - loss: 1.1131 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5114 - loss: 1.0995
[1m133/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5132 - loss: 1.0908
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5140 - loss: 1.0876 - val_accuracy: 0.5670 - val_loss: 0.9580
Epoch 35/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5469 - loss: 1.0676
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5386 - loss: 1.0524 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5363 - loss: 1.0521
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5331 - loss: 1.0539
[1m163/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5317 - loss: 1.0551
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5315 - loss: 1.0554 - val_accuracy: 0.5591 - val_loss: 0.9607
Epoch 36/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1772
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0575 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5295 - loss: 1.0612
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5293 - loss: 1.0601
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5292 - loss: 1.0592 - val_accuracy: 0.5378 - val_loss: 0.9940
Epoch 37/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5625 - loss: 0.9869
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0452 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5436 - loss: 1.0537
[1m125/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5403 - loss: 1.0561
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5373 - loss: 1.0587
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5373 - loss: 1.0588 - val_accuracy: 0.5627 - val_loss: 0.9694
Epoch 38/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6094 - loss: 0.9440
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5594 - loss: 1.0402 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5501 - loss: 1.0499
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5454 - loss: 1.0534
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5426 - loss: 1.0546
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5426 - loss: 1.0546 - val_accuracy: 0.5703 - val_loss: 0.9448
Epoch 39/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.4375 - loss: 1.0655
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5286 - loss: 1.0232 
[1m 82/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5327 - loss: 1.0320
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0386
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5341 - loss: 1.0414 - val_accuracy: 0.5621 - val_loss: 0.9463
Epoch 40/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.1825
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5268 - loss: 1.0622 
[1m 85/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5357 - loss: 1.0508
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5370 - loss: 1.0473
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5364 - loss: 1.0468 - val_accuracy: 0.5611 - val_loss: 0.9382
Epoch 41/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.1439
[1m 46/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0608 
[1m 91/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5329 - loss: 1.0537
[1m137/167[0m [32m━━━━━━━━━━━━━━━━[0m[37m━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5335 - loss: 1.0515
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5336 - loss: 1.0506 - val_accuracy: 0.5542 - val_loss: 0.9499
Epoch 42/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5156 - loss: 1.2694
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5350 - loss: 1.0759 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0603
[1m130/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5367 - loss: 1.0512
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5379 - loss: 1.0471 - val_accuracy: 0.5788 - val_loss: 0.9263
Epoch 43/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.6406 - loss: 0.9320
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5468 - loss: 1.0155 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5460 - loss: 1.0208
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5447 - loss: 1.0241
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5440 - loss: 1.0260
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5440 - loss: 1.0260 - val_accuracy: 0.5838 - val_loss: 0.9102
Epoch 44/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0799
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5463 - loss: 1.0041 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5430 - loss: 1.0151
[1m123/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5425 - loss: 1.0192
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5428 - loss: 1.0195 - val_accuracy: 0.5995 - val_loss: 0.8979
Epoch 45/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4531 - loss: 1.0293
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5204 - loss: 1.0361 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5283 - loss: 1.0326
[1m118/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5342 - loss: 1.0300
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5376 - loss: 1.0285
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5380 - loss: 1.0284 - val_accuracy: 0.5759 - val_loss: 0.8977
Epoch 46/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.2220
[1m 37/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5309 - loss: 1.0428 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5406 - loss: 1.0281
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5444 - loss: 1.0234
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5459 - loss: 1.0208
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5460 - loss: 1.0207 - val_accuracy: 0.5512 - val_loss: 0.9292
Epoch 47/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 22ms/step - accuracy: 0.4844 - loss: 1.1983
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5506 - loss: 1.0255 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5537 - loss: 1.0143
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5547 - loss: 1.0136
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5555 - loss: 1.0123 - val_accuracy: 0.5687 - val_loss: 0.9156
Epoch 48/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 23ms/step - accuracy: 0.4062 - loss: 1.2519
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5333 - loss: 1.0220 
[1m 84/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0146
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5431 - loss: 1.0151
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5440 - loss: 1.0148 - val_accuracy: 0.5706 - val_loss: 0.9162
Epoch 49/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.4688 - loss: 1.1799
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5183 - loss: 1.0588 
[1m 87/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5328 - loss: 1.0317
[1m131/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5390 - loss: 1.0221
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5414 - loss: 1.0193 - val_accuracy: 0.5641 - val_loss: 0.9195
Epoch 50/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5938 - loss: 1.1801
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5543 - loss: 1.0277 
[1m 89/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5556 - loss: 1.0217
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5568 - loss: 1.0166
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5579 - loss: 1.0131 - val_accuracy: 0.5831 - val_loss: 0.8889
Epoch 51/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5000 - loss: 1.0440
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5571 - loss: 1.0041 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5597 - loss: 1.0023
[1m121/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5595 - loss: 1.0022
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5602 - loss: 1.0010
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5603 - loss: 1.0008 - val_accuracy: 0.5864 - val_loss: 0.9082
Epoch 52/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.4844 - loss: 1.0716
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5708 - loss: 0.9862 
[1m 81/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5683 - loss: 0.9908
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5658 - loss: 0.9925
[1m164/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5640 - loss: 0.9941
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5639 - loss: 0.9941 - val_accuracy: 0.6022 - val_loss: 0.9012
Epoch 53/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5781 - loss: 1.1057
[1m 45/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5496 - loss: 1.0116 
[1m 88/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5533 - loss: 1.0022
[1m129/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5542 - loss: 0.9997
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5546 - loss: 0.9985 - val_accuracy: 0.5775 - val_loss: 0.9033
Epoch 54/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5312 - loss: 1.0684
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5428 - loss: 1.0147 
[1m 80/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5452 - loss: 1.0056
[1m124/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5488 - loss: 0.9985
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5521 - loss: 0.9953
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5524 - loss: 0.9951 - val_accuracy: 0.5749 - val_loss: 0.9314
Epoch 55/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 21ms/step - accuracy: 0.5000 - loss: 1.1016
[1m 40/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5581 - loss: 1.0082 
[1m 75/167[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5634 - loss: 0.9971
[1m117/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5636 - loss: 0.9945
[1m162/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5634 - loss: 0.9945
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5634 - loss: 0.9944 - val_accuracy: 0.5969 - val_loss: 0.8738
Epoch 56/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 19ms/step - accuracy: 0.5156 - loss: 1.0027
[1m 44/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5660 - loss: 0.9677 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5615 - loss: 0.9731
[1m128/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5609 - loss: 0.9767
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5614 - loss: 0.9779
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5614 - loss: 0.9779 - val_accuracy: 0.5825 - val_loss: 0.8956
Epoch 57/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.6562 - loss: 0.9350
[1m 42/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5665 - loss: 1.0029 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5615 - loss: 1.0015
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5603 - loss: 0.9999
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5601 - loss: 0.9976 - val_accuracy: 0.5821 - val_loss: 0.8943
Epoch 58/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 0.9501
[1m 41/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5503 - loss: 0.9787 
[1m 83/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5563 - loss: 0.9789
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5583 - loss: 0.9782
[1m166/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5592 - loss: 0.9787
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5592 - loss: 0.9787 - val_accuracy: 0.5884 - val_loss: 0.8790
Epoch 59/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3s[0m 20ms/step - accuracy: 0.5312 - loss: 1.0476
[1m 38/167[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5655 - loss: 0.9822 
[1m 79/167[0m [32m━━━━━━━━━[0m[37m━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5692 - loss: 0.9771
[1m122/167[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5689 - loss: 0.9775
[1m165/167[0m [32m━━━━━━━━━━━━━━━━━━━[0m[37m━[0m [1m0s[0m 1ms/step - accuracy: 0.5686 - loss: 0.9787
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5686 - loss: 0.9788 - val_accuracy: 0.5618 - val_loss: 0.9104
Epoch 60/145

[1m  1/167[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m4s[0m 24ms/step - accuracy: 0.4844 - loss: 1.2490
[1m 43/167[0m [32m━━━━━[0m[37m━━━━━━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5621 - loss: 1.0168 
[1m 86/167[0m [32m━━━━━━━━━━[0m[37m━━━━━━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5641 - loss: 0.9987
[1m127/167[0m [32m━━━━━━━━━━━━━━━[0m[37m━━━━━[0m [1m0s[0m 1ms/step - accuracy: 0.5625 - loss: 0.9930
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.5621 - loss: 0.9903
[1m167/167[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.5621 - loss: 0.9903 - val_accuracy: 0.5719 - val_loss: 0.9259

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m23s[0m 494ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 10ms/step  
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 10ms/step
Saved model to disk.

Accuracy capturado en la ejecución 29: 56.12 [%]
F1-score capturado en la ejecución 29: 56.06 [%]

=== 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)
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)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ conv1d (Conv1D)                 │ (None, 3, 32)          │        24,032 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization             │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout (Dropout)               │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ conv1d_1 (Conv1D)               │ (None, 3, 32)          │         3,104 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ layer_normalization_1           │ (None, 3, 32)          │            64 │
│ (LayerNormalization)            │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_1 (Dropout)             │ (None, 3, 32)          │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ global_average_pooling1d        │ (None, 32)             │             0 │
│ (GlobalAveragePooling1D)        │                        │               │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dropout_2 (Dropout)             │ (None, 32)             │             0 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense (Dense)                   │ (None, 4)              │           132 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 27,396 (107.02 KB)
 Trainable params: 27,396 (107.02 KB)
 Non-trainable params: 0 (0.00 B)
Loaded model from disk.
(1545, 3, 250)
(10634, 3, 250)

[1m  1/333[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m3:43[0m 674ms/step
[1m 68/333[0m [32m━━━━[0m[37m━━━━━━━━━━━━━━━━[0m [1m0s[0m 749us/step  
[1m146/333[0m [32m━━━━━━━━[0m[37m━━━━━━━━━━━━[0m [1m0s[0m 693us/step
[1m217/333[0m [32m━━━━━━━━━━━━━[0m[37m━━━━━━━[0m [1m0s[0m 698us/step
[1m292/333[0m [32m━━━━━━━━━━━━━━━━━[0m[37m━━━[0m [1m0s[0m 692us/step
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step  
[1m333/333[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 2ms/step

[1m 1/49[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m0s[0m 15ms/step
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step 
[1m49/49[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 9ms/step

[1m 1/96[0m [37m━━━━━━━━━━━━━━━━━━━━[0m [1m1s[0m 15ms/step
[1m68/96[0m [32m━━━━━━━━━━━━━━[0m[37m━━━━━━[0m [1m0s[0m 749us/step
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step  
[1m96/96[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 5ms/step
Global accuracy score (validation) = 56.8 [%]
Global F1 score (validation) = 56.99 [%]
[[1.]
 [1.]
 [1.]
 ...
 [2.]
 [2.]
 [2.]]
(1545, 1)
[[0.44650757 0.48956567 0.0107516  0.05317511]
 [0.41375795 0.38107938 0.05860343 0.14655925]
 [0.41088268 0.5361919  0.00331443 0.04961112]
 ...
 [0.07454951 0.04349992 0.84942174 0.03252881]
 [0.02898897 0.01507278 0.9434489  0.01248938]
 [0.02841438 0.01474168 0.94460416 0.01223983]]
(1545, 4)
-------------------------------------------------

Global accuracy score (train) = 58.86 [%]
Global accuracy score (test) = 54.76 [%]
Global F1 score (train) = 58.99 [%]
Global F1 score (test) = 53.7 [%]
                    precision    recall  f1-score   support

   LIGHT-INTENSITY       0.37      0.29      0.33       400
MODERATE-INTENSITY       0.45      0.61      0.52       400
         SEDENTARY       0.63      0.86      0.73       400
VIGOROUS-INTENSITY       0.97      0.41      0.57       345

          accuracy                           0.55      1545
         macro avg       0.61      0.54      0.54      1545
      weighted avg       0.59      0.55      0.54      1545


Accuracy capturado en la ejecución 30: 54.76 [%]
F1-score capturado en la ejecución 30: 53.7 [%]

=== RESUMEN FINAL ===
Accuracies: [52.49, 50.49, 56.31, 55.86, 48.41, 53.14, 56.89, 50.42, 54.95, 53.27, 52.56, 55.53, 54.56, 56.12, 59.48, 57.41, 54.95, 55.02, 54.89, 58.32, 56.57, 51.46, 56.05, 52.82, 53.66, 51.46, 49.71, 55.99, 56.12, 54.76]
F1-scores: [53.43, 50.05, 55.88, 55.23, 47.67, 53.22, 56.81, 48.76, 54.06, 52.45, 52.22, 54.51, 54.22, 54.52, 59.35, 55.58, 54.91, 54.33, 53.7, 58.56, 55.49, 50.32, 56.06, 51.02, 52.41, 50.33, 48.02, 55.55, 56.06, 53.7]
Accuracy mean: 54.3223 | std: 2.5807
F1 mean: 53.6140 | std: 2.8226

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