2025-10-25 19:42:22.375896: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-25 19:42:22.388565: 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 00:00:1761414142.402587 1837945 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761414142.407139 1837945 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761414142.418500 1837945 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414142.418548 1837945 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414142.418551 1837945 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414142.418553 1837945 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-25 19:42:22.422120: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-10-25 19:42:40,930	INFO worker.py:1927 -- Started a local Ray instance.
2025-10-25 19:42:41,654	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-10-25 19:42:41,733	INFO trial.py:182 -- Creating a new dirname dir_0195f_ece2 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,737	INFO trial.py:182 -- Creating a new dirname dir_0195f_2c96 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,739	INFO trial.py:182 -- Creating a new dirname dir_0195f_76d2 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,741	INFO trial.py:182 -- Creating a new dirname dir_0195f_c359 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,743	INFO trial.py:182 -- Creating a new dirname dir_0195f_3d4c because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,745	INFO trial.py:182 -- Creating a new dirname dir_0195f_b4a7 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,748	INFO trial.py:182 -- Creating a new dirname dir_0195f_c6ed because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,750	INFO trial.py:182 -- Creating a new dirname dir_0195f_5980 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,753	INFO trial.py:182 -- Creating a new dirname dir_0195f_651b because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,756	INFO trial.py:182 -- Creating a new dirname dir_0195f_bc2a because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,759	INFO trial.py:182 -- Creating a new dirname dir_0195f_7d58 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,762	INFO trial.py:182 -- Creating a new dirname dir_0195f_f676 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,766	INFO trial.py:182 -- Creating a new dirname dir_0195f_875c because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,770	INFO trial.py:182 -- Creating a new dirname dir_0195f_de6e because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,774	INFO trial.py:182 -- Creating a new dirname dir_0195f_eeff because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,778	INFO trial.py:182 -- Creating a new dirname dir_0195f_a5a8 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,782	INFO trial.py:182 -- Creating a new dirname dir_0195f_86c6 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,795	INFO trial.py:182 -- Creating a new dirname dir_0195f_7c35 because trial dirname 'dir_0195f' already exists.
2025-10-25 19:42:41,805	INFO trial.py:182 -- Creating a new dirname dir_0195f_b78f because trial dirname 'dir_0195f' already exists.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
Se lanza la búsqueda de hiperparámetros óptimos del modelo.
╭───────────────────────────────────────────────────────────────────╮
│ Configuration for experiment     XGBoost_hyperparameters_tuning   │
├───────────────────────────────────────────────────────────────────┤
│ Search algorithm                 BasicVariantGenerator            │
│ Scheduler                        AsyncHyperBandScheduler          │
│ Number of trials                 20                               │
╰───────────────────────────────────────────────────────────────────╯

View detailed results here: /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_XGB_acc_gyr_17_classes/XGBoost_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-10-25_19-42-39_047191_1837945/artifacts/2025-10-25_19-42-41/XGBoost_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-10-25 19:42:41. Total running time: 0s
Logical resource usage: 0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       num_boost_round     max_depth     learning_rate     subsample     colsample_bytree     gamma     min_child_weight     reg_alpha     reg_lambda     random_state │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_0195f    PENDING                  490             3        0.0180686       0.386165             0.401018   1.91019                   26     28.4719          1.13302             2009 │
│ trial_0195f    PENDING                  216             3        0.0417666       0.538654             0.569217   3.2315                    21      1.13863       168.641               3282 │
│ trial_0195f    PENDING                  472             2        0.0362057       0.691935             0.596683   3.41711                   50      0.229961       27.507               6718 │
│ trial_0195f    PENDING                  267             3        0.0379462       0.678655             0.339382   8.27026                   46      0.155793       16.3947              6848 │
│ trial_0195f    PENDING                  406             2        0.0188112       0.475566             0.620105   8.00317                   41     17.1877          1.8251              5173 │
│ trial_0195f    PENDING                  450             2        0.0382595       0.603751             0.569897   3.0477                    54      0.103932        7.40936             1023 │
│ trial_0195f    PENDING                  315             3        0.0439828       0.390514             0.352631   6.90055                   11      1.81695        43.3285              2402 │
│ trial_0195f    PENDING                  331             2        0.0317944       0.313674             0.67536    4.41856                   53      5.32658        48.6357              7554 │
│ trial_0195f    PENDING                  195             2        0.00514406      0.580216             0.441027   5.8561                    50     57.2455         85.8182              8258 │
│ trial_0195f    PENDING                  414             2        0.0202599       0.695361             0.509306   1.56986                   58     93.6182          2.27817             1172 │
│ trial_0195f    PENDING                  111             3        0.0257285       0.332271             0.433404   9.4826                    26      0.114293       56.1492              7789 │
│ trial_0195f    PENDING                  253             2        0.0231462       0.46082              0.450991   5.86543                   12      0.55736         6.01178             7339 │
│ trial_0195f    PENDING                  489             2        0.0257862       0.674977             0.554238   4.86723                   23     15.2685         12.2752              3488 │
│ trial_0195f    PENDING                  210             3        0.0277019       0.451699             0.324143   8.75302                   48      3.3268          3.18417             6615 │
│ trial_0195f    PENDING                  502             3        0.0343541       0.330756             0.302921   8.9828                    17      0.114231        1.95791             1454 │
│ trial_0195f    PENDING                  127             2        0.0211436       0.673953             0.614406   5.99959                   45     55.8846          3.0865              7679 │
│ trial_0195f    PENDING                  512             3        0.0413879       0.513759             0.697578   2.60018                   55      0.548801       90.1764               512 │
│ trial_0195f    PENDING                  481             3        0.034166        0.68654              0.613953   2.28701                   49      0.108157      119.964               8159 │
│ trial_0195f    PENDING                  558             2        0.00744142      0.549635             0.625081   8.77248                   41     14.3318          1.34804             8995 │
│ trial_0195f    PENDING                  527             3        0.0135209       0.653459             0.597106   6.02627                   57     21.8788          1.27441             3227 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.44103 │
│ gamma                         5.8561 │
│ learning_rate                0.00514 │
│ max_depth                          2 │
│ min_child_weight                  50 │
│ num_boost_round                  195 │
│ random_state                    8258 │
│ reg_alpha                    57.2455 │
│ reg_lambda                   85.8182 │
│ subsample                    0.58022 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.40102 │
│ gamma                        1.91019 │
│ learning_rate                0.01807 │
│ max_depth                          3 │
│ min_child_weight                  26 │
│ num_boost_round                  490 │
│ random_state                    2009 │
│ reg_alpha                    28.4719 │
│ reg_lambda                   1.13302 │
│ subsample                    0.38617 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.33938 │
│ gamma                        8.27026 │
│ learning_rate                0.03795 │
│ max_depth                          3 │
│ min_child_weight                  46 │
│ num_boost_round                  267 │
│ random_state                    6848 │
│ reg_alpha                    0.15579 │
│ reg_lambda                   16.3947 │
│ subsample                    0.67865 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.56922 │
│ gamma                         3.2315 │
│ learning_rate                0.04177 │
│ max_depth                          3 │
│ min_child_weight                  21 │
│ num_boost_round                  216 │
│ random_state                    3282 │
│ reg_alpha                    1.13863 │
│ reg_lambda                   168.641 │
│ subsample                    0.53865 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree              0.4334 │
│ gamma                         9.4826 │
│ learning_rate                0.02573 │
│ max_depth                          3 │
│ min_child_weight                  26 │
│ num_boost_round                  111 │
│ random_state                    7789 │
│ reg_alpha                    0.11429 │
│ reg_lambda                   56.1492 │
│ subsample                    0.33227 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.59668 │
│ gamma                        3.41711 │
│ learning_rate                0.03621 │
│ max_depth                          2 │
│ min_child_weight                  50 │
│ num_boost_round                  472 │
│ random_state                    6718 │
│ reg_alpha                    0.22996 │
│ reg_lambda                    27.507 │
│ subsample                    0.69194 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.67536 │
│ gamma                        4.41856 │
│ learning_rate                0.03179 │
│ max_depth                          2 │
│ min_child_weight                  53 │
│ num_boost_round                  331 │
│ random_state                    7554 │
│ reg_alpha                    5.32658 │
│ reg_lambda                   48.6357 │
│ subsample                    0.31367 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.32414 │
│ gamma                        8.75302 │
│ learning_rate                 0.0277 │
│ max_depth                          3 │
│ min_child_weight                  48 │
│ num_boost_round                  210 │
│ random_state                    6615 │
│ reg_alpha                     3.3268 │
│ reg_lambda                   3.18417 │
│ subsample                     0.4517 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.45099 │
│ gamma                        5.86543 │
│ learning_rate                0.02315 │
│ max_depth                          2 │
│ min_child_weight                  12 │
│ num_boost_round                  253 │
│ random_state                    7339 │
│ reg_alpha                    0.55736 │
│ reg_lambda                   6.01178 │
│ subsample                    0.46082 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
[36m(train_xgb_ray_tune pid=1839856)[0m 2025-10-25 19:42:45.446989: 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_xgb_ray_tune pid=1839856)[0m 2025-10-25 19:42:45.469974: 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
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.35263 │
│ gamma                        6.90055 │
│ learning_rate                0.04398 │
│ max_depth                          3 │
│ min_child_weight                  11 │
│ num_boost_round                  315 │
│ random_state                    2402 │
│ reg_alpha                    1.81695 │
│ reg_lambda                   43.3285 │
│ subsample                    0.39051 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.30292 │
│ gamma                         8.9828 │
│ learning_rate                0.03435 │
│ max_depth                          3 │
│ min_child_weight                  17 │
│ num_boost_round                  502 │
│ random_state                    1454 │
│ reg_alpha                    0.11423 │
│ reg_lambda                   1.95791 │
│ subsample                    0.33076 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.55424 │
│ gamma                        4.86723 │
│ learning_rate                0.02579 │
│ max_depth                          2 │
│ min_child_weight                  23 │
│ num_boost_round                  489 │
│ random_state                    3488 │
│ reg_alpha                    15.2685 │
│ reg_lambda                   12.2752 │
│ subsample                    0.67498 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.61395 │
│ gamma                        2.28701 │
│ learning_rate                0.03417 │
│ max_depth                          3 │
│ min_child_weight                  49 │
│ num_boost_round                  481 │
│ random_state                    8159 │
│ reg_alpha                    0.10816 │
│ reg_lambda                   119.964 │
│ subsample                    0.68654 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.69758 │
│ gamma                        2.60018 │
│ learning_rate                0.04139 │
│ max_depth                          3 │
│ min_child_weight                  55 │
│ num_boost_round                  512 │
│ random_state                     512 │
│ reg_alpha                     0.5488 │
│ reg_lambda                   90.1764 │
│ subsample                    0.51376 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree              0.6201 │
│ gamma                        8.00317 │
│ learning_rate                0.01881 │
│ max_depth                          2 │
│ min_child_weight                  41 │
│ num_boost_round                  406 │
│ random_state                    5173 │
│ reg_alpha                    17.1877 │
│ reg_lambda                    1.8251 │
│ subsample                    0.47557 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.50931 │
│ gamma                        1.56986 │
│ learning_rate                0.02026 │
│ max_depth                          2 │
│ min_child_weight                  58 │
│ num_boost_round                  414 │
│ random_state                    1172 │
│ reg_alpha                    93.6182 │
│ reg_lambda                   2.27817 │
│ subsample                    0.69536 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.61441 │
│ gamma                        5.99959 │
│ learning_rate                0.02114 │
│ max_depth                          2 │
│ min_child_weight                  45 │
│ num_boost_round                  127 │
│ random_state                    7679 │
│ reg_alpha                    55.8846 │
│ reg_lambda                    3.0865 │
│ subsample                    0.67395 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree              0.5699 │
│ gamma                         3.0477 │
│ learning_rate                0.03826 │
│ max_depth                          2 │
│ min_child_weight                  54 │
│ num_boost_round                  450 │
│ random_state                    1023 │
│ reg_alpha                    0.10393 │
│ reg_lambda                   7.40936 │
│ subsample                    0.60375 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
[36m(train_xgb_ray_tune pid=1839846)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_xgb_ray_tune pid=1839846)[0m E0000 00:00:1761414165.538657 1841104 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
[36m(train_xgb_ray_tune pid=1839846)[0m E0000 00:00:1761414165.547607 1841104 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[36m(train_xgb_ray_tune pid=1839846)[0m W0000 00:00:1761414165.571699 1841104 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_xgb_ray_tune pid=1839846)[0m W0000 00:00:1761414165.571736 1841104 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_xgb_ray_tune pid=1839846)[0m W0000 00:00:1761414165.571739 1841104 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_xgb_ray_tune pid=1839846)[0m W0000 00:00:1761414165.571741 1841104 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
[36m(train_xgb_ray_tune pid=1839856)[0m 2025-10-25 19:42:45.534655: 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_xgb_ray_tune pid=1839856)[0m To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.62508 │
│ gamma                        8.77248 │
│ learning_rate                0.00744 │
│ max_depth                          2 │
│ min_child_weight                  41 │
│ num_boost_round                  558 │
│ random_state                    8995 │
│ reg_alpha                    14.3318 │
│ reg_lambda                   1.34804 │
│ subsample                    0.54963 │
╰──────────────────────────────────────╯
Trial trial_0195f started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_0195f config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.59711 │
│ gamma                        6.02627 │
│ learning_rate                0.01352 │
│ max_depth                          3 │
│ min_child_weight                  57 │
│ num_boost_round                  527 │
│ random_state                    3227 │
│ reg_alpha                    21.8788 │
│ reg_lambda                   1.27441 │
│ subsample                    0.65346 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=1839846)[0m [0]	train-mlogloss:2.67580	validation-mlogloss:2.67666
[36m(train_xgb_ray_tune pid=1839855)[0m [0]	train-mlogloss:2.63518	validation-mlogloss:2.63833
[36m(train_xgb_ray_tune pid=1839847)[0m [0]	train-mlogloss:2.66536	validation-mlogloss:2.66600
[36m(train_xgb_ray_tune pid=1839856)[0m [0]	train-mlogloss:2.70277	validation-mlogloss:2.70268
[36m(train_xgb_ray_tune pid=1839856)[0m [1]	train-mlogloss:2.69755	validation-mlogloss:2.69750
[36m(train_xgb_ray_tune pid=1839846)[0m [1]	train-mlogloss:2.64368	validation-mlogloss:2.64594
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[36m(train_xgb_ray_tune pid=1839868)[0m [100]	train-mlogloss:1.55741	validation-mlogloss:1.54215
[36m(train_xgb_ray_tune pid=1839880)[0m [102]	train-mlogloss:1.94556	validation-mlogloss:1.95850
[36m(train_xgb_ray_tune pid=1839849)[0m [76]	train-mlogloss:1.72061	validation-mlogloss:1.74535
[36m(train_xgb_ray_tune pid=1839846)[0m [102]	train-mlogloss:1.51241	validation-mlogloss:1.52027
[36m(train_xgb_ray_tune pid=1839862)[0m [79]	train-mlogloss:1.23515	validation-mlogloss:1.25125
[36m(train_xgb_ray_tune pid=1839871)[0m [97]	train-mlogloss:1.22564	validation-mlogloss:1.22286
[36m(train_xgb_ray_tune pid=1839845)[0m [148]	train-mlogloss:1.13112	validation-mlogloss:1.14796
[36m(train_xgb_ray_tune pid=1839877)[0m [113]	train-mlogloss:1.49482	validation-mlogloss:1.48429
[36m(train_xgb_ray_tune pid=1839870)[0m [114]	train-mlogloss:1.59140	validation-mlogloss:1.57383
[36m(train_xgb_ray_tune pid=1839878)[0m [131]	train-mlogloss:1.17013	validation-mlogloss:1.19557
[36m(train_xgb_ray_tune pid=1839879)[0m [118]	train-mlogloss:1.34947	validation-mlogloss:1.33032
[36m(train_xgb_ray_tune pid=1839881)[0m [115]	train-mlogloss:1.35110	validation-mlogloss:1.34436
[36m(train_xgb_ray_tune pid=1839856)[0m [128]	train-mlogloss:2.20280	validation-mlogloss:2.20040
[36m(train_xgb_ray_tune pid=1839846)[0m [103]	train-mlogloss:1.50832	validation-mlogloss:1.51614
[36m(train_xgb_ray_tune pid=1839845)[0m [149]	train-mlogloss:1.12914	validation-mlogloss:1.14636
[36m(train_xgb_ray_tune pid=1839855)[0m [81]	train-mlogloss:1.19179	validation-mlogloss:1.23517
[36m(train_xgb_ray_tune pid=1839877)[0m [114]	train-mlogloss:1.49117	validation-mlogloss:1.48033
[36m(train_xgb_ray_tune pid=1839862)[0m [80]	train-mlogloss:1.23093	validation-mlogloss:1.24696
[36m(train_xgb_ray_tune pid=1839871)[0m [98]	train-mlogloss:1.22214	validation-mlogloss:1.22033
[36m(train_xgb_ray_tune pid=1839887)[0m [89]	train-mlogloss:1.46947	validation-mlogloss:1.46837
[36m(train_xgb_ray_tune pid=1839878)[0m [132]	train-mlogloss:1.16764	validation-mlogloss:1.19319
[36m(train_xgb_ray_tune pid=1839856)[0m [129]	train-mlogloss:2.19990	validation-mlogloss:2.19740
[36m(train_xgb_ray_tune pid=1839869)[0m [82]	train-mlogloss:1.21634	validation-mlogloss:1.25818
[36m(train_xgb_ray_tune pid=1839880)[0m [103]	train-mlogloss:1.94142	validation-mlogloss:1.95444
[36m(train_xgb_ray_tune pid=1839845)[0m [150]	train-mlogloss:1.12720	validation-mlogloss:1.14496
[36m(train_xgb_ray_tune pid=1839847)[0m [79]	train-mlogloss:1.33931	validation-mlogloss:1.35473
[36m(train_xgb_ray_tune pid=1839870)[0m [115]	train-mlogloss:1.58811	validation-mlogloss:1.57024
[36m(train_xgb_ray_tune pid=1839878)[0m [133]	train-mlogloss:1.16534	validation-mlogloss:1.19094
[36m(train_xgb_ray_tune pid=1839848)[0m [116]	train-mlogloss:1.34027	validation-mlogloss:1.32613
[36m(train_xgb_ray_tune pid=1839879)[0m [119]	train-mlogloss:1.34599	validation-mlogloss:1.32699
[36m(train_xgb_ray_tune pid=1839881)[0m [116]	train-mlogloss:1.34767	validation-mlogloss:1.34090
[36m(train_xgb_ray_tune pid=1839893)[0m [72]	train-mlogloss:1.32657	validation-mlogloss:1.34782
[36m(train_xgb_ray_tune pid=1839868)[0m [101]	train-mlogloss:1.55345	validation-mlogloss:1.53783
[36m(train_xgb_ray_tune pid=1839894)[0m [75]	train-mlogloss:1.37624	validation-mlogloss:1.39640
[36m(train_xgb_ray_tune pid=1839849)[0m [77]	train-mlogloss:1.71416	validation-mlogloss:1.73895
[36m(train_xgb_ray_tune pid=1839887)[0m [90]	train-mlogloss:1.46414	validation-mlogloss:1.46301
[36m(train_xgb_ray_tune pid=1839856)[0m [130]	train-mlogloss:2.19687	validation-mlogloss:2.19432
[36m(train_xgb_ray_tune pid=1839846)[0m [104]	train-mlogloss:1.50431	validation-mlogloss:1.51222
[36m(train_xgb_ray_tune pid=1839845)[0m [151]	train-mlogloss:1.12533	validation-mlogloss:1.14370
[36m(train_xgb_ray_tune pid=1839855)[0m [82]	train-mlogloss:1.18708	validation-mlogloss:1.23126
[36m(train_xgb_ray_tune pid=1839877)[0m [115]	train-mlogloss:1.48752	validation-mlogloss:1.47657
[36m(train_xgb_ray_tune pid=1839862)[0m [81]	train-mlogloss:1.22667	validation-mlogloss:1.24267
[36m(train_xgb_ray_tune pid=1839871)[0m [99]	train-mlogloss:1.21886	validation-mlogloss:1.21763
[36m(train_xgb_ray_tune pid=1839878)[0m [134]	train-mlogloss:1.16327	validation-mlogloss:1.18915
[36m(train_xgb_ray_tune pid=1839848)[0m [117]	train-mlogloss:1.33710	validation-mlogloss:1.32301
[36m(train_xgb_ray_tune pid=1839879)[0m [120]	train-mlogloss:1.34284	validation-mlogloss:1.32385
[36m(train_xgb_ray_tune pid=1839880)[0m [104]	train-mlogloss:1.93742	validation-mlogloss:1.95058
[36m(train_xgb_ray_tune pid=1839894)[0m [76]	train-mlogloss:1.37004	validation-mlogloss:1.39055
[36m(train_xgb_ray_tune pid=1839846)[0m [105]	train-mlogloss:1.50028	validation-mlogloss:1.50792
[36m(train_xgb_ray_tune pid=1839845)[0m [152]	train-mlogloss:1.12350	validation-mlogloss:1.14258
[36m(train_xgb_ray_tune pid=1839847)[0m [80]	train-mlogloss:1.33379	validation-mlogloss:1.34962
[36m(train_xgb_ray_tune pid=1839870)[0m [116]	train-mlogloss:1.58474	validation-mlogloss:1.56674
[36m(train_xgb_ray_tune pid=1839887)[0m [91]	train-mlogloss:1.45919	validation-mlogloss:1.45807
[36m(train_xgb_ray_tune pid=1839878)[0m [135]	train-mlogloss:1.16131	validation-mlogloss:1.18756
[36m(train_xgb_ray_tune pid=1839879)[0m [121]	train-mlogloss:1.33986	validation-mlogloss:1.32123
[36m(train_xgb_ray_tune pid=1839881)[0m [117]	train-mlogloss:1.34405	validation-mlogloss:1.33740
[36m(train_xgb_ray_tune pid=1839856)[0m [131]	train-mlogloss:2.19401	validation-mlogloss:2.19142
[36m(train_xgb_ray_tune pid=1839869)[0m [83]	train-mlogloss:1.21206	validation-mlogloss:1.25444
[36m(train_xgb_ray_tune pid=1839893)[0m [73]	train-mlogloss:1.32050	validation-mlogloss:1.34185
[36m(train_xgb_ray_tune pid=1839849)[0m [78]	train-mlogloss:1.70780	validation-mlogloss:1.73235
[36m(train_xgb_ray_tune pid=1839848)[0m [118]	train-mlogloss:1.33394	validation-mlogloss:1.31974
[36m(train_xgb_ray_tune pid=1839845)[0m [153]	train-mlogloss:1.12173	validation-mlogloss:1.14131
[36m(train_xgb_ray_tune pid=1839855)[0m [83]	train-mlogloss:1.18254	validation-mlogloss:1.22752
[36m(train_xgb_ray_tune pid=1839877)[0m [116]	train-mlogloss:1.48384	validation-mlogloss:1.47279
[36m(train_xgb_ray_tune pid=1839862)[0m [82]	train-mlogloss:1.22256	validation-mlogloss:1.23903
[36m(train_xgb_ray_tune pid=1839881)[0m [118]	train-mlogloss:1.34045	validation-mlogloss:1.33387
[36m(train_xgb_ray_tune pid=1839856)[0m [132]	train-mlogloss:2.19106	validation-mlogloss:2.18841
[36m(train_xgb_ray_tune pid=1839868)[0m [102]	train-mlogloss:1.54958	validation-mlogloss:1.53370
[36m(train_xgb_ray_tune pid=1839894)[0m [77]	train-mlogloss:1.36392	validation-mlogloss:1.38503
[36m(train_xgb_ray_tune pid=1839880)[0m [105]	train-mlogloss:1.93317	validation-mlogloss:1.94616
[36m(train_xgb_ray_tune pid=1839871)[0m [100]	train-mlogloss:1.21563	validation-mlogloss:1.21497
[36m(train_xgb_ray_tune pid=1839878)[0m [136]	train-mlogloss:1.15926	validation-mlogloss:1.18577
[36m(train_xgb_ray_tune pid=1839849)[0m [79]	train-mlogloss:1.70173	validation-mlogloss:1.72629
[36m(train_xgb_ray_tune pid=1839846)[0m [106]	train-mlogloss:1.49614	validation-mlogloss:1.50384
[36m(train_xgb_ray_tune pid=1839887)[0m [92]	train-mlogloss:1.45446	validation-mlogloss:1.45332
[36m(train_xgb_ray_tune pid=1839845)[0m [154]	train-mlogloss:1.11998	validation-mlogloss:1.13993
[36m(train_xgb_ray_tune pid=1839877)[0m [117]	train-mlogloss:1.48023	validation-mlogloss:1.46908
[36m(train_xgb_ray_tune pid=1839848)[0m [119]	train-mlogloss:1.33072	validation-mlogloss:1.31689
[36m(train_xgb_ray_tune pid=1839879)[0m [122]	train-mlogloss:1.33676	validation-mlogloss:1.31830
[36m(train_xgb_ray_tune pid=1839881)[0m [119]	train-mlogloss:1.33697	validation-mlogloss:1.33056
[36m(train_xgb_ray_tune pid=1839856)[0m [133]	train-mlogloss:2.18803	validation-mlogloss:2.18535
[36m(train_xgb_ray_tune pid=1839869)[0m [84]	train-mlogloss:1.20805	validation-mlogloss:1.25031
[36m(train_xgb_ray_tune pid=1839893)[0m [74]	train-mlogloss:1.31465	validation-mlogloss:1.33588
[36m(train_xgb_ray_tune pid=1839880)[0m [106]	train-mlogloss:1.92919	validation-mlogloss:1.94205
[36m(train_xgb_ray_tune pid=1839846)[0m [107]	train-mlogloss:1.49174	validation-mlogloss:1.49934
[36m(train_xgb_ray_tune pid=1839845)[0m [155]	train-mlogloss:1.11811	validation-mlogloss:1.13852
[36m(train_xgb_ray_tune pid=1839855)[0m [84]	train-mlogloss:1.17827	validation-mlogloss:1.22371
[36m(train_xgb_ray_tune pid=1839877)[0m [118]	train-mlogloss:1.47675	validation-mlogloss:1.46533
[36m(train_xgb_ray_tune pid=1839847)[0m [81]	train-mlogloss:1.32826	validation-mlogloss:1.34467
[36m(train_xgb_ray_tune pid=1839870)[0m [117]	train-mlogloss:1.58147	validation-mlogloss:1.56320
[36m(train_xgb_ray_tune pid=1839862)[0m [83]	train-mlogloss:1.21839	validation-mlogloss:1.23541
[36m(train_xgb_ray_tune pid=1839871)[0m [101]	train-mlogloss:1.21230	validation-mlogloss:1.21226
[36m(train_xgb_ray_tune pid=1839887)[0m [93]	train-mlogloss:1.44974	validation-mlogloss:1.44850
[36m(train_xgb_ray_tune pid=1839878)[0m [137]	train-mlogloss:1.15703	validation-mlogloss:1.18376
[36m(train_xgb_ray_tune pid=1839848)[0m [120]	train-mlogloss:1.32750	validation-mlogloss:1.31377
[36m(train_xgb_ray_tune pid=1839879)[0m [123]	train-mlogloss:1.33375	validation-mlogloss:1.31533
[36m(train_xgb_ray_tune pid=1839881)[0m [120]	train-mlogloss:1.33356	validation-mlogloss:1.32723
[36m(train_xgb_ray_tune pid=1839894)[0m [78]	train-mlogloss:1.35805	validation-mlogloss:1.37931
[36m(train_xgb_ray_tune pid=1839845)[0m [156]	train-mlogloss:1.11627	validation-mlogloss:1.13720
[36m(train_xgb_ray_tune pid=1839870)[0m [118]	train-mlogloss:1.57822	validation-mlogloss:1.55973
[36m(train_xgb_ray_tune pid=1839871)[0m [102]	train-mlogloss:1.20908	validation-mlogloss:1.20959
[36m(train_xgb_ray_tune pid=1839878)[0m [138]	train-mlogloss:1.15494	validation-mlogloss:1.18206
[36m(train_xgb_ray_tune pid=1839848)[0m [121]	train-mlogloss:1.32435	validation-mlogloss:1.31061
[36m(train_xgb_ray_tune pid=1839879)[0m [124]	train-mlogloss:1.33079	validation-mlogloss:1.31221
[36m(train_xgb_ray_tune pid=1839856)[0m [134]	train-mlogloss:2.18534	validation-mlogloss:2.18251
[36m(train_xgb_ray_tune pid=1839869)[0m [85]	train-mlogloss:1.20394	validation-mlogloss:1.24637
[36m(train_xgb_ray_tune pid=1839893)[0m [75]	train-mlogloss:1.30883	validation-mlogloss:1.33034
[36m(train_xgb_ray_tune pid=1839868)[0m [103]	train-mlogloss:1.54541	validation-mlogloss:1.52926
[36m(train_xgb_ray_tune pid=1839880)[0m [107]	train-mlogloss:1.92519	validation-mlogloss:1.93795
[36m(train_xgb_ray_tune pid=1839849)[0m [80]	train-mlogloss:1.69578	validation-mlogloss:1.72044
[36m(train_xgb_ray_tune pid=1839846)[0m [108]	train-mlogloss:1.48778	validation-mlogloss:1.49524
[36m(train_xgb_ray_tune pid=1839862)[0m [84]	train-mlogloss:1.21442	validation-mlogloss:1.23216
[36m(train_xgb_ray_tune pid=1839871)[0m [103]	train-mlogloss:1.20596	validation-mlogloss:1.20704
[36m(train_xgb_ray_tune pid=1839887)[0m [94]	train-mlogloss:1.44500	validation-mlogloss:1.44342
[36m(train_xgb_ray_tune pid=1839878)[0m [139]	train-mlogloss:1.15274	validation-mlogloss:1.18037
[36m(train_xgb_ray_tune pid=1839881)[0m [121]	train-mlogloss:1.33018	validation-mlogloss:1.32389
[36m(train_xgb_ray_tune pid=1839856)[0m [135]	train-mlogloss:2.18249	validation-mlogloss:2.17961
[36m(train_xgb_ray_tune pid=1839894)[0m [79]	train-mlogloss:1.35228	validation-mlogloss:1.37383
[36m(train_xgb_ray_tune pid=1839846)[0m [109]	train-mlogloss:1.48366	validation-mlogloss:1.49108
[36m(train_xgb_ray_tune pid=1839845)[0m [157]	train-mlogloss:1.11465	validation-mlogloss:1.13601
[36m(train_xgb_ray_tune pid=1839855)[0m [85]	train-mlogloss:1.17381	validation-mlogloss:1.22005
[36m(train_xgb_ray_tune pid=1839877)[0m [119]	train-mlogloss:1.47330	validation-mlogloss:1.46165
[36m(train_xgb_ray_tune pid=1839870)[0m [119]	train-mlogloss:1.57504	validation-mlogloss:1.55639
[36m(train_xgb_ray_tune pid=1839848)[0m [122]	train-mlogloss:1.32150	validation-mlogloss:1.30780
[36m(train_xgb_ray_tune pid=1839879)[0m [125]	train-mlogloss:1.32800	validation-mlogloss:1.30937
[36m(train_xgb_ray_tune pid=1839881)[0m [122]	train-mlogloss:1.32681	validation-mlogloss:1.32054
[36m(train_xgb_ray_tune pid=1839869)[0m [86]	train-mlogloss:1.20000	validation-mlogloss:1.24327
[36m(train_xgb_ray_tune pid=1839893)[0m [76]	train-mlogloss:1.30323	validation-mlogloss:1.32506
[36m(train_xgb_ray_tune pid=1839880)[0m [108]	train-mlogloss:1.92111	validation-mlogloss:1.93370
[36m(train_xgb_ray_tune pid=1839849)[0m [81]	train-mlogloss:1.68988	validation-mlogloss:1.71447
[36m(train_xgb_ray_tune pid=1839845)[0m [158]	train-mlogloss:1.11285	validation-mlogloss:1.13471
[36m(train_xgb_ray_tune pid=1839877)[0m [120]	train-mlogloss:1.46986	validation-mlogloss:1.45795
[36m(train_xgb_ray_tune pid=1839847)[0m [82]	train-mlogloss:1.32299	validation-mlogloss:1.33954
[36m(train_xgb_ray_tune pid=1839870)[0m [120]	train-mlogloss:1.57188	validation-mlogloss:1.55312
[36m(train_xgb_ray_tune pid=1839862)[0m [85]	train-mlogloss:1.21105	validation-mlogloss:1.22904
[36m(train_xgb_ray_tune pid=1839871)[0m [104]	train-mlogloss:1.20296	validation-mlogloss:1.20446
[36m(train_xgb_ray_tune pid=1839887)[0m [95]	train-mlogloss:1.44029	validation-mlogloss:1.43860
[36m(train_xgb_ray_tune pid=1839848)[0m [123]	train-mlogloss:1.31869	validation-mlogloss:1.30517
[36m(train_xgb_ray_tune pid=1839879)[0m [126]	train-mlogloss:1.32513	validation-mlogloss:1.30667
[36m(train_xgb_ray_tune pid=1839856)[0m [136]	train-mlogloss:2.17971	validation-mlogloss:2.17681
[36m(train_xgb_ray_tune pid=1839868)[0m [104]	train-mlogloss:1.54156	validation-mlogloss:1.52528
[36m(train_xgb_ray_tune pid=1839894)[0m [80]	train-mlogloss:1.34653	validation-mlogloss:1.36835
[36m(train_xgb_ray_tune pid=1839869)[0m [87]	train-mlogloss:1.19624	validation-mlogloss:1.23973
[36m(train_xgb_ray_tune pid=1839878)[0m [140]	train-mlogloss:1.15061	validation-mlogloss:1.17876
[36m(train_xgb_ray_tune pid=1839846)[0m [110]	train-mlogloss:1.47984	validation-mlogloss:1.48696
[36m(train_xgb_ray_tune pid=1839845)[0m [159]	train-mlogloss:1.11116	validation-mlogloss:1.13377
[36m(train_xgb_ray_tune pid=1839855)[0m [86]	train-mlogloss:1.16963	validation-mlogloss:1.21661
[36m(train_xgb_ray_tune pid=1839862)[0m [86]	train-mlogloss:1.20718	validation-mlogloss:1.22559
[36m(train_xgb_ray_tune pid=1839848)[0m [124]	train-mlogloss:1.31570	validation-mlogloss:1.30209
[36m(train_xgb_ray_tune pid=1839881)[0m [123]	train-mlogloss:1.32349	validation-mlogloss:1.31693
[36m(train_xgb_ray_tune pid=1839856)[0m [137]	train-mlogloss:2.17675	validation-mlogloss:2.17380
[36m(train_xgb_ray_tune pid=1839893)[0m [77]	train-mlogloss:1.29790	validation-mlogloss:1.32010
[36m(train_xgb_ray_tune pid=1839880)[0m [109]	train-mlogloss:1.91701	validation-mlogloss:1.92942
[36m(train_xgb_ray_tune pid=1839849)[0m [82]	train-mlogloss:1.68402	validation-mlogloss:1.70853
[36m(train_xgb_ray_tune pid=1839870)[0m [121]	train-mlogloss:1.56862	validation-mlogloss:1.54959
[36m(train_xgb_ray_tune pid=1839871)[0m [105]	train-mlogloss:1.19966	validation-mlogloss:1.20141
[36m(train_xgb_ray_tune pid=1839877)[0m [121]	train-mlogloss:1.46631	validation-mlogloss:1.45417
[36m(train_xgb_ray_tune pid=1839846)[0m [111]	train-mlogloss:1.47590	validation-mlogloss:1.48279
[36m(train_xgb_ray_tune pid=1839845)[0m [160]	train-mlogloss:1.10949	validation-mlogloss:1.13282
[36m(train_xgb_ray_tune pid=1839847)[0m [83]	train-mlogloss:1.31770	validation-mlogloss:1.33471
[36m(train_xgb_ray_tune pid=1839887)[0m [96]	train-mlogloss:1.43555	validation-mlogloss:1.43384
[36m(train_xgb_ray_tune pid=1839878)[0m [141]	train-mlogloss:1.14866	validation-mlogloss:1.17694
[36m(train_xgb_ray_tune pid=1839879)[0m [127]	train-mlogloss:1.32205	validation-mlogloss:1.30389
[36m(train_xgb_ray_tune pid=1839881)[0m [124]	train-mlogloss:1.32013	validation-mlogloss:1.31383
[36m(train_xgb_ray_tune pid=1839856)[0m [138]	train-mlogloss:2.17406	validation-mlogloss:2.17114
[36m(train_xgb_ray_tune pid=1839869)[0m [88]	train-mlogloss:1.19242	validation-mlogloss:1.23625
[36m(train_xgb_ray_tune pid=1839894)[0m [81]	train-mlogloss:1.34095	validation-mlogloss:1.36300

Trial status: 20 RUNNING
Current time: 2025-10-25 19:43:12. Total running time: 30s
Logical resource usage: 20.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status       num_boost_round     max_depth     learning_rate     subsample     colsample_bytree     gamma     min_child_weight     reg_alpha     reg_lambda     random_state │
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_0195f    RUNNING                  490             3        0.0180686       0.386165             0.401018   1.91019                   26     28.4719          1.13302             2009 │
│ trial_0195f    RUNNING                  216             3        0.0417666       0.538654             0.569217   3.2315                    21      1.13863       168.641               3282 │
│ trial_0195f    RUNNING                  472             2        0.0362057       0.691935             0.596683   3.41711                   50      0.229961       27.507               6718 │
│ trial_0195f    RUNNING                  267             3        0.0379462       0.678655             0.339382   8.27026                   46      0.155793       16.3947              6848 │
│ trial_0195f    RUNNING                  406             2        0.0188112       0.475566             0.620105   8.00317                   41     17.1877          1.8251              5173 │
│ trial_0195f    RUNNING                  450             2        0.0382595       0.603751             0.569897   3.0477                    54      0.103932        7.40936             1023 │
│ trial_0195f    RUNNING                  315             3        0.0439828       0.390514             0.352631   6.90055                   11      1.81695        43.3285              2402 │
│ trial_0195f    RUNNING                  331             2        0.0317944       0.313674             0.67536    4.41856                   53      5.32658        48.6357              7554 │
│ trial_0195f    RUNNING                  195             2        0.00514406      0.580216             0.441027   5.8561                    50     57.2455         85.8182              8258 │
│ trial_0195f    RUNNING                  414             2        0.0202599       0.695361             0.509306   1.56986                   58     93.6182          2.27817             1172 │
│ trial_0195f    RUNNING                  111             3        0.0257285       0.332271             0.433404   9.4826                    26      0.114293       56.1492              7789 │
│ trial_0195f    RUNNING                  253             2        0.0231462       0.46082              0.450991   5.86543                   12      0.55736         6.01178             7339 │
│ trial_0195f    RUNNING                  489             2        0.0257862       0.674977             0.554238   4.86723                   23     15.2685         12.2752              3488 │
│ trial_0195f    RUNNING                  210             3        0.0277019       0.451699             0.324143   8.75302                   48      3.3268          3.18417             6615 │
│ trial_0195f    RUNNING                  502             3        0.0343541       0.330756             0.302921   8.9828                    17      0.114231        1.95791             1454 │
│ trial_0195f    RUNNING                  127             2        0.0211436       0.673953             0.614406   5.99959                   45     55.8846          3.0865              7679 │
│ trial_0195f    RUNNING                  512             3        0.0413879       0.513759             0.697578   2.60018                   55      0.548801       90.1764               512 │
│ trial_0195f    RUNNING                  481             3        0.034166        0.68654              0.613953   2.28701                   49      0.108157      119.964               8159 │
│ trial_0195f    RUNNING                  558             2        0.00744142      0.549635             0.625081   8.77248                   41     14.3318          1.34804             8995 │
│ trial_0195f    RUNNING                  527             3        0.0135209       0.653459             0.597106   6.02627                   57     21.8788          1.27441             3227 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=1839845)[0m [161]	train-mlogloss:1.10774	validation-mlogloss:1.13183
[36m(train_xgb_ray_tune pid=1839855)[0m [87]	train-mlogloss:1.16547	validation-mlogloss:1.21335
[36m(train_xgb_ray_tune pid=1839877)[0m [122]	train-mlogloss:1.46307	validation-mlogloss:1.45088
[36m(train_xgb_ray_tune pid=1839870)[0m [122]	train-mlogloss:1.56550	validation-mlogloss:1.54630
[36m(train_xgb_ray_tune pid=1839862)[0m [87]	train-mlogloss:1.20377	validation-mlogloss:1.22255
[36m(train_xgb_ray_tune pid=1839871)[0m [106]	train-mlogloss:1.19653	validation-mlogloss:1.19911
[36m(train_xgb_ray_tune pid=1839878)[0m [142]	train-mlogloss:1.14679	validation-mlogloss:1.17539
[36m(train_xgb_ray_tune pid=1839848)[0m [125]	train-mlogloss:1.31279	validation-mlogloss:1.29902
[36m(train_xgb_ray_tune pid=1839879)[0m [128]	train-mlogloss:1.31948	validation-mlogloss:1.30134
[36m(train_xgb_ray_tune pid=1839856)[0m [139]	train-mlogloss:2.17129	validation-mlogloss:2.16834
[36m(train_xgb_ray_tune pid=1839880)[0m [110]	train-mlogloss:1.91304	validation-mlogloss:1.92535
[36m(train_xgb_ray_tune pid=1839849)[0m [83]	train-mlogloss:1.67824	validation-mlogloss:1.70288
[36m(train_xgb_ray_tune pid=1839846)[0m [112]	train-mlogloss:1.47193	validation-mlogloss:1.47892
[36m(train_xgb_ray_tune pid=1839845)[0m [162]	train-mlogloss:1.10595	validation-mlogloss:1.13095
[36m(train_xgb_ray_tune pid=1839870)[0m [123]	train-mlogloss:1.56255	validation-mlogloss:1.54310
[36m(train_xgb_ray_tune pid=1839871)[0m [107]	train-mlogloss:1.19363	validation-mlogloss:1.19706
[36m(train_xgb_ray_tune pid=1839887)[0m [97]	train-mlogloss:1.43097	validation-mlogloss:1.42914
[36m(train_xgb_ray_tune pid=1839848)[0m [126]	train-mlogloss:1.31009	validation-mlogloss:1.29625
[36m(train_xgb_ray_tune pid=1839881)[0m [125]	train-mlogloss:1.31698	validation-mlogloss:1.31093
[36m(train_xgb_ray_tune pid=1839869)[0m [89]	train-mlogloss:1.18869	validation-mlogloss:1.23247
[36m(train_xgb_ray_tune pid=1839868)[0m [105]	train-mlogloss:1.53782	validation-mlogloss:1.52129
[36m(train_xgb_ray_tune pid=1839894)[0m [82]	train-mlogloss:1.33519	validation-mlogloss:1.35750
[36m(train_xgb_ray_tune pid=1839877)[0m [123]	train-mlogloss:1.45969	validation-mlogloss:1.44713
[36m(train_xgb_ray_tune pid=1839845)[0m [163]	train-mlogloss:1.10412	validation-mlogloss:1.12989
[36m(train_xgb_ray_tune pid=1839855)[0m [88]	train-mlogloss:1.16152	validation-mlogloss:1.20970
[36m(train_xgb_ray_tune pid=1839847)[0m [84]	train-mlogloss:1.31266	validation-mlogloss:1.32981
[36m(train_xgb_ray_tune pid=1839862)[0m [88]	train-mlogloss:1.19965	validation-mlogloss:1.21882
[36m(train_xgb_ray_tune pid=1839887)[0m [98]	train-mlogloss:1.42662	validation-mlogloss:1.42471
[36m(train_xgb_ray_tune pid=1839878)[0m [143]	train-mlogloss:1.14469	validation-mlogloss:1.17343
[36m(train_xgb_ray_tune pid=1839848)[0m [127]	train-mlogloss:1.30738	validation-mlogloss:1.29356
[36m(train_xgb_ray_tune pid=1839879)[0m [129]	train-mlogloss:1.31656	validation-mlogloss:1.29825
[36m(train_xgb_ray_tune pid=1839856)[0m [140]	train-mlogloss:2.16851	validation-mlogloss:2.16551
[36m(train_xgb_ray_tune pid=1839893)[0m [78]	train-mlogloss:1.29254	validation-mlogloss:1.31526
[36m(train_xgb_ray_tune pid=1839880)[0m [111]	train-mlogloss:1.90904	validation-mlogloss:1.92118
[36m(train_xgb_ray_tune pid=1839846)[0m [113]	train-mlogloss:1.46804	validation-mlogloss:1.47493
[36m(train_xgb_ray_tune pid=1839869)[0m [90]	train-mlogloss:1.18490	validation-mlogloss:1.22905
[36m(train_xgb_ray_tune pid=1839871)[0m [108]	train-mlogloss:1.19063	validation-mlogloss:1.19486
[36m(train_xgb_ray_tune pid=1839881)[0m [126]	train-mlogloss:1.31393	validation-mlogloss:1.30823
[36m(train_xgb_ray_tune pid=1839849)[0m [84]	train-mlogloss:1.67250	validation-mlogloss:1.69701
[36m(train_xgb_ray_tune pid=1839845)[0m [164]	train-mlogloss:1.10235	validation-mlogloss:1.12883
[36m(train_xgb_ray_tune pid=1839877)[0m [124]	train-mlogloss:1.45625	validation-mlogloss:1.44345
[36m(train_xgb_ray_tune pid=1839870)[0m [124]	train-mlogloss:1.55991	validation-mlogloss:1.54035
[36m(train_xgb_ray_tune pid=1839862)[0m [89]	train-mlogloss:1.19637	validation-mlogloss:1.21559
[36m(train_xgb_ray_tune pid=1839878)[0m [144]	train-mlogloss:1.14274	validation-mlogloss:1.17200
[36m(train_xgb_ray_tune pid=1839879)[0m [130]	train-mlogloss:1.31363	validation-mlogloss:1.29545
[36m(train_xgb_ray_tune pid=1839856)[0m [141]	train-mlogloss:2.16572	validation-mlogloss:2.16265
[36m(train_xgb_ray_tune pid=1839894)[0m [83]	train-mlogloss:1.32978	validation-mlogloss:1.35258
[36m(train_xgb_ray_tune pid=1839868)[0m [106]	train-mlogloss:1.53418	validation-mlogloss:1.51757
[36m(train_xgb_ray_tune pid=1839848)[0m [128]	train-mlogloss:1.30450	validation-mlogloss:1.29092
[36m(train_xgb_ray_tune pid=1839846)[0m [114]	train-mlogloss:1.46418	validation-mlogloss:1.47105
[36m(train_xgb_ray_tune pid=1839845)[0m [165]	train-mlogloss:1.10079	validation-mlogloss:1.12788
[36m(train_xgb_ray_tune pid=1839877)[0m [125]	train-mlogloss:1.45320	validation-mlogloss:1.44035
[36m(train_xgb_ray_tune pid=1839871)[0m [109]	train-mlogloss:1.18772	validation-mlogloss:1.19225
[36m(train_xgb_ray_tune pid=1839887)[0m [99]	train-mlogloss:1.42183	validation-mlogloss:1.41982
[36m(train_xgb_ray_tune pid=1839878)[0m [145]	train-mlogloss:1.14077	validation-mlogloss:1.17017
[36m(train_xgb_ray_tune pid=1839879)[0m [131]	train-mlogloss:1.31105	validation-mlogloss:1.29309
[36m(train_xgb_ray_tune pid=1839881)[0m [127]	train-mlogloss:1.31093	validation-mlogloss:1.30518
[36m(train_xgb_ray_tune pid=1839856)[0m [142]	train-mlogloss:2.16296	validation-mlogloss:2.15975
[36m(train_xgb_ray_tune pid=1839869)[0m [91]	train-mlogloss:1.18136	validation-mlogloss:1.22598
[36m(train_xgb_ray_tune pid=1839880)[0m [112]	train-mlogloss:1.90515	validation-mlogloss:1.91709
[36m(train_xgb_ray_tune pid=1839845)[0m [166]	train-mlogloss:1.09914	validation-mlogloss:1.12679
[36m(train_xgb_ray_tune pid=1839855)[0m [89]	train-mlogloss:1.15756	validation-mlogloss:1.20632
[36m(train_xgb_ray_tune pid=1839847)[0m [85]	train-mlogloss:1.30776	validation-mlogloss:1.32519
[36m(train_xgb_ray_tune pid=1839870)[0m [125]	train-mlogloss:1.55698	validation-mlogloss:1.53721
[36m(train_xgb_ray_tune pid=1839862)[0m [90]	train-mlogloss:1.19309	validation-mlogloss:1.21244
[36m(train_xgb_ray_tune pid=1839871)[0m [110]	train-mlogloss:1.18480	validation-mlogloss:1.18981
[36m(train_xgb_ray_tune pid=1839878)[0m [146]	train-mlogloss:1.13893	validation-mlogloss:1.16859
[36m(train_xgb_ray_tune pid=1839848)[0m [129]	train-mlogloss:1.30160	validation-mlogloss:1.28799
[36m(train_xgb_ray_tune pid=1839881)[0m [128]	train-mlogloss:1.30776	validation-mlogloss:1.30196
[36m(train_xgb_ray_tune pid=1839893)[0m [79]	train-mlogloss:1.28741	validation-mlogloss:1.31020
[36m(train_xgb_ray_tune pid=1839894)[0m [84]	train-mlogloss:1.32458	validation-mlogloss:1.34782
[36m(train_xgb_ray_tune pid=1839880)[0m [113]	train-mlogloss:1.90118	validation-mlogloss:1.91302
[36m(train_xgb_ray_tune pid=1839849)[0m [85]	train-mlogloss:1.66684	validation-mlogloss:1.69131
[36m(train_xgb_ray_tune pid=1839845)[0m [167]	train-mlogloss:1.09752	validation-mlogloss:1.12542
[36m(train_xgb_ray_tune pid=1839877)[0m [126]	train-mlogloss:1.44983	validation-mlogloss:1.43701
[36m(train_xgb_ray_tune pid=1839887)[0m [100]	train-mlogloss:1.41758	validation-mlogloss:1.41550
[36m(train_xgb_ray_tune pid=1839878)[0m [147]	train-mlogloss:1.13660	validation-mlogloss:1.16661
[36m(train_xgb_ray_tune pid=1839848)[0m [130]	train-mlogloss:1.29893	validation-mlogloss:1.28554
[36m(train_xgb_ray_tune pid=1839879)[0m [132]	train-mlogloss:1.30838	validation-mlogloss:1.29055
[36m(train_xgb_ray_tune pid=1839881)[0m [129]	train-mlogloss:1.30483	validation-mlogloss:1.29930
[36m(train_xgb_ray_tune pid=1839856)[0m [143]	train-mlogloss:2.16026	validation-mlogloss:2.15706
[36m(train_xgb_ray_tune pid=1839869)[0m [92]	train-mlogloss:1.17751	validation-mlogloss:1.22287
[36m(train_xgb_ray_tune pid=1839846)[0m [115]	train-mlogloss:1.46041	validation-mlogloss:1.46716
[36m(train_xgb_ray_tune pid=1839845)[0m [168]	train-mlogloss:1.09585	validation-mlogloss:1.12427
[36m(train_xgb_ray_tune pid=1839855)[0m [90]	train-mlogloss:1.15335	validation-mlogloss:1.20284
[36m(train_xgb_ray_tune pid=1839877)[0m [127]	train-mlogloss:1.44659	validation-mlogloss:1.43354
[36m(train_xgb_ray_tune pid=1839847)[0m [86]	train-mlogloss:1.30273	validation-mlogloss:1.32067
[36m(train_xgb_ray_tune pid=1839870)[0m [126]	train-mlogloss:1.55407	validation-mlogloss:1.53408
[36m(train_xgb_ray_tune pid=1839862)[0m [91]	train-mlogloss:1.18946	validation-mlogloss:1.20889
[36m(train_xgb_ray_tune pid=1839871)[0m [111]	train-mlogloss:1.18179	validation-mlogloss:1.18692
[36m(train_xgb_ray_tune pid=1839887)[0m [101]	train-mlogloss:1.41321	validation-mlogloss:1.41096
[36m(train_xgb_ray_tune pid=1839878)[0m [148]	train-mlogloss:1.13475	validation-mlogloss:1.16521
[36m(train_xgb_ray_tune pid=1839879)[0m [133]	train-mlogloss:1.30547	validation-mlogloss:1.28745
[36m(train_xgb_ray_tune pid=1839856)[0m [144]	train-mlogloss:2.15738	validation-mlogloss:2.15410
[36m(train_xgb_ray_tune pid=1839893)[0m [80]	train-mlogloss:1.28227	validation-mlogloss:1.30524
[36m(train_xgb_ray_tune pid=1839868)[0m [107]	train-mlogloss:1.53031	validation-mlogloss:1.51342
[36m(train_xgb_ray_tune pid=1839880)[0m [114]	train-mlogloss:1.89717	validation-mlogloss:1.90894
[36m(train_xgb_ray_tune pid=1839849)[0m [86]	train-mlogloss:1.66100	validation-mlogloss:1.68557
[36m(train_xgb_ray_tune pid=1839848)[0m [131]	train-mlogloss:1.29628	validation-mlogloss:1.28288
[36m(train_xgb_ray_tune pid=1839869)[0m [93]	train-mlogloss:1.17419	validation-mlogloss:1.21964
[36m(train_xgb_ray_tune pid=1839845)[0m [169]	train-mlogloss:1.09436	validation-mlogloss:1.12334
[36m(train_xgb_ray_tune pid=1839871)[0m [112]	train-mlogloss:1.17885	validation-mlogloss:1.18455
[36m(train_xgb_ray_tune pid=1839879)[0m [134]	train-mlogloss:1.30311	validation-mlogloss:1.28515
[36m(train_xgb_ray_tune pid=1839881)[0m [130]	train-mlogloss:1.30179	validation-mlogloss:1.29651
[36m(train_xgb_ray_tune pid=1839856)[0m [145]	train-mlogloss:2.15456	validation-mlogloss:2.15117
[36m(train_xgb_ray_tune pid=1839894)[0m [85]	train-mlogloss:1.31922	validation-mlogloss:1.34302
[36m(train_xgb_ray_tune pid=1839880)[0m [115]	train-mlogloss:1.89327	validation-mlogloss:1.90487
[36m(train_xgb_ray_tune pid=1839878)[0m [149]	train-mlogloss:1.13309	validation-mlogloss:1.16374
[36m(train_xgb_ray_tune pid=1839862)[0m [92]	train-mlogloss:1.18611	validation-mlogloss:1.20589
[36m(train_xgb_ray_tune pid=1839845)[0m [170]	train-mlogloss:1.09274	validation-mlogloss:1.12212
[36m(train_xgb_ray_tune pid=1839855)[0m [91]	train-mlogloss:1.14920	validation-mlogloss:1.19954
[36m(train_xgb_ray_tune pid=1839877)[0m [128]	train-mlogloss:1.44352	validation-mlogloss:1.43009
[36m(train_xgb_ray_tune pid=1839847)[0m [87]	train-mlogloss:1.29785	validation-mlogloss:1.31607
[36m(train_xgb_ray_tune pid=1839870)[0m [127]	train-mlogloss:1.55118	validation-mlogloss:1.53103
[36m(train_xgb_ray_tune pid=1839887)[0m [102]	train-mlogloss:1.40915	validation-mlogloss:1.40678
[36m(train_xgb_ray_tune pid=1839848)[0m [132]	train-mlogloss:1.29359	validation-mlogloss:1.28031
[36m(train_xgb_ray_tune pid=1839881)[0m [131]	train-mlogloss:1.29882	validation-mlogloss:1.29385
[36m(train_xgb_ray_tune pid=1839856)[0m [146]	train-mlogloss:2.15181	validation-mlogloss:2.14835
[36m(train_xgb_ray_tune pid=1839869)[0m [94]	train-mlogloss:1.17061	validation-mlogloss:1.21631
[36m(train_xgb_ray_tune pid=1839893)[0m [81]	train-mlogloss:1.27703	validation-mlogloss:1.30068
[36m(train_xgb_ray_tune pid=1839849)[0m [87]	train-mlogloss:1.65536	validation-mlogloss:1.67985
[36m(train_xgb_ray_tune pid=1839879)[0m [135]	train-mlogloss:1.30059	validation-mlogloss:1.28250
[36m(train_xgb_ray_tune pid=1839846)[0m [116]	train-mlogloss:1.45685	validation-mlogloss:1.46351
[36m(train_xgb_ray_tune pid=1839845)[0m [171]	train-mlogloss:1.09124	validation-mlogloss:1.12146
[36m(train_xgb_ray_tune pid=1839877)[0m [129]	train-mlogloss:1.44030	validation-mlogloss:1.42647
[36m(train_xgb_ray_tune pid=1839862)[0m [93]	train-mlogloss:1.18306	validation-mlogloss:1.20301
[36m(train_xgb_ray_tune pid=1839871)[0m [113]	train-mlogloss:1.17593	validation-mlogloss:1.18181
[36m(train_xgb_ray_tune pid=1839878)[0m [150]	train-mlogloss:1.13154	validation-mlogloss:1.16239
[36m(train_xgb_ray_tune pid=1839848)[0m [133]	train-mlogloss:1.29086	validation-mlogloss:1.27764
[36m(train_xgb_ray_tune pid=1839881)[0m [132]	train-mlogloss:1.29584	validation-mlogloss:1.29079
[36m(train_xgb_ray_tune pid=1839868)[0m [108]	train-mlogloss:1.52655	validation-mlogloss:1.50938
[36m(train_xgb_ray_tune pid=1839880)[0m [116]	train-mlogloss:1.88960	validation-mlogloss:1.90112
[36m(train_xgb_ray_tune pid=1839845)[0m [172]	train-mlogloss:1.08964	validation-mlogloss:1.12019
[36m(train_xgb_ray_tune pid=1839855)[0m [92]	train-mlogloss:1.14547	validation-mlogloss:1.19639
[36m(train_xgb_ray_tune pid=1839847)[0m [88]	train-mlogloss:1.29311	validation-mlogloss:1.31162
[36m(train_xgb_ray_tune pid=1839870)[0m [128]	train-mlogloss:1.54854	validation-mlogloss:1.52832
[36m(train_xgb_ray_tune pid=1839871)[0m [114]	train-mlogloss:1.17322	validation-mlogloss:1.17926
[36m(train_xgb_ray_tune pid=1839887)[0m [103]	train-mlogloss:1.40500	validation-mlogloss:1.40277
[36m(train_xgb_ray_tune pid=1839878)[0m [151]	train-mlogloss:1.12998	validation-mlogloss:1.16113
[36m(train_xgb_ray_tune pid=1839848)[0m [134]	train-mlogloss:1.28810	validation-mlogloss:1.27523
[36m(train_xgb_ray_tune pid=1839879)[0m [136]	train-mlogloss:1.29810	validation-mlogloss:1.28021
[36m(train_xgb_ray_tune pid=1839856)[0m [147]	train-mlogloss:2.14895	validation-mlogloss:2.14538
[36m(train_xgb_ray_tune pid=1839869)[0m [95]	train-mlogloss:1.16741	validation-mlogloss:1.21284
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[36m(train_xgb_ray_tune pid=1839869)[0m [100]	train-mlogloss:1.15188	validation-mlogloss:1.19803
[36m(train_xgb_ray_tune pid=1839893)[0m [89]	train-mlogloss:1.23855	validation-mlogloss:1.26658
[36m(train_xgb_ray_tune pid=1839894)[0m [93]	train-mlogloss:1.28003	validation-mlogloss:1.30651
[36m(train_xgb_ray_tune pid=1839862)[0m [102]	train-mlogloss:1.15718	validation-mlogloss:1.17932
[36m(train_xgb_ray_tune pid=1839846)[0m [125]	train-mlogloss:1.42659	validation-mlogloss:1.43283
[36m(train_xgb_ray_tune pid=1839845)[0m [188]	train-mlogloss:1.06576	validation-mlogloss:1.10650
[36m(train_xgb_ray_tune pid=1839855)[0m [97]	train-mlogloss:1.12746	validation-mlogloss:1.18106
[36m(train_xgb_ray_tune pid=1839870)[0m [138]	train-mlogloss:1.52181	validation-mlogloss:1.50027
[36m(train_xgb_ray_tune pid=1839871)[0m [125]	train-mlogloss:1.14531	validation-mlogloss:1.15794
[36m(train_xgb_ray_tune pid=1839887)[0m [110]	train-mlogloss:1.37729	validation-mlogloss:1.37439
[36m(train_xgb_ray_tune pid=1839878)[0m [164]	train-mlogloss:1.10964	validation-mlogloss:1.14447
[36m(train_xgb_ray_tune pid=1839848)[0m [145]	train-mlogloss:1.26110	validation-mlogloss:1.24903
[36m(train_xgb_ray_tune pid=1839879)[0m [147]	train-mlogloss:1.27267	validation-mlogloss:1.25529
[36m(train_xgb_ray_tune pid=1839881)[0m [144]	train-mlogloss:1.26343	validation-mlogloss:1.25978
[36m(train_xgb_ray_tune pid=1839856)[0m [157]	train-mlogloss:2.12275	validation-mlogloss:2.11865
[36m(train_xgb_ray_tune pid=1839868)[0m [113]	train-mlogloss:1.50880	validation-mlogloss:1.49107
[36m(train_xgb_ray_tune pid=1839880)[0m [126]	train-mlogloss:1.85263	validation-mlogloss:1.86258
[36m(train_xgb_ray_tune pid=1839849)[0m [95]	train-mlogloss:1.61318	validation-mlogloss:1.63767
[36m(train_xgb_ray_tune pid=1839869)[0m [101]	train-mlogloss:1.14894	validation-mlogloss:1.19523
[36m(train_xgb_ray_tune pid=1839846)[0m [126]	train-mlogloss:1.42328	validation-mlogloss:1.42963
[36m(train_xgb_ray_tune pid=1839845)[0m [189]	train-mlogloss:1.06440	validation-mlogloss:1.10571
[36m(train_xgb_ray_tune pid=1839877)[0m [140]	train-mlogloss:1.40763	validation-mlogloss:1.39248
[36m(train_xgb_ray_tune pid=1839870)[0m [139]	train-mlogloss:1.51913	validation-mlogloss:1.49749
[36m(train_xgb_ray_tune pid=1839862)[0m [103]	train-mlogloss:1.15415	validation-mlogloss:1.17660
[36m(train_xgb_ray_tune pid=1839878)[0m [165]	train-mlogloss:1.10810	validation-mlogloss:1.14300
[36m(train_xgb_ray_tune pid=1839848)[0m [146]	train-mlogloss:1.25892	validation-mlogloss:1.24697
[36m(train_xgb_ray_tune pid=1839881)[0m [145]	train-mlogloss:1.26089	validation-mlogloss:1.25746
[36m(train_xgb_ray_tune pid=1839856)[0m [158]	train-mlogloss:2.12005	validation-mlogloss:2.11583
[36m(train_xgb_ray_tune pid=1839893)[0m [90]	train-mlogloss:1.23431	validation-mlogloss:1.26273
[36m(train_xgb_ray_tune pid=1839894)[0m [94]	train-mlogloss:1.27556	validation-mlogloss:1.30250
[36m(train_xgb_ray_tune pid=1839880)[0m [127]	train-mlogloss:1.84894	validation-mlogloss:1.85860
[36m(train_xgb_ray_tune pid=1839845)[0m [190]	train-mlogloss:1.06305	validation-mlogloss:1.10531
[36m(train_xgb_ray_tune pid=1839855)[0m [98]	train-mlogloss:1.12378	validation-mlogloss:1.17848
[36m(train_xgb_ray_tune pid=1839877)[0m [141]	train-mlogloss:1.40491	validation-mlogloss:1.38976
[36m(train_xgb_ray_tune pid=1839847)[0m [95]	train-mlogloss:1.26179	validation-mlogloss:1.28333
[36m(train_xgb_ray_tune pid=1839871)[0m [126]	train-mlogloss:1.14296	validation-mlogloss:1.15619
[36m(train_xgb_ray_tune pid=1839878)[0m [166]	train-mlogloss:1.10665	validation-mlogloss:1.14176
[36m(train_xgb_ray_tune pid=1839848)[0m [147]	train-mlogloss:1.25673	validation-mlogloss:1.24506
[36m(train_xgb_ray_tune pid=1839879)[0m [148]	train-mlogloss:1.27053	validation-mlogloss:1.25345
[36m(train_xgb_ray_tune pid=1839869)[0m [102]	train-mlogloss:1.14585	validation-mlogloss:1.19286
[36m(train_xgb_ray_tune pid=1839849)[0m [96]	train-mlogloss:1.60831	validation-mlogloss:1.63274
[36m(train_xgb_ray_tune pid=1839846)[0m [127]	train-mlogloss:1.42017	validation-mlogloss:1.42636
[36m(train_xgb_ray_tune pid=1839845)[0m [191]	train-mlogloss:1.06158	validation-mlogloss:1.10470
[36m(train_xgb_ray_tune pid=1839887)[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_xgb_ray_tune pid=1839887)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=1839849)[0m 2025-10-25 19:42:46.378147: 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] (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_xgb_ray_tune pid=1839849)[0m 2025-10-25 19:42:46.401746: 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_xgb_ray_tune pid=1839849)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR[32m [repeated 19x across cluster][0m
[36m(train_xgb_ray_tune pid=1839849)[0m E0000 00:00:1761414166.431802 1841250 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_xgb_ray_tune pid=1839849)[0m E0000 00:00:1761414166.440474 1841250 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_xgb_ray_tune pid=1839849)[0m W0000 00:00:1761414166.463536 1841250 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_xgb_ray_tune pid=1839849)[0m 2025-10-25 19:42:46.469772: 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_xgb_ray_tune pid=1839849)[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_xgb_ray_tune pid=1839870)[0m [140]	train-mlogloss:1.51666	validation-mlogloss:1.49483
[36m(train_xgb_ray_tune pid=1839862)[0m [104]	train-mlogloss:1.15154	validation-mlogloss:1.17451
[36m(train_xgb_ray_tune pid=1839871)[0m [127]	train-mlogloss:1.14043	validation-mlogloss:1.15450
[36m(train_xgb_ray_tune pid=1839879)[0m [149]	train-mlogloss:1.26830	validation-mlogloss:1.25139
[36m(train_xgb_ray_tune pid=1839881)[0m [146]	train-mlogloss:1.25838	validation-mlogloss:1.25534
[36m(train_xgb_ray_tune pid=1839856)[0m [159]	train-mlogloss:2.11749	validation-mlogloss:2.11333
[36m(train_xgb_ray_tune pid=1839893)[0m [91]	train-mlogloss:1.22991	validation-mlogloss:1.25860
[36m(train_xgb_ray_tune pid=1839868)[0m [114]	train-mlogloss:1.50550	validation-mlogloss:1.48780
[36m(train_xgb_ray_tune pid=1839880)[0m [128]	train-mlogloss:1.84520	validation-mlogloss:1.85471
[36m(train_xgb_ray_tune pid=1839869)[0m [103]	train-mlogloss:1.14315	validation-mlogloss:1.19030
[36m(train_xgb_ray_tune pid=1839845)[0m [192]	train-mlogloss:1.06026	validation-mlogloss:1.10393
[36m(train_xgb_ray_tune pid=1839855)[0m [99]	train-mlogloss:1.12023	validation-mlogloss:1.17513
[36m(train_xgb_ray_tune pid=1839877)[0m [142]	train-mlogloss:1.40213	validation-mlogloss:1.38663
[36m(train_xgb_ray_tune pid=1839847)[0m [96]	train-mlogloss:1.25750	validation-mlogloss:1.27935
[36m(train_xgb_ray_tune pid=1839870)[0m [141]	train-mlogloss:1.51417	validation-mlogloss:1.49220
[36m(train_xgb_ray_tune pid=1839878)[0m [167]	train-mlogloss:1.10545	validation-mlogloss:1.14048
[36m(train_xgb_ray_tune pid=1839848)[0m [148]	train-mlogloss:1.25445	validation-mlogloss:1.24303
[36m(train_xgb_ray_tune pid=1839881)[0m [147]	train-mlogloss:1.25613	validation-mlogloss:1.25314
[36m(train_xgb_ray_tune pid=1839856)[0m [160]	train-mlogloss:2.11487	validation-mlogloss:2.11064
[36m(train_xgb_ray_tune pid=1839894)[0m [95]	train-mlogloss:1.27106	validation-mlogloss:1.29839
[36m(train_xgb_ray_tune pid=1839880)[0m [129]	train-mlogloss:1.84169	validation-mlogloss:1.85116
[36m(train_xgb_ray_tune pid=1839849)[0m [97]	train-mlogloss:1.60344	validation-mlogloss:1.62784

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:16. Total running time: 34s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             30.7858 │
│ time_total_s                 30.7858 │
│ training_iteration                 1 │
│ validation_accuracy          0.59512 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=1839879)[0m [150]	train-mlogloss:1.26626	validation-mlogloss:1.24956
[36m(train_xgb_ray_tune pid=1839893)[0m [92]	train-mlogloss:1.22563	validation-mlogloss:1.25457
[36m(train_xgb_ray_tune pid=1839846)[0m [128]	train-mlogloss:1.41710	validation-mlogloss:1.42334
[36m(train_xgb_ray_tune pid=1839871)[0m [128]	train-mlogloss:1.13800	validation-mlogloss:1.15217

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:16. Total running time: 34s
[36m(train_xgb_ray_tune pid=1839845)[0m [193]	train-mlogloss:1.05886	validation-mlogloss:1.10336
[36m(train_xgb_ray_tune pid=1839877)[0m [143]	train-mlogloss:1.39941	validation-mlogloss:1.38391
[36m(train_xgb_ray_tune pid=1839862)[0m [105]	train-mlogloss:1.14889	validation-mlogloss:1.17203
[36m(train_xgb_ray_tune pid=1839878)[0m [168]	train-mlogloss:1.10422	validation-mlogloss:1.13942
[36m(train_xgb_ray_tune pid=1839848)[0m [149]	train-mlogloss:1.25225	validation-mlogloss:1.24109
[36m(train_xgb_ray_tune pid=1839856)[0m [161]	train-mlogloss:2.11231	validation-mlogloss:2.10800
[36m(train_xgb_ray_tune pid=1839869)[0m [104]	train-mlogloss:1.14007	validation-mlogloss:1.18750
[36m(train_xgb_ray_tune pid=1839870)[0m [142]	train-mlogloss:1.51174	validation-mlogloss:1.48974
[36m(train_xgb_ray_tune pid=1839881)[0m [148]	train-mlogloss:1.25372	validation-mlogloss:1.25093
[36m(train_xgb_ray_tune pid=1839880)[0m [130]	train-mlogloss:1.83809	validation-mlogloss:1.84728
[36m(train_xgb_ray_tune pid=1839846)[0m [129]	train-mlogloss:1.41406	validation-mlogloss:1.42025
[36m(train_xgb_ray_tune pid=1839845)[0m [194]	train-mlogloss:1.05744	validation-mlogloss:1.10258
[36m(train_xgb_ray_tune pid=1839855)[0m [100]	train-mlogloss:1.11701	validation-mlogloss:1.17220
[36m(train_xgb_ray_tune pid=1839847)[0m [97]	train-mlogloss:1.25337	validation-mlogloss:1.27609
[36m(train_xgb_ray_tune pid=1839871)[0m [129]	train-mlogloss:1.13575	validation-mlogloss:1.15053
[36m(train_xgb_ray_tune pid=1839878)[0m [169]	train-mlogloss:1.10264	validation-mlogloss:1.13816
[36m(train_xgb_ray_tune pid=1839879)[0m [151]	train-mlogloss:1.26413	validation-mlogloss:1.24766
[36m(train_xgb_ray_tune pid=1839868)[0m [115]	train-mlogloss:1.50216	validation-mlogloss:1.48408
[36m(train_xgb_ray_tune pid=1839894)[0m [96]	train-mlogloss:1.26662	validation-mlogloss:1.29444
[36m(train_xgb_ray_tune pid=1839849)[0m [98]	train-mlogloss:1.59854	validation-mlogloss:1.62281
[36m(train_xgb_ray_tune pid=1839869)[0m [105]	train-mlogloss:1.13726	validation-mlogloss:1.18524
[36m(train_xgb_ray_tune pid=1839893)[0m [93]	train-mlogloss:1.22156	validation-mlogloss:1.25153
[36m(train_xgb_ray_tune pid=1839862)[0m [106]	train-mlogloss:1.14664	validation-mlogloss:1.17004
[36m(train_xgb_ray_tune pid=1839845)[0m [195]	train-mlogloss:1.05613	validation-mlogloss:1.10167
[36m(train_xgb_ray_tune pid=1839877)[0m [144]	train-mlogloss:1.39667	validation-mlogloss:1.38111
[36m(train_xgb_ray_tune pid=1839870)[0m [143]	train-mlogloss:1.50939	validation-mlogloss:1.48728
[36m(train_xgb_ray_tune pid=1839871)[0m [130]	train-mlogloss:1.13339	validation-mlogloss:1.14898
[36m(train_xgb_ray_tune pid=1839878)[0m [170]	train-mlogloss:1.10123	validation-mlogloss:1.13702
[36m(train_xgb_ray_tune pid=1839848)[0m [150]	train-mlogloss:1.25006	validation-mlogloss:1.23914
[36m(train_xgb_ray_tune pid=1839879)[0m [152]	train-mlogloss:1.26207	validation-mlogloss:1.24566
[36m(train_xgb_ray_tune pid=1839881)[0m [149]	train-mlogloss:1.25141	validation-mlogloss:1.24875
[36m(train_xgb_ray_tune pid=1839856)[0m [162]	train-mlogloss:2.10979	validation-mlogloss:2.10543
[36m(train_xgb_ray_tune pid=1839880)[0m [131]	train-mlogloss:1.83463	validation-mlogloss:1.84366
[36m(train_xgb_ray_tune pid=1839846)[0m [130]	train-mlogloss:1.41107	validation-mlogloss:1.41738
[36m(train_xgb_ray_tune pid=1839845)[0m [196]	train-mlogloss:1.05478	validation-mlogloss:1.10105
[36m(train_xgb_ray_tune pid=1839855)[0m [101]	train-mlogloss:1.11397	validation-mlogloss:1.16941
[36m(train_xgb_ray_tune pid=1839877)[0m [145]	train-mlogloss:1.39409	validation-mlogloss:1.37853
[36m(train_xgb_ray_tune pid=1839847)[0m [98]	train-mlogloss:1.24918	validation-mlogloss:1.27229
[36m(train_xgb_ray_tune pid=1839870)[0m [144]	train-mlogloss:1.50687	validation-mlogloss:1.48463
[36m(train_xgb_ray_tune pid=1839862)[0m [107]	train-mlogloss:1.14406	validation-mlogloss:1.16775
[36m(train_xgb_ray_tune pid=1839871)[0m [131]	train-mlogloss:1.13118	validation-mlogloss:1.14754
[36m(train_xgb_ray_tune pid=1839878)[0m [171]	train-mlogloss:1.10002	validation-mlogloss:1.13597
[36m(train_xgb_ray_tune pid=1839848)[0m [151]	train-mlogloss:1.24786	validation-mlogloss:1.23721
[36m(train_xgb_ray_tune pid=1839879)[0m [153]	train-mlogloss:1.26000	validation-mlogloss:1.24394
[36m(train_xgb_ray_tune pid=1839881)[0m [150]	train-mlogloss:1.24904	validation-mlogloss:1.24684
[36m(train_xgb_ray_tune pid=1839856)[0m [163]	train-mlogloss:2.10738	validation-mlogloss:2.10301
[36m(train_xgb_ray_tune pid=1839869)[0m [106]	train-mlogloss:1.13418	validation-mlogloss:1.18259
[36m(train_xgb_ray_tune pid=1839894)[0m [97]	train-mlogloss:1.26224	validation-mlogloss:1.29038
[36m(train_xgb_ray_tune pid=1839849)[0m [99]	train-mlogloss:1.59376	validation-mlogloss:1.61795
[36m(train_xgb_ray_tune pid=1839846)[0m [131]	train-mlogloss:1.40785	validation-mlogloss:1.41423
[36m(train_xgb_ray_tune pid=1839878)[0m [172]	train-mlogloss:1.09887	validation-mlogloss:1.13502
[36m(train_xgb_ray_tune pid=1839893)[0m [94]	train-mlogloss:1.21749	validation-mlogloss:1.24800
[36m(train_xgb_ray_tune pid=1839868)[0m [116]	train-mlogloss:1.49891	validation-mlogloss:1.48051
[36m(train_xgb_ray_tune pid=1839880)[0m [132]	train-mlogloss:1.83129	validation-mlogloss:1.84014
[36m(train_xgb_ray_tune pid=1839870)[0m [145]	train-mlogloss:1.50470	validation-mlogloss:1.48223
[36m(train_xgb_ray_tune pid=1839871)[0m [132]	train-mlogloss:1.12895	validation-mlogloss:1.14596
[36m(train_xgb_ray_tune pid=1839879)[0m [154]	train-mlogloss:1.25787	validation-mlogloss:1.24201
[36m(train_xgb_ray_tune pid=1839881)[0m [151]	train-mlogloss:1.24681	validation-mlogloss:1.24489
[36m(train_xgb_ray_tune pid=1839856)[0m [164]	train-mlogloss:2.10482	validation-mlogloss:2.10037
[36m(train_xgb_ray_tune pid=1839869)[0m [107]	train-mlogloss:1.13103	validation-mlogloss:1.18021
[36m(train_xgb_ray_tune pid=1839877)[0m [146]	train-mlogloss:1.39157	validation-mlogloss:1.37576
[36m(train_xgb_ray_tune pid=1839846)[0m [132]	train-mlogloss:1.40491	validation-mlogloss:1.41122
[36m(train_xgb_ray_tune pid=1839845)[0m [197]	train-mlogloss:1.05333	validation-mlogloss:1.10034
[36m(train_xgb_ray_tune pid=1839855)[0m [102]	train-mlogloss:1.11065	validation-mlogloss:1.16676
[36m(train_xgb_ray_tune pid=1839847)[0m [99]	train-mlogloss:1.24521	validation-mlogloss:1.26918
[36m(train_xgb_ray_tune pid=1839862)[0m [108]	train-mlogloss:1.14158	validation-mlogloss:1.16542
[36m(train_xgb_ray_tune pid=1839878)[0m [173]	train-mlogloss:1.09732	validation-mlogloss:1.13390
[36m(train_xgb_ray_tune pid=1839848)[0m [152]	train-mlogloss:1.24578	validation-mlogloss:1.23542
[36m(train_xgb_ray_tune pid=1839894)[0m [98]	train-mlogloss:1.25802	validation-mlogloss:1.28675
[36m(train_xgb_ray_tune pid=1839880)[0m [133]	train-mlogloss:1.82778	validation-mlogloss:1.83653
[36m(train_xgb_ray_tune pid=1839877)[0m [147]	train-mlogloss:1.38898	validation-mlogloss:1.37283
[36m(train_xgb_ray_tune pid=1839870)[0m [146]	train-mlogloss:1.50221	validation-mlogloss:1.47975
[36m(train_xgb_ray_tune pid=1839862)[0m [109]	train-mlogloss:1.13933	validation-mlogloss:1.16303
[36m(train_xgb_ray_tune pid=1839871)[0m [133]	train-mlogloss:1.12659	validation-mlogloss:1.14449
[36m(train_xgb_ray_tune pid=1839878)[0m [174]	train-mlogloss:1.09607	validation-mlogloss:1.13285
[36m(train_xgb_ray_tune pid=1839848)[0m [153]	train-mlogloss:1.24365	validation-mlogloss:1.23371
[36m(train_xgb_ray_tune pid=1839879)[0m [155]	train-mlogloss:1.25586	validation-mlogloss:1.24042
[36m(train_xgb_ray_tune pid=1839881)[0m [152]	train-mlogloss:1.24441	validation-mlogloss:1.24270
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[36m(train_xgb_ray_tune pid=1839881)[0m [173]	train-mlogloss:1.19980	validation-mlogloss:1.20382
[36m(train_xgb_ray_tune pid=1839856)[0m [186]	train-mlogloss:2.05155	validation-mlogloss:2.04556
[36m(train_xgb_ray_tune pid=1839869)[0m [125]	train-mlogloss:1.08870	validation-mlogloss:1.14308
[36m(train_xgb_ray_tune pid=1839894)[0m [112]	train-mlogloss:1.20440	validation-mlogloss:1.24035
[36m(train_xgb_ray_tune pid=1839849)[0m [113]	train-mlogloss:1.53199	validation-mlogloss:1.55616
[36m(train_xgb_ray_tune pid=1839870)[0m [165]	train-mlogloss:1.46296	validation-mlogloss:1.43863
[36m(train_xgb_ray_tune pid=1839845)[0m [225]	train-mlogloss:1.01787	validation-mlogloss:1.08463
[36m(train_xgb_ray_tune pid=1839855)[0m [115]	train-mlogloss:1.07297	validation-mlogloss:1.13810
[36m(train_xgb_ray_tune pid=1839877)[0m [164]	train-mlogloss:1.34892	validation-mlogloss:1.33240
[36m(train_xgb_ray_tune pid=1839862)[0m [125]	train-mlogloss:1.10523	validation-mlogloss:1.13425
[36m(train_xgb_ray_tune pid=1839878)[0m [201]	train-mlogloss:1.06566	validation-mlogloss:1.10996
[36m(train_xgb_ray_tune pid=1839848)[0m [175]	train-mlogloss:1.20282	validation-mlogloss:1.19882
[36m(train_xgb_ray_tune pid=1839879)[0m [178]	train-mlogloss:1.21512	validation-mlogloss:1.20443
[36m(train_xgb_ray_tune pid=1839856)[0m [187]	train-mlogloss:2.04925	validation-mlogloss:2.04323
[36m(train_xgb_ray_tune pid=1839846)[0m [151]	train-mlogloss:1.35330	validation-mlogloss:1.35954
[36m(train_xgb_ray_tune pid=1839845)[0m [226]	train-mlogloss:1.01678	validation-mlogloss:1.08428
[36m(train_xgb_ray_tune pid=1839847)[0m [114]	train-mlogloss:1.19141	validation-mlogloss:1.22322
[36m(train_xgb_ray_tune pid=1839870)[0m [166]	train-mlogloss:1.46120	validation-mlogloss:1.43675
[36m(train_xgb_ray_tune pid=1839871)[0m [155]	train-mlogloss:1.08459	validation-mlogloss:1.11750
[36m(train_xgb_ray_tune pid=1839878)[0m [202]	train-mlogloss:1.06483	validation-mlogloss:1.10908
[36m(train_xgb_ray_tune pid=1839848)[0m [176]	train-mlogloss:1.20120	validation-mlogloss:1.19746
[36m(train_xgb_ray_tune pid=1839881)[0m [174]	train-mlogloss:1.19790	validation-mlogloss:1.20220
[36m(train_xgb_ray_tune pid=1839869)[0m [126]	train-mlogloss:1.08654	validation-mlogloss:1.14155
[36m(train_xgb_ray_tune pid=1839893)[0m [110]	train-mlogloss:1.15994	validation-mlogloss:1.20143
[36m(train_xgb_ray_tune pid=1839894)[0m [113]	train-mlogloss:1.20111	validation-mlogloss:1.23764
[36m(train_xgb_ray_tune pid=1839880)[0m [151]	train-mlogloss:1.77024	validation-mlogloss:1.77746
[36m(train_xgb_ray_tune pid=1839849)[0m [114]	train-mlogloss:1.52799	validation-mlogloss:1.55225
[36m(train_xgb_ray_tune pid=1839845)[0m [227]	train-mlogloss:1.01545	validation-mlogloss:1.08402
[36m(train_xgb_ray_tune pid=1839877)[0m [165]	train-mlogloss:1.34672	validation-mlogloss:1.33023
[36m(train_xgb_ray_tune pid=1839870)[0m [167]	train-mlogloss:1.45922	validation-mlogloss:1.43474
[36m(train_xgb_ray_tune pid=1839862)[0m [126]	train-mlogloss:1.10363	validation-mlogloss:1.13246
[36m(train_xgb_ray_tune pid=1839871)[0m [156]	train-mlogloss:1.08262	validation-mlogloss:1.11637
[36m(train_xgb_ray_tune pid=1839878)[0m [203]	train-mlogloss:1.06392	validation-mlogloss:1.10864
[36m(train_xgb_ray_tune pid=1839879)[0m [179]	train-mlogloss:1.21376	validation-mlogloss:1.20309
[36m(train_xgb_ray_tune pid=1839881)[0m [175]	train-mlogloss:1.19592	validation-mlogloss:1.20068
[36m(train_xgb_ray_tune pid=1839856)[0m [188]	train-mlogloss:2.04693	validation-mlogloss:2.04077
[36m(train_xgb_ray_tune pid=1839868)[0m [126]	train-mlogloss:1.46817	validation-mlogloss:1.44814
[36m(train_xgb_ray_tune pid=1839846)[0m [152]	train-mlogloss:1.35101	validation-mlogloss:1.35731
[36m(train_xgb_ray_tune pid=1839847)[0m [115]	train-mlogloss:1.18829	validation-mlogloss:1.22057
[36m(train_xgb_ray_tune pid=1839848)[0m [177]	train-mlogloss:1.19958	validation-mlogloss:1.19602
[36m(train_xgb_ray_tune pid=1839855)[0m [116]	train-mlogloss:1.07034	validation-mlogloss:1.13590
[36m(train_xgb_ray_tune pid=1839879)[0m [180]	train-mlogloss:1.21224	validation-mlogloss:1.20168
[36m(train_xgb_ray_tune pid=1839845)[0m [228]	train-mlogloss:1.01435	validation-mlogloss:1.08345
[36m(train_xgb_ray_tune pid=1839894)[0m [114]	train-mlogloss:1.19781	validation-mlogloss:1.23471
[36m(train_xgb_ray_tune pid=1839862)[0m [127]	train-mlogloss:1.10191	validation-mlogloss:1.13124
[36m(train_xgb_ray_tune pid=1839871)[0m [157]	train-mlogloss:1.08087	validation-mlogloss:1.11545
[36m(train_xgb_ray_tune pid=1839878)[0m [204]	train-mlogloss:1.06312	validation-mlogloss:1.10768
[36m(train_xgb_ray_tune pid=1839881)[0m [176]	train-mlogloss:1.19384	validation-mlogloss:1.19904
[36m(train_xgb_ray_tune pid=1839856)[0m [189]	train-mlogloss:2.04452	validation-mlogloss:2.03827
[36m(train_xgb_ray_tune pid=1839869)[0m [127]	train-mlogloss:1.08463	validation-mlogloss:1.13963
[36m(train_xgb_ray_tune pid=1839880)[0m [152]	train-mlogloss:1.76715	validation-mlogloss:1.77433
[36m(train_xgb_ray_tune pid=1839849)[0m [115]	train-mlogloss:1.52403	validation-mlogloss:1.54827
[36m(train_xgb_ray_tune pid=1839845)[0m [229]	train-mlogloss:1.01326	validation-mlogloss:1.08303
[36m(train_xgb_ray_tune pid=1839877)[0m [166]	train-mlogloss:1.34473	validation-mlogloss:1.32814
[36m(train_xgb_ray_tune pid=1839870)[0m [168]	train-mlogloss:1.45738	validation-mlogloss:1.43290
[36m(train_xgb_ray_tune pid=1839879)[0m [181]	train-mlogloss:1.21062	validation-mlogloss:1.20024
[36m(train_xgb_ray_tune pid=1839848)[0m [178]	train-mlogloss:1.19794	validation-mlogloss:1.19454
[36m(train_xgb_ray_tune pid=1839846)[0m [153]	train-mlogloss:1.34870	validation-mlogloss:1.35499
[36m(train_xgb_ray_tune pid=1839845)[0m [230]	train-mlogloss:1.01216	validation-mlogloss:1.08225
[36m(train_xgb_ray_tune pid=1839847)[0m [116]	train-mlogloss:1.18505	validation-mlogloss:1.21757
[36m(train_xgb_ray_tune pid=1839871)[0m [158]	train-mlogloss:1.07929	validation-mlogloss:1.11466
[36m(train_xgb_ray_tune pid=1839878)[0m [205]	train-mlogloss:1.06231	validation-mlogloss:1.10706
[36m(train_xgb_ray_tune pid=1839856)[0m [190]	train-mlogloss:2.04224	validation-mlogloss:2.03595
[36m(train_xgb_ray_tune pid=1839894)[0m [115]	train-mlogloss:1.19455	validation-mlogloss:1.23203
[36m(train_xgb_ray_tune pid=1839880)[0m [153]	train-mlogloss:1.76414	validation-mlogloss:1.77125
[36m(train_xgb_ray_tune pid=1839845)[0m [231]	train-mlogloss:1.01108	validation-mlogloss:1.08201
[36m(train_xgb_ray_tune pid=1839855)[0m [117]	train-mlogloss:1.06786	validation-mlogloss:1.13444
[36m(train_xgb_ray_tune pid=1839877)[0m [167]	train-mlogloss:1.34275	validation-mlogloss:1.32614
[36m(train_xgb_ray_tune pid=1839870)[0m [169]	train-mlogloss:1.45558	validation-mlogloss:1.43096
[36m(train_xgb_ray_tune pid=1839862)[0m [128]	train-mlogloss:1.10030	validation-mlogloss:1.13013
[36m(train_xgb_ray_tune pid=1839871)[0m [159]	train-mlogloss:1.07757	validation-mlogloss:1.11378
[36m(train_xgb_ray_tune pid=1839878)[0m [206]	train-mlogloss:1.06157	validation-mlogloss:1.10622
[36m(train_xgb_ray_tune pid=1839848)[0m [179]	train-mlogloss:1.19640	validation-mlogloss:1.19343
[36m(train_xgb_ray_tune pid=1839879)[0m [182]	train-mlogloss:1.20900	validation-mlogloss:1.19894
[36m(train_xgb_ray_tune pid=1839881)[0m [177]	train-mlogloss:1.19203	validation-mlogloss:1.19763
[36m(train_xgb_ray_tune pid=1839856)[0m [191]	train-mlogloss:2.03990	validation-mlogloss:2.03354
[36m(train_xgb_ray_tune pid=1839869)[0m [128]	train-mlogloss:1.08257	validation-mlogloss:1.13822
[36m(train_xgb_ray_tune pid=1839893)[0m [111]	train-mlogloss:1.15676	validation-mlogloss:1.19888
[36m(train_xgb_ray_tune pid=1839849)[0m [116]	train-mlogloss:1.52012	validation-mlogloss:1.54456
[36m(train_xgb_ray_tune pid=1839846)[0m [154]	train-mlogloss:1.34628	validation-mlogloss:1.35247
[36m(train_xgb_ray_tune pid=1839845)[0m [232]	train-mlogloss:1.00998	validation-mlogloss:1.08148
[36m(train_xgb_ray_tune pid=1839877)[0m [168]	train-mlogloss:1.34066	validation-mlogloss:1.32396
[36m(train_xgb_ray_tune pid=1839878)[0m [207]	train-mlogloss:1.06061	validation-mlogloss:1.10530
[36m(train_xgb_ray_tune pid=1839848)[0m [180]	train-mlogloss:1.19478	validation-mlogloss:1.19208
[36m(train_xgb_ray_tune pid=1839879)[0m [183]	train-mlogloss:1.20768	validation-mlogloss:1.19774
[36m(train_xgb_ray_tune pid=1839881)[0m [178]	train-mlogloss:1.19028	validation-mlogloss:1.19612
[36m(train_xgb_ray_tune pid=1839894)[0m [116]	train-mlogloss:1.19139	validation-mlogloss:1.22952
[36m(train_xgb_ray_tune pid=1839880)[0m [154]	train-mlogloss:1.76114	validation-mlogloss:1.76800
[36m(train_xgb_ray_tune pid=1839847)[0m [117]	train-mlogloss:1.18178	validation-mlogloss:1.21504
[36m(train_xgb_ray_tune pid=1839870)[0m [170]	train-mlogloss:1.45370	validation-mlogloss:1.42899
[36m(train_xgb_ray_tune pid=1839862)[0m [129]	train-mlogloss:1.09898	validation-mlogloss:1.12897
[36m(train_xgb_ray_tune pid=1839871)[0m [160]	train-mlogloss:1.07602	validation-mlogloss:1.11307
[36m(train_xgb_ray_tune pid=1839846)[0m [155]	train-mlogloss:1.34387	validation-mlogloss:1.35004
[36m(train_xgb_ray_tune pid=1839845)[0m [233]	train-mlogloss:1.00879	validation-mlogloss:1.08123
[36m(train_xgb_ray_tune pid=1839855)[0m [118]	train-mlogloss:1.06534	validation-mlogloss:1.13277
[36m(train_xgb_ray_tune pid=1839877)[0m [169]	train-mlogloss:1.33849	validation-mlogloss:1.32181
[36m(train_xgb_ray_tune pid=1839878)[0m [208]	train-mlogloss:1.05977	validation-mlogloss:1.10469
[36m(train_xgb_ray_tune pid=1839879)[0m [184]	train-mlogloss:1.20630	validation-mlogloss:1.19652
[36m(train_xgb_ray_tune pid=1839881)[0m [179]	train-mlogloss:1.18848	validation-mlogloss:1.19476
[36m(train_xgb_ray_tune pid=1839856)[0m [192]	train-mlogloss:2.03772	validation-mlogloss:2.03130
[36m(train_xgb_ray_tune pid=1839869)[0m [129]	train-mlogloss:1.08047	validation-mlogloss:1.13699
[36m(train_xgb_ray_tune pid=1839893)[0m [112]	train-mlogloss:1.15372	validation-mlogloss:1.19660
[36m(train_xgb_ray_tune pid=1839849)[0m [117]	train-mlogloss:1.51614	validation-mlogloss:1.54031
[36m(train_xgb_ray_tune pid=1839845)[0m [234]	train-mlogloss:1.00758	validation-mlogloss:1.08084
[36m(train_xgb_ray_tune pid=1839870)[0m [171]	train-mlogloss:1.45194	validation-mlogloss:1.42722
[36m(train_xgb_ray_tune pid=1839862)[0m [130]	train-mlogloss:1.09756	validation-mlogloss:1.12764
[36m(train_xgb_ray_tune pid=1839871)[0m [161]	train-mlogloss:1.07424	validation-mlogloss:1.11201
[36m(train_xgb_ray_tune pid=1839848)[0m [181]	train-mlogloss:1.19324	validation-mlogloss:1.19073
[36m(train_xgb_ray_tune pid=1839856)[0m [193]	train-mlogloss:2.03547	validation-mlogloss:2.02896
[36m(train_xgb_ray_tune pid=1839894)[0m [117]	train-mlogloss:1.18824	validation-mlogloss:1.22710
[36m(train_xgb_ray_tune pid=1839880)[0m [155]	train-mlogloss:1.75827	validation-mlogloss:1.76494
[36m(train_xgb_ray_tune pid=1839878)[0m [209]	train-mlogloss:1.05913	validation-mlogloss:1.10422
[36m(train_xgb_ray_tune pid=1839847)[0m [118]	train-mlogloss:1.17877	validation-mlogloss:1.21273
[36m(train_xgb_ray_tune pid=1839879)[0m [185]	train-mlogloss:1.20475	validation-mlogloss:1.19519
[36m(train_xgb_ray_tune pid=1839846)[0m [156]	train-mlogloss:1.34161	validation-mlogloss:1.34781
[36m(train_xgb_ray_tune pid=1839845)[0m [235]	train-mlogloss:1.00643	validation-mlogloss:1.08065
[36m(train_xgb_ray_tune pid=1839855)[0m [119]	train-mlogloss:1.06280	validation-mlogloss:1.13092
[36m(train_xgb_ray_tune pid=1839877)[0m [170]	train-mlogloss:1.33633	validation-mlogloss:1.31970
[36m(train_xgb_ray_tune pid=1839870)[0m [172]	train-mlogloss:1.45024	validation-mlogloss:1.42537
[36m(train_xgb_ray_tune pid=1839871)[0m [162]	train-mlogloss:1.07253	validation-mlogloss:1.11096
[36m(train_xgb_ray_tune pid=1839848)[0m [182]	train-mlogloss:1.19166	validation-mlogloss:1.18935
[36m(train_xgb_ray_tune pid=1839881)[0m [180]	train-mlogloss:1.18670	validation-mlogloss:1.19317
[36m(train_xgb_ray_tune pid=1839856)[0m [194]	train-mlogloss:2.03322	validation-mlogloss:2.02661
[36m(train_xgb_ray_tune pid=1839869)[0m [130]	train-mlogloss:1.07861	validation-mlogloss:1.13527
[36m(train_xgb_ray_tune pid=1839893)[0m [113]	train-mlogloss:1.15069	validation-mlogloss:1.19437
[36m(train_xgb_ray_tune pid=1839849)[0m [118]	train-mlogloss:1.51215	validation-mlogloss:1.53629

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:22. Total running time: 40s
[36m(train_xgb_ray_tune pid=1839868)[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_xgb_ray_tune pid=1839868)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=1839878)[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_xgb_ray_tune pid=1839878)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              36.624 │
│ time_total_s                  36.624 │
│ training_iteration                 1 │
│ validation_accuracy          0.54528 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:22. Total running time: 40s
[36m(train_xgb_ray_tune pid=1839845)[0m [236]	train-mlogloss:1.00517	validation-mlogloss:1.08019
[36m(train_xgb_ray_tune pid=1839877)[0m [171]	train-mlogloss:1.33445	validation-mlogloss:1.31786
[36m(train_xgb_ray_tune pid=1839847)[0m [119]	train-mlogloss:1.17589	validation-mlogloss:1.21060
[36m(train_xgb_ray_tune pid=1839862)[0m [131]	train-mlogloss:1.09581	validation-mlogloss:1.12647
[36m(train_xgb_ray_tune pid=1839871)[0m [163]	train-mlogloss:1.07088	validation-mlogloss:1.11030
[36m(train_xgb_ray_tune pid=1839848)[0m [183]	train-mlogloss:1.19016	validation-mlogloss:1.18802
[36m(train_xgb_ray_tune pid=1839879)[0m [186]	train-mlogloss:1.20339	validation-mlogloss:1.19396
[36m(train_xgb_ray_tune pid=1839881)[0m [181]	train-mlogloss:1.18480	validation-mlogloss:1.19162
[36m(train_xgb_ray_tune pid=1839894)[0m [118]	train-mlogloss:1.18496	validation-mlogloss:1.22460
[36m(train_xgb_ray_tune pid=1839880)[0m [156]	train-mlogloss:1.75530	validation-mlogloss:1.76185
[36m(train_xgb_ray_tune pid=1839846)[0m [157]	train-mlogloss:1.33924	validation-mlogloss:1.34540
[36m(train_xgb_ray_tune pid=1839845)[0m [237]	train-mlogloss:1.00412	validation-mlogloss:1.07977
[36m(train_xgb_ray_tune pid=1839855)[0m [120]	train-mlogloss:1.06044	validation-mlogloss:1.12969
[36m(train_xgb_ray_tune pid=1839877)[0m [172]	train-mlogloss:1.33243	validation-mlogloss:1.31578
[36m(train_xgb_ray_tune pid=1839870)[0m [173]	train-mlogloss:1.44848	validation-mlogloss:1.42343
[36m(train_xgb_ray_tune pid=1839871)[0m [164]	train-mlogloss:1.06921	validation-mlogloss:1.10912
[36m(train_xgb_ray_tune pid=1839848)[0m [184]	train-mlogloss:1.18866	validation-mlogloss:1.18684
[36m(train_xgb_ray_tune pid=1839879)[0m [187]	train-mlogloss:1.20191	validation-mlogloss:1.19271
[36m(train_xgb_ray_tune pid=1839881)[0m [182]	train-mlogloss:1.18310	validation-mlogloss:1.19017
[36m(train_xgb_ray_tune pid=1839869)[0m [131]	train-mlogloss:1.07697	validation-mlogloss:1.13399
[36m(train_xgb_ray_tune pid=1839880)[0m [157]	train-mlogloss:1.75256	validation-mlogloss:1.75900
[36m(train_xgb_ray_tune pid=1839849)[0m [119]	train-mlogloss:1.50815	validation-mlogloss:1.53229
[36m(train_xgb_ray_tune pid=1839846)[0m [158]	train-mlogloss:1.33700	validation-mlogloss:1.34309
[36m(train_xgb_ray_tune pid=1839845)[0m [238]	train-mlogloss:1.00301	validation-mlogloss:1.07925
[36m(train_xgb_ray_tune pid=1839877)[0m [173]	train-mlogloss:1.33042	validation-mlogloss:1.31371
[36m(train_xgb_ray_tune pid=1839847)[0m [120]	train-mlogloss:1.17293	validation-mlogloss:1.20851
[36m(train_xgb_ray_tune pid=1839870)[0m [174]	train-mlogloss:1.44659	validation-mlogloss:1.42168
[36m(train_xgb_ray_tune pid=1839848)[0m [185]	train-mlogloss:1.18708	validation-mlogloss:1.18537
[36m(train_xgb_ray_tune pid=1839879)[0m [188]	train-mlogloss:1.20042	validation-mlogloss:1.19141
[36m(train_xgb_ray_tune pid=1839869)[0m [132]	train-mlogloss:1.07536	validation-mlogloss:1.13249
[36m(train_xgb_ray_tune pid=1839893)[0m [114]	train-mlogloss:1.14772	validation-mlogloss:1.19199
[36m(train_xgb_ray_tune pid=1839894)[0m [119]	train-mlogloss:1.18191	validation-mlogloss:1.22203
[36m(train_xgb_ray_tune pid=1839845)[0m [239]	train-mlogloss:1.00185	validation-mlogloss:1.07886
[36m(train_xgb_ray_tune pid=1839855)[0m [121]	train-mlogloss:1.05800	validation-mlogloss:1.12781
[36m(train_xgb_ray_tune pid=1839881)[0m [183]	train-mlogloss:1.18152	validation-mlogloss:1.18885
[36m(train_xgb_ray_tune pid=1839846)[0m [159]	train-mlogloss:1.33479	validation-mlogloss:1.34090
[36m(train_xgb_ray_tune pid=1839845)[0m [240]	train-mlogloss:1.00076	validation-mlogloss:1.07845
[36m(train_xgb_ray_tune pid=1839877)[0m [174]	train-mlogloss:1.32837	validation-mlogloss:1.31181
[36m(train_xgb_ray_tune pid=1839847)[0m [121]	train-mlogloss:1.17003	validation-mlogloss:1.20607
[36m(train_xgb_ray_tune pid=1839870)[0m [175]	train-mlogloss:1.44486	validation-mlogloss:1.41994
[36m(train_xgb_ray_tune pid=1839862)[0m [132]	train-mlogloss:1.09441	validation-mlogloss:1.12534
[36m(train_xgb_ray_tune pid=1839871)[0m [165]	train-mlogloss:1.06735	validation-mlogloss:1.10835
[36m(train_xgb_ray_tune pid=1839848)[0m [186]	train-mlogloss:1.18559	validation-mlogloss:1.18424
[36m(train_xgb_ray_tune pid=1839879)[0m [189]	train-mlogloss:1.19903	validation-mlogloss:1.19018
[36m(train_xgb_ray_tune pid=1839880)[0m [158]	train-mlogloss:1.74971	validation-mlogloss:1.75597
[36m(train_xgb_ray_tune pid=1839849)[0m [120]	train-mlogloss:1.50447	validation-mlogloss:1.52861
[36m(train_xgb_ray_tune pid=1839845)[0m [241]	train-mlogloss:0.99954	validation-mlogloss:1.07816
[36m(train_xgb_ray_tune pid=1839855)[0m [122]	train-mlogloss:1.05542	validation-mlogloss:1.12605
[36m(train_xgb_ray_tune pid=1839877)[0m [175]	train-mlogloss:1.32631	validation-mlogloss:1.30967
[36m(train_xgb_ray_tune pid=1839862)[0m [133]	train-mlogloss:1.09315	validation-mlogloss:1.12462
[36m(train_xgb_ray_tune pid=1839871)[0m [166]	train-mlogloss:1.06574	validation-mlogloss:1.10776
[36m(train_xgb_ray_tune pid=1839881)[0m [184]	train-mlogloss:1.17976	validation-mlogloss:1.18748
[36m(train_xgb_ray_tune pid=1839869)[0m [133]	train-mlogloss:1.07361	validation-mlogloss:1.13106
[36m(train_xgb_ray_tune pid=1839893)[0m [115]	train-mlogloss:1.14461	validation-mlogloss:1.18953
[36m(train_xgb_ray_tune pid=1839894)[0m [120]	train-mlogloss:1.17879	validation-mlogloss:1.21928
[36m(train_xgb_ray_tune pid=1839879)[0m [190]	train-mlogloss:1.19767	validation-mlogloss:1.18888
[36m(train_xgb_ray_tune pid=1839880)[0m [159]	train-mlogloss:1.74685	validation-mlogloss:1.75292
[36m(train_xgb_ray_tune pid=1839849)[0m [121]	train-mlogloss:1.50067	validation-mlogloss:1.52471
[36m(train_xgb_ray_tune pid=1839846)[0m [160]	train-mlogloss:1.33242	validation-mlogloss:1.33854
[36m(train_xgb_ray_tune pid=1839845)[0m [242]	train-mlogloss:0.99841	validation-mlogloss:1.07771
[36m(train_xgb_ray_tune pid=1839855)[0m [123]	train-mlogloss:1.05327	validation-mlogloss:1.12453
[36m(train_xgb_ray_tune pid=1839877)[0m [176]	train-mlogloss:1.32431	validation-mlogloss:1.30768
[36m(train_xgb_ray_tune pid=1839847)[0m [122]	train-mlogloss:1.16724	validation-mlogloss:1.20374
[36m(train_xgb_ray_tune pid=1839870)[0m [176]	train-mlogloss:1.44299	validation-mlogloss:1.41788
[36m(train_xgb_ray_tune pid=1839871)[0m [167]	train-mlogloss:1.06411	validation-mlogloss:1.10695
[36m(train_xgb_ray_tune pid=1839848)[0m [187]	train-mlogloss:1.18397	validation-mlogloss:1.18263

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:22. Total running time: 41s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             37.5941 │
│ time_total_s                 37.5941 │
│ training_iteration                 1 │
│ validation_accuracy          0.61633 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=1839869)[0m [134]	train-mlogloss:1.07163	validation-mlogloss:1.12984
[36m(train_xgb_ray_tune pid=1839893)[0m [116]	train-mlogloss:1.14158	validation-mlogloss:1.18716
[36m(train_xgb_ray_tune pid=1839879)[0m [191]	train-mlogloss:1.19625	validation-mlogloss:1.18789
[36m(train_xgb_ray_tune pid=1839894)[0m [121]	train-mlogloss:1.17574	validation-mlogloss:1.21697

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:22. Total running time: 41s
[36m(train_xgb_ray_tune pid=1839862)[0m [134]	train-mlogloss:1.09151	validation-mlogloss:1.12353
[36m(train_xgb_ray_tune pid=1839848)[0m [188]	train-mlogloss:1.18248	validation-mlogloss:1.18170
[36m(train_xgb_ray_tune pid=1839846)[0m [161]	train-mlogloss:1.33014	validation-mlogloss:1.33622
[36m(train_xgb_ray_tune pid=1839845)[0m [243]	train-mlogloss:0.99726	validation-mlogloss:1.07739
[36m(train_xgb_ray_tune pid=1839877)[0m [177]	train-mlogloss:1.32249	validation-mlogloss:1.30594
[36m(train_xgb_ray_tune pid=1839847)[0m [123]	train-mlogloss:1.16445	validation-mlogloss:1.20143
[36m(train_xgb_ray_tune pid=1839870)[0m [177]	train-mlogloss:1.44133	validation-mlogloss:1.41610
[36m(train_xgb_ray_tune pid=1839871)[0m [168]	train-mlogloss:1.06247	validation-mlogloss:1.10616
[36m(train_xgb_ray_tune pid=1839869)[0m [135]	train-mlogloss:1.06975	validation-mlogloss:1.12846
[36m(train_xgb_ray_tune pid=1839880)[0m [160]	train-mlogloss:1.74401	validation-mlogloss:1.74998
[36m(train_xgb_ray_tune pid=1839849)[0m [122]	train-mlogloss:1.49702	validation-mlogloss:1.52107
[36m(train_xgb_ray_tune pid=1839879)[0m [192]	train-mlogloss:1.19493	validation-mlogloss:1.18662

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:23. Total running time: 41s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             38.1458 │
│ time_total_s                 38.1458 │
│ training_iteration                 1 │
│ validation_accuracy          0.50732 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:23. Total running time: 41s
[36m(train_xgb_ray_tune pid=1839845)[0m [244]	train-mlogloss:0.99635	validation-mlogloss:1.07684
[36m(train_xgb_ray_tune pid=1839855)[0m [124]	train-mlogloss:1.05060	validation-mlogloss:1.12260
[36m(train_xgb_ray_tune pid=1839877)[0m [178]	train-mlogloss:1.32050	validation-mlogloss:1.30394
[36m(train_xgb_ray_tune pid=1839870)[0m [178]	train-mlogloss:1.43960	validation-mlogloss:1.41433
[36m(train_xgb_ray_tune pid=1839862)[0m [135]	train-mlogloss:1.08962	validation-mlogloss:1.12217
[36m(train_xgb_ray_tune pid=1839871)[0m [169]	train-mlogloss:1.06096	validation-mlogloss:1.10564
[36m(train_xgb_ray_tune pid=1839848)[0m [189]	train-mlogloss:1.18096	validation-mlogloss:1.18048
[36m(train_xgb_ray_tune pid=1839881)[0m [185]	train-mlogloss:1.17798	validation-mlogloss:1.18627
[36m(train_xgb_ray_tune pid=1839893)[0m [117]	train-mlogloss:1.13864	validation-mlogloss:1.18478
[36m(train_xgb_ray_tune pid=1839894)[0m [122]	train-mlogloss:1.17266	validation-mlogloss:1.21437
[36m(train_xgb_ray_tune pid=1839880)[0m [161]	train-mlogloss:1.74112	validation-mlogloss:1.74699
[36m(train_xgb_ray_tune pid=1839845)[0m [245]	train-mlogloss:0.99518	validation-mlogloss:1.07628
[36m(train_xgb_ray_tune pid=1839846)[0m [162]	train-mlogloss:1.32816	validation-mlogloss:1.33432
[36m(train_xgb_ray_tune pid=1839869)[0m [136]	train-mlogloss:1.06789	validation-mlogloss:1.12685
[36m(train_xgb_ray_tune pid=1839845)[0m [246]	train-mlogloss:0.99393	validation-mlogloss:1.07564
[36m(train_xgb_ray_tune pid=1839855)[0m [125]	train-mlogloss:1.04849	validation-mlogloss:1.12138
[36m(train_xgb_ray_tune pid=1839847)[0m [124]	train-mlogloss:1.16163	validation-mlogloss:1.19961
[36m(train_xgb_ray_tune pid=1839870)[0m [179]	train-mlogloss:1.43794	validation-mlogloss:1.41261
[36m(train_xgb_ray_tune pid=1839862)[0m [136]	train-mlogloss:1.08850	validation-mlogloss:1.12119
[36m(train_xgb_ray_tune pid=1839871)[0m [170]	train-mlogloss:1.05948	validation-mlogloss:1.10472
[36m(train_xgb_ray_tune pid=1839848)[0m [190]	train-mlogloss:1.17944	validation-mlogloss:1.17902
[36m(train_xgb_ray_tune pid=1839879)[0m [193]	train-mlogloss:1.19354	validation-mlogloss:1.18552
[36m(train_xgb_ray_tune pid=1839869)[0m [137]	train-mlogloss:1.06619	validation-mlogloss:1.12541
[36m(train_xgb_ray_tune pid=1839894)[0m [123]	train-mlogloss:1.16951	validation-mlogloss:1.21180
[36m(train_xgb_ray_tune pid=1839880)[0m [162]	train-mlogloss:1.73823	validation-mlogloss:1.74394
[36m(train_xgb_ray_tune pid=1839849)[0m [123]	train-mlogloss:1.49320	validation-mlogloss:1.51712
[36m(train_xgb_ray_tune pid=1839846)[0m [163]	train-mlogloss:1.32602	validation-mlogloss:1.33225
[36m(train_xgb_ray_tune pid=1839845)[0m [247]	train-mlogloss:0.99294	validation-mlogloss:1.07525
[36m(train_xgb_ray_tune pid=1839877)[0m [179]	train-mlogloss:1.31863	validation-mlogloss:1.30207
[36m(train_xgb_ray_tune pid=1839870)[0m [180]	train-mlogloss:1.43628	validation-mlogloss:1.41092
[36m(train_xgb_ray_tune pid=1839862)[0m [137]	train-mlogloss:1.08726	validation-mlogloss:1.12009
[36m(train_xgb_ray_tune pid=1839871)[0m [171]	train-mlogloss:1.05801	validation-mlogloss:1.10362
[36m(train_xgb_ray_tune pid=1839871)[0m [172]	train-mlogloss:1.05655	validation-mlogloss:1.10312
[36m(train_xgb_ray_tune pid=1839848)[0m [191]	train-mlogloss:1.17804	validation-mlogloss:1.17792
[36m(train_xgb_ray_tune pid=1839879)[0m [194]	train-mlogloss:1.19205	validation-mlogloss:1.18425
[36m(train_xgb_ray_tune pid=1839881)[0m [186]	train-mlogloss:1.17625	validation-mlogloss:1.18457
[36m(train_xgb_ray_tune pid=1839869)[0m [138]	train-mlogloss:1.06484	validation-mlogloss:1.12429
[36m(train_xgb_ray_tune pid=1839893)[0m [118]	train-mlogloss:1.13563	validation-mlogloss:1.18264
[36m(train_xgb_ray_tune pid=1839849)[0m [124]	train-mlogloss:1.48939	validation-mlogloss:1.51330
[36m(train_xgb_ray_tune pid=1839845)[0m [248]	train-mlogloss:0.99182	validation-mlogloss:1.07489
[36m(train_xgb_ray_tune pid=1839845)[0m [249]	train-mlogloss:0.99075	validation-mlogloss:1.07457
[36m(train_xgb_ray_tune pid=1839855)[0m [126]	train-mlogloss:1.04617	validation-mlogloss:1.11959
[36m(train_xgb_ray_tune pid=1839877)[0m [180]	train-mlogloss:1.31673	validation-mlogloss:1.30021
[36m(train_xgb_ray_tune pid=1839847)[0m [125]	train-mlogloss:1.15890	validation-mlogloss:1.19740
[36m(train_xgb_ray_tune pid=1839870)[0m [181]	train-mlogloss:1.43475	validation-mlogloss:1.40932
[36m(train_xgb_ray_tune pid=1839848)[0m [192]	train-mlogloss:1.17654	validation-mlogloss:1.17639
[36m(train_xgb_ray_tune pid=1839879)[0m [195]	train-mlogloss:1.19079	validation-mlogloss:1.18312
[36m(train_xgb_ray_tune pid=1839881)[0m [187]	train-mlogloss:1.17451	validation-mlogloss:1.18334
[36m(train_xgb_ray_tune pid=1839893)[0m [119]	train-mlogloss:1.13283	validation-mlogloss:1.18058
[36m(train_xgb_ray_tune pid=1839894)[0m [124]	train-mlogloss:1.16654	validation-mlogloss:1.20923
[36m(train_xgb_ray_tune pid=1839880)[0m [163]	train-mlogloss:1.73543	validation-mlogloss:1.74107
[36m(train_xgb_ray_tune pid=1839871)[0m [173]	train-mlogloss:1.05494	validation-mlogloss:1.10214
[36m(train_xgb_ray_tune pid=1839845)[0m [250]	train-mlogloss:0.98984	validation-mlogloss:1.07399
[36m(train_xgb_ray_tune pid=1839855)[0m [127]	train-mlogloss:1.04412	validation-mlogloss:1.11771
[36m(train_xgb_ray_tune pid=1839877)[0m [181]	train-mlogloss:1.31482	validation-mlogloss:1.29838
[36m(train_xgb_ray_tune pid=1839862)[0m [138]	train-mlogloss:1.08599	validation-mlogloss:1.11891
[36m(train_xgb_ray_tune pid=1839879)[0m [196]	train-mlogloss:1.18943	validation-mlogloss:1.18190
[36m(train_xgb_ray_tune pid=1839881)[0m [188]	train-mlogloss:1.17267	validation-mlogloss:1.18165
[36m(train_xgb_ray_tune pid=1839869)[0m [139]	train-mlogloss:1.06316	validation-mlogloss:1.12297
[36m(train_xgb_ray_tune pid=1839880)[0m [164]	train-mlogloss:1.73272	validation-mlogloss:1.73825
[36m(train_xgb_ray_tune pid=1839849)[0m [125]	train-mlogloss:1.48576	validation-mlogloss:1.50987
[36m(train_xgb_ray_tune pid=1839871)[0m [174]	train-mlogloss:1.05337	validation-mlogloss:1.10129
[36m(train_xgb_ray_tune pid=1839848)[0m [193]	train-mlogloss:1.17512	validation-mlogloss:1.17530
[36m(train_xgb_ray_tune pid=1839846)[0m [164]	train-mlogloss:1.32393	validation-mlogloss:1.33011
[36m(train_xgb_ray_tune pid=1839845)[0m [251]	train-mlogloss:0.98877	validation-mlogloss:1.07360
[36m(train_xgb_ray_tune pid=1839855)[0m [128]	train-mlogloss:1.04133	validation-mlogloss:1.11597
[36m(train_xgb_ray_tune pid=1839877)[0m [182]	train-mlogloss:1.31291	validation-mlogloss:1.29667
[36m(train_xgb_ray_tune pid=1839847)[0m [126]	train-mlogloss:1.15630	validation-mlogloss:1.19534
[36m(train_xgb_ray_tune pid=1839870)[0m [182]	train-mlogloss:1.43309	validation-mlogloss:1.40763
[36m(train_xgb_ray_tune pid=1839862)[0m [139]	train-mlogloss:1.08431	validation-mlogloss:1.11764
[36m(train_xgb_ray_tune pid=1839871)[0m [175]	train-mlogloss:1.05171	validation-mlogloss:1.10034
[36m(train_xgb_ray_tune pid=1839879)[0m [197]	train-mlogloss:1.18806	validation-mlogloss:1.18066
[36m(train_xgb_ray_tune pid=1839881)[0m [189]	train-mlogloss:1.17101	validation-mlogloss:1.18028
[36m(train_xgb_ray_tune pid=1839869)[0m [140]	train-mlogloss:1.06150	validation-mlogloss:1.12169
[36m(train_xgb_ray_tune pid=1839893)[0m [120]	train-mlogloss:1.13010	validation-mlogloss:1.17875
[36m(train_xgb_ray_tune pid=1839894)[0m [125]	train-mlogloss:1.16368	validation-mlogloss:1.20692
[36m(train_xgb_ray_tune pid=1839849)[0m [126]	train-mlogloss:1.48226	validation-mlogloss:1.50642
[36m(train_xgb_ray_tune pid=1839845)[0m [252]	train-mlogloss:0.98765	validation-mlogloss:1.07295
[36m(train_xgb_ray_tune pid=1839870)[0m [183]	train-mlogloss:1.43152	validation-mlogloss:1.40605
[36m(train_xgb_ray_tune pid=1839871)[0m [176]	train-mlogloss:1.05034	validation-mlogloss:1.09956
[36m(train_xgb_ray_tune pid=1839848)[0m [194]	train-mlogloss:1.17369	validation-mlogloss:1.17432
[36m(train_xgb_ray_tune pid=1839881)[0m [190]	train-mlogloss:1.16938	validation-mlogloss:1.17876
[36m(train_xgb_ray_tune pid=1839893)[0m [121]	train-mlogloss:1.12727	validation-mlogloss:1.17670
[36m(train_xgb_ray_tune pid=1839880)[0m [165]	train-mlogloss:1.72990	validation-mlogloss:1.73535
[36m(train_xgb_ray_tune pid=1839849)[0m [127]	train-mlogloss:1.47861	validation-mlogloss:1.50269
[36m(train_xgb_ray_tune pid=1839855)[0m [129]	train-mlogloss:1.03901	validation-mlogloss:1.11479
[36m(train_xgb_ray_tune pid=1839862)[0m [140]	train-mlogloss:1.08284	validation-mlogloss:1.11662
[36m(train_xgb_ray_tune pid=1839879)[0m [198]	train-mlogloss:1.18668	validation-mlogloss:1.17949
[36m(train_xgb_ray_tune pid=1839869)[0m [141]	train-mlogloss:1.06008	validation-mlogloss:1.12062
[36m(train_xgb_ray_tune pid=1839894)[0m [126]	train-mlogloss:1.16074	validation-mlogloss:1.20454
[36m(train_xgb_ray_tune pid=1839845)[0m [253]	train-mlogloss:0.98666	validation-mlogloss:1.07267
[36m(train_xgb_ray_tune pid=1839847)[0m [127]	train-mlogloss:1.15375	validation-mlogloss:1.19338
[36m(train_xgb_ray_tune pid=1839877)[0m [183]	train-mlogloss:1.31122	validation-mlogloss:1.29481
[36m(train_xgb_ray_tune pid=1839845)[0m [254]	train-mlogloss:0.98561	validation-mlogloss:1.07240
[36m(train_xgb_ray_tune pid=1839870)[0m [184]	train-mlogloss:1.43000	validation-mlogloss:1.40455
[36m(train_xgb_ray_tune pid=1839871)[0m [177]	train-mlogloss:1.04886	validation-mlogloss:1.09896
[36m(train_xgb_ray_tune pid=1839848)[0m [195]	train-mlogloss:1.17231	validation-mlogloss:1.17324
[36m(train_xgb_ray_tune pid=1839881)[0m [191]	train-mlogloss:1.16773	validation-mlogloss:1.17749
[36m(train_xgb_ray_tune pid=1839869)[0m [142]	train-mlogloss:1.05864	validation-mlogloss:1.11960
[36m(train_xgb_ray_tune pid=1839893)[0m [122]	train-mlogloss:1.12466	validation-mlogloss:1.17465
[36m(train_xgb_ray_tune pid=1839880)[0m [166]	train-mlogloss:1.72716	validation-mlogloss:1.73244
[36m(train_xgb_ray_tune pid=1839846)[0m [165]	train-mlogloss:1.32184	validation-mlogloss:1.32814
[36m(train_xgb_ray_tune pid=1839855)[0m [130]	train-mlogloss:1.03684	validation-mlogloss:1.11290
[36m(train_xgb_ray_tune pid=1839846)[0m [166]	train-mlogloss:1.31973	validation-mlogloss:1.32616
[36m(train_xgb_ray_tune pid=1839845)[0m [255]	train-mlogloss:0.98471	validation-mlogloss:1.07206
[36m(train_xgb_ray_tune pid=1839877)[0m [184]	train-mlogloss:1.30934	validation-mlogloss:1.29292
[36m(train_xgb_ray_tune pid=1839847)[0m [128]	train-mlogloss:1.15109	validation-mlogloss:1.19141
[36m(train_xgb_ray_tune pid=1839870)[0m [185]	train-mlogloss:1.42845	validation-mlogloss:1.40293
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[36m(train_xgb_ray_tune pid=1839871)[0m [178]	train-mlogloss:1.04737	validation-mlogloss:1.09833
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[36m(train_xgb_ray_tune pid=1839846)[0m [220]	train-mlogloss:1.22983	validation-mlogloss:1.23944
[36m(train_xgb_ray_tune pid=1839845)[0m [362]	train-mlogloss:0.88915	validation-mlogloss:1.04533
[36m(train_xgb_ray_tune pid=1839845)[0m [363]	train-mlogloss:0.88834	validation-mlogloss:1.04516
[36m(train_xgb_ray_tune pid=1839877)[0m [254]	train-mlogloss:1.21745	validation-mlogloss:1.20604
[36m(train_xgb_ray_tune pid=1839870)[0m [250]	train-mlogloss:1.35108	validation-mlogloss:1.32664
[36m(train_xgb_ray_tune pid=1839862)[0m [216]	train-mlogloss:1.02893	validation-mlogloss:1.07900
[36m(train_xgb_ray_tune pid=1839862)[0m [217]	train-mlogloss:1.02849	validation-mlogloss:1.07864
[36m(train_xgb_ray_tune pid=1839881)[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_xgb_ray_tune pid=1839881)[0m   _log_deprecation_warning([32m [repeated 2x across cluster][0m
[36m(train_xgb_ray_tune pid=1839871)[0m [257]	train-mlogloss:0.94559	validation-mlogloss:1.05520
[36m(train_xgb_ray_tune pid=1839879)[0m [270]	train-mlogloss:1.11968	validation-mlogloss:1.12799
[36m(train_xgb_ray_tune pid=1839869)[0m [219]	train-mlogloss:1.00222	validation-mlogloss:1.08091
[36m(train_xgb_ray_tune pid=1839880)[0m [214]	train-mlogloss:1.61178	validation-mlogloss:1.61108
[36m(train_xgb_ray_tune pid=1839849)[0m [168]	train-mlogloss:1.35677	validation-mlogloss:1.37967
[36m(train_xgb_ray_tune pid=1839879)[0m [271]	train-mlogloss:1.11896	validation-mlogloss:1.12748
[36m(train_xgb_ray_tune pid=1839893)[0m [171]	train-mlogloss:1.01961	validation-mlogloss:1.10797
[36m(train_xgb_ray_tune pid=1839894)[0m [176]	train-mlogloss:1.04762	validation-mlogloss:1.12441
[36m(train_xgb_ray_tune pid=1839855)[0m [183]	train-mlogloss:0.94890	validation-mlogloss:1.06633
[36m(train_xgb_ray_tune pid=1839848)[0m [271]	train-mlogloss:1.08867	validation-mlogloss:1.11501
[36m(train_xgb_ray_tune pid=1839846)[0m [221]	train-mlogloss:1.22844	validation-mlogloss:1.23815
[36m(train_xgb_ray_tune pid=1839845)[0m [364]	train-mlogloss:0.88750	validation-mlogloss:1.04498
[36m(train_xgb_ray_tune pid=1839869)[0m [220]	train-mlogloss:1.00178	validation-mlogloss:1.08045

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:34. Total running time: 53s
╭─────────────────────────────────────╮
│ Trial trial_0195f result            │
├─────────────────────────────────────┤
│ checkpoint_dir_name                 │
│ time_this_iter_s             49.395 │
│ time_total_s                 49.395 │
│ training_iteration                1 │
│ validation_accuracy           0.593 │
╰─────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:34. Total running time: 53s
[36m(train_xgb_ray_tune pid=1839845)[0m [365]	train-mlogloss:0.88682	validation-mlogloss:1.04494
[36m(train_xgb_ray_tune pid=1839877)[0m [255]	train-mlogloss:1.21637	validation-mlogloss:1.20514
[36m(train_xgb_ray_tune pid=1839847)[0m [174]	train-mlogloss:1.05551	validation-mlogloss:1.12308
[36m(train_xgb_ray_tune pid=1839870)[0m [251]	train-mlogloss:1.35018	validation-mlogloss:1.32574
[36m(train_xgb_ray_tune pid=1839862)[0m [218]	train-mlogloss:1.02813	validation-mlogloss:1.07827
[36m(train_xgb_ray_tune pid=1839871)[0m [258]	train-mlogloss:0.94442	validation-mlogloss:1.05508
[36m(train_xgb_ray_tune pid=1839879)[0m [272]	train-mlogloss:1.11827	validation-mlogloss:1.12694
[36m(train_xgb_ray_tune pid=1839848)[0m [272]	train-mlogloss:1.08779	validation-mlogloss:1.11429
[36m(train_xgb_ray_tune pid=1839869)[0m [221]	train-mlogloss:1.00099	validation-mlogloss:1.08024
[36m(train_xgb_ray_tune pid=1839880)[0m [215]	train-mlogloss:1.60956	validation-mlogloss:1.60872
[36m(train_xgb_ray_tune pid=1839849)[0m [169]	train-mlogloss:1.35434	validation-mlogloss:1.37727
[36m(train_xgb_ray_tune pid=1839845)[0m [366]	train-mlogloss:0.88608	validation-mlogloss:1.04500
[36m(train_xgb_ray_tune pid=1839877)[0m [256]	train-mlogloss:1.21535	validation-mlogloss:1.20427
[36m(train_xgb_ray_tune pid=1839879)[0m [273]	train-mlogloss:1.11760	validation-mlogloss:1.12666
[36m(train_xgb_ray_tune pid=1839846)[0m [222]	train-mlogloss:1.22712	validation-mlogloss:1.23691
[36m(train_xgb_ray_tune pid=1839845)[0m [367]	train-mlogloss:0.88532	validation-mlogloss:1.04455
[36m(train_xgb_ray_tune pid=1839855)[0m [184]	train-mlogloss:0.94759	validation-mlogloss:1.06573
[36m(train_xgb_ray_tune pid=1839847)[0m [175]	train-mlogloss:1.05379	validation-mlogloss:1.12196
[36m(train_xgb_ray_tune pid=1839870)[0m [252]	train-mlogloss:1.34935	validation-mlogloss:1.32497
[36m(train_xgb_ray_tune pid=1839862)[0m [219]	train-mlogloss:1.02782	validation-mlogloss:1.07818
[36m(train_xgb_ray_tune pid=1839871)[0m [259]	train-mlogloss:0.94336	validation-mlogloss:1.05463
[36m(train_xgb_ray_tune pid=1839869)[0m [222]	train-mlogloss:1.00071	validation-mlogloss:1.08004
[36m(train_xgb_ray_tune pid=1839893)[0m [172]	train-mlogloss:1.01785	validation-mlogloss:1.10694
[36m(train_xgb_ray_tune pid=1839894)[0m [177]	train-mlogloss:1.04583	validation-mlogloss:1.12345
[36m(train_xgb_ray_tune pid=1839846)[0m [223]	train-mlogloss:1.22593	validation-mlogloss:1.23588
[36m(train_xgb_ray_tune pid=1839845)[0m [368]	train-mlogloss:0.88455	validation-mlogloss:1.04436
[36m(train_xgb_ray_tune pid=1839845)[0m [369]	train-mlogloss:0.88377	validation-mlogloss:1.04419
[36m(train_xgb_ray_tune pid=1839877)[0m [257]	train-mlogloss:1.21437	validation-mlogloss:1.20334
[36m(train_xgb_ray_tune pid=1839877)[0m [258]	train-mlogloss:1.21352	validation-mlogloss:1.20255
[36m(train_xgb_ray_tune pid=1839847)[0m [176]	train-mlogloss:1.05219	validation-mlogloss:1.12114
[36m(train_xgb_ray_tune pid=1839870)[0m [253]	train-mlogloss:1.34854	validation-mlogloss:1.32417
[36m(train_xgb_ray_tune pid=1839862)[0m [220]	train-mlogloss:1.02761	validation-mlogloss:1.07808
[36m(train_xgb_ray_tune pid=1839871)[0m [260]	train-mlogloss:0.94239	validation-mlogloss:1.05428
[36m(train_xgb_ray_tune pid=1839848)[0m [273]	train-mlogloss:1.08696	validation-mlogloss:1.11352
[36m(train_xgb_ray_tune pid=1839879)[0m [274]	train-mlogloss:1.11691	validation-mlogloss:1.12600
[36m(train_xgb_ray_tune pid=1839879)[0m [275]	train-mlogloss:1.11612	validation-mlogloss:1.12549
[36m(train_xgb_ray_tune pid=1839869)[0m [223]	train-mlogloss:1.00027	validation-mlogloss:1.07974
[36m(train_xgb_ray_tune pid=1839893)[0m [173]	train-mlogloss:1.01622	validation-mlogloss:1.10600
[36m(train_xgb_ray_tune pid=1839894)[0m [178]	train-mlogloss:1.04410	validation-mlogloss:1.12239
[36m(train_xgb_ray_tune pid=1839880)[0m [216]	train-mlogloss:1.60752	validation-mlogloss:1.60651
[36m(train_xgb_ray_tune pid=1839855)[0m [185]	train-mlogloss:0.94670	validation-mlogloss:1.06514
[36m(train_xgb_ray_tune pid=1839849)[0m [170]	train-mlogloss:1.35196	validation-mlogloss:1.37496
[36m(train_xgb_ray_tune pid=1839846)[0m [224]	train-mlogloss:1.22473	validation-mlogloss:1.23463
[36m(train_xgb_ray_tune pid=1839845)[0m [370]	train-mlogloss:0.88301	validation-mlogloss:1.04407
[36m(train_xgb_ray_tune pid=1839847)[0m [177]	train-mlogloss:1.05063	validation-mlogloss:1.12003
[36m(train_xgb_ray_tune pid=1839870)[0m [254]	train-mlogloss:1.34767	validation-mlogloss:1.32335
[36m(train_xgb_ray_tune pid=1839862)[0m [221]	train-mlogloss:1.02709	validation-mlogloss:1.07780
[36m(train_xgb_ray_tune pid=1839871)[0m [261]	train-mlogloss:0.94142	validation-mlogloss:1.05401
[36m(train_xgb_ray_tune pid=1839848)[0m [274]	train-mlogloss:1.08620	validation-mlogloss:1.11315
[36m(train_xgb_ray_tune pid=1839848)[0m [275]	train-mlogloss:1.08531	validation-mlogloss:1.11271
[36m(train_xgb_ray_tune pid=1839893)[0m [174]	train-mlogloss:1.01454	validation-mlogloss:1.10503
[36m(train_xgb_ray_tune pid=1839880)[0m [217]	train-mlogloss:1.60552	validation-mlogloss:1.60449
[36m(train_xgb_ray_tune pid=1839877)[0m [259]	train-mlogloss:1.21273	validation-mlogloss:1.20192
[36m(train_xgb_ray_tune pid=1839879)[0m [276]	train-mlogloss:1.11531	validation-mlogloss:1.12482
[36m(train_xgb_ray_tune pid=1839880)[0m [218]	train-mlogloss:1.60341	validation-mlogloss:1.60222
[36m(train_xgb_ray_tune pid=1839845)[0m [371]	train-mlogloss:0.88224	validation-mlogloss:1.04383
[36m(train_xgb_ray_tune pid=1839855)[0m [186]	train-mlogloss:0.94557	validation-mlogloss:1.06453
[36m(train_xgb_ray_tune pid=1839870)[0m [255]	train-mlogloss:1.34696	validation-mlogloss:1.32267
[36m(train_xgb_ray_tune pid=1839862)[0m [222]	train-mlogloss:1.02665	validation-mlogloss:1.07744
[36m(train_xgb_ray_tune pid=1839871)[0m [262]	train-mlogloss:0.94040	validation-mlogloss:1.05381
[36m(train_xgb_ray_tune pid=1839848)[0m [276]	train-mlogloss:1.08456	validation-mlogloss:1.11217
[36m(train_xgb_ray_tune pid=1839894)[0m [179]	train-mlogloss:1.04229	validation-mlogloss:1.12136
[36m(train_xgb_ray_tune pid=1839849)[0m [171]	train-mlogloss:1.34962	validation-mlogloss:1.37251
[36m(train_xgb_ray_tune pid=1839879)[0m [277]	train-mlogloss:1.11440	validation-mlogloss:1.12418
[36m(train_xgb_ray_tune pid=1839869)[0m [224]	train-mlogloss:0.99993	validation-mlogloss:1.07945
[36m(train_xgb_ray_tune pid=1839847)[0m [178]	train-mlogloss:1.04899	validation-mlogloss:1.11875
[36m(train_xgb_ray_tune pid=1839846)[0m [225]	train-mlogloss:1.22345	validation-mlogloss:1.23331
[36m(train_xgb_ray_tune pid=1839845)[0m [372]	train-mlogloss:0.88153	validation-mlogloss:1.04364
[36m(train_xgb_ray_tune pid=1839845)[0m [373]	train-mlogloss:0.88079	validation-mlogloss:1.04354
[36m(train_xgb_ray_tune pid=1839877)[0m [260]	train-mlogloss:1.21192	validation-mlogloss:1.20133
[36m(train_xgb_ray_tune pid=1839870)[0m [256]	train-mlogloss:1.34617	validation-mlogloss:1.32193
[36m(train_xgb_ray_tune pid=1839862)[0m [223]	train-mlogloss:1.02633	validation-mlogloss:1.07746
[36m(train_xgb_ray_tune pid=1839862)[0m [224]	train-mlogloss:1.02607	validation-mlogloss:1.07733
[36m(train_xgb_ray_tune pid=1839871)[0m [263]	train-mlogloss:0.93941	validation-mlogloss:1.05357
[36m(train_xgb_ray_tune pid=1839848)[0m [277]	train-mlogloss:1.08363	validation-mlogloss:1.11180
[36m(train_xgb_ray_tune pid=1839848)[0m [278]	train-mlogloss:1.08291	validation-mlogloss:1.11139
[36m(train_xgb_ray_tune pid=1839879)[0m [278]	train-mlogloss:1.11374	validation-mlogloss:1.12365
[36m(train_xgb_ray_tune pid=1839893)[0m [175]	train-mlogloss:1.01284	validation-mlogloss:1.10385
[36m(train_xgb_ray_tune pid=1839894)[0m [180]	train-mlogloss:1.04056	validation-mlogloss:1.12024
[36m(train_xgb_ray_tune pid=1839880)[0m [219]	train-mlogloss:1.60135	validation-mlogloss:1.60001
[36m(train_xgb_ray_tune pid=1839855)[0m [187]	train-mlogloss:0.94452	validation-mlogloss:1.06404
[36m(train_xgb_ray_tune pid=1839870)[0m [257]	train-mlogloss:1.34530	validation-mlogloss:1.32111
[36m(train_xgb_ray_tune pid=1839846)[0m [226]	train-mlogloss:1.22224	validation-mlogloss:1.23218
[36m(train_xgb_ray_tune pid=1839845)[0m [374]	train-mlogloss:0.88004	validation-mlogloss:1.04336
[36m(train_xgb_ray_tune pid=1839845)[0m [375]	train-mlogloss:0.87933	validation-mlogloss:1.04317
[36m(train_xgb_ray_tune pid=1839877)[0m [261]	train-mlogloss:1.21098	validation-mlogloss:1.20030
[36m(train_xgb_ray_tune pid=1839870)[0m [258]	train-mlogloss:1.34450	validation-mlogloss:1.32029
[36m(train_xgb_ray_tune pid=1839862)[0m [225]	train-mlogloss:1.02580	validation-mlogloss:1.07720
[36m(train_xgb_ray_tune pid=1839871)[0m [264]	train-mlogloss:0.93826	validation-mlogloss:1.05313
[36m(train_xgb_ray_tune pid=1839848)[0m [279]	train-mlogloss:1.08218	validation-mlogloss:1.11084
[36m(train_xgb_ray_tune pid=1839879)[0m [279]	train-mlogloss:1.11325	validation-mlogloss:1.12331
[36m(train_xgb_ray_tune pid=1839869)[0m [225]	train-mlogloss:0.99969	validation-mlogloss:1.07927
[36m(train_xgb_ray_tune pid=1839880)[0m [220]	train-mlogloss:1.59926	validation-mlogloss:1.59780
[36m(train_xgb_ray_tune pid=1839849)[0m [172]	train-mlogloss:1.34729	validation-mlogloss:1.37006
[36m(train_xgb_ray_tune pid=1839869)[0m [226]	train-mlogloss:0.99958	validation-mlogloss:1.07909
[36m(train_xgb_ray_tune pid=1839893)[0m [176]	train-mlogloss:1.01133	validation-mlogloss:1.10334
[36m(train_xgb_ray_tune pid=1839845)[0m [376]	train-mlogloss:0.87864	validation-mlogloss:1.04302
[36m(train_xgb_ray_tune pid=1839845)[0m [377]	train-mlogloss:0.87799	validation-mlogloss:1.04282
[36m(train_xgb_ray_tune pid=1839855)[0m [188]	train-mlogloss:0.94349	validation-mlogloss:1.06354
[36m(train_xgb_ray_tune pid=1839877)[0m [262]	train-mlogloss:1.21010	validation-mlogloss:1.19949
[36m(train_xgb_ray_tune pid=1839877)[0m [263]	train-mlogloss:1.20922	validation-mlogloss:1.19868
[36m(train_xgb_ray_tune pid=1839847)[0m [179]	train-mlogloss:1.04730	validation-mlogloss:1.11770
[36m(train_xgb_ray_tune pid=1839870)[0m [259]	train-mlogloss:1.34374	validation-mlogloss:1.31965
[36m(train_xgb_ray_tune pid=1839862)[0m [226]	train-mlogloss:1.02554	validation-mlogloss:1.07727
[36m(train_xgb_ray_tune pid=1839871)[0m [265]	train-mlogloss:0.93721	validation-mlogloss:1.05285
[36m(train_xgb_ray_tune pid=1839848)[0m [280]	train-mlogloss:1.08146	validation-mlogloss:1.11053
[36m(train_xgb_ray_tune pid=1839879)[0m [280]	train-mlogloss:1.11258	validation-mlogloss:1.12279
[36m(train_xgb_ray_tune pid=1839869)[0m [227]	train-mlogloss:0.99908	validation-mlogloss:1.07902
[36m(train_xgb_ray_tune pid=1839894)[0m [181]	train-mlogloss:1.03883	validation-mlogloss:1.11950
[36m(train_xgb_ray_tune pid=1839846)[0m [227]	train-mlogloss:1.22109	validation-mlogloss:1.23101
[36m(train_xgb_ray_tune pid=1839848)[0m [281]	train-mlogloss:1.08060	validation-mlogloss:1.11014
[36m(train_xgb_ray_tune pid=1839845)[0m [378]	train-mlogloss:0.87724	validation-mlogloss:1.04239
[36m(train_xgb_ray_tune pid=1839855)[0m [189]	train-mlogloss:0.94256	validation-mlogloss:1.06307
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[36m(train_xgb_ray_tune pid=1839862)[0m [227]	train-mlogloss:1.02513	validation-mlogloss:1.07704
[36m(train_xgb_ray_tune pid=1839862)[0m [228]	train-mlogloss:1.02492	validation-mlogloss:1.07709
[36m(train_xgb_ray_tune pid=1839871)[0m [266]	train-mlogloss:0.93618	validation-mlogloss:1.05268
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[36m(train_xgb_ray_tune pid=1839879)[0m [281]	train-mlogloss:1.11185	validation-mlogloss:1.12235
[36m(train_xgb_ray_tune pid=1839869)[0m [228]	train-mlogloss:0.99891	validation-mlogloss:1.07896
[36m(train_xgb_ray_tune pid=1839893)[0m [177]	train-mlogloss:1.00969	validation-mlogloss:1.10227
[36m(train_xgb_ray_tune pid=1839880)[0m [221]	train-mlogloss:1.59718	validation-mlogloss:1.59564
[36m(train_xgb_ray_tune pid=1839849)[0m [173]	train-mlogloss:1.34489	validation-mlogloss:1.36771
[36m(train_xgb_ray_tune pid=1839846)[0m [228]	train-mlogloss:1.21978	validation-mlogloss:1.22991
[36m(train_xgb_ray_tune pid=1839845)[0m [379]	train-mlogloss:0.87650	validation-mlogloss:1.04235
[36m(train_xgb_ray_tune pid=1839877)[0m [265]	train-mlogloss:1.20773	validation-mlogloss:1.19748
[36m(train_xgb_ray_tune pid=1839870)[0m [261]	train-mlogloss:1.34204	validation-mlogloss:1.31790
[36m(train_xgb_ray_tune pid=1839862)[0m [229]	train-mlogloss:1.02447	validation-mlogloss:1.07692
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[36m(train_xgb_ray_tune pid=1839869)[0m [229]	train-mlogloss:0.99862	validation-mlogloss:1.07895
[36m(train_xgb_ray_tune pid=1839894)[0m [182]	train-mlogloss:1.03712	validation-mlogloss:1.11842
[36m(train_xgb_ray_tune pid=1839880)[0m [222]	train-mlogloss:1.59510	validation-mlogloss:1.59347
[36m(train_xgb_ray_tune pid=1839849)[0m [174]	train-mlogloss:1.34251	validation-mlogloss:1.36533
[36m(train_xgb_ray_tune pid=1839845)[0m [380]	train-mlogloss:0.87577	validation-mlogloss:1.04227
[36m(train_xgb_ray_tune pid=1839855)[0m [190]	train-mlogloss:0.94144	validation-mlogloss:1.06262
[36m(train_xgb_ray_tune pid=1839877)[0m [266]	train-mlogloss:1.20690	validation-mlogloss:1.19673
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[36m(train_xgb_ray_tune pid=1839879)[0m [313]	train-mlogloss:1.09433	validation-mlogloss:1.11047
[36m(train_xgb_ray_tune pid=1839869)[0m [271]	train-mlogloss:0.98663	validation-mlogloss:1.07295
[36m(train_xgb_ray_tune pid=1839846)[0m [260]	train-mlogloss:1.18515	validation-mlogloss:1.19941
[36m(train_xgb_ray_tune pid=1839845)[0m [425]	train-mlogloss:0.84459	validation-mlogloss:1.03660
[36m(train_xgb_ray_tune pid=1839877)[0m [302]	train-mlogloss:1.18264	validation-mlogloss:1.17565
[36m(train_xgb_ray_tune pid=1839847)[0m [203]	train-mlogloss:1.01357	validation-mlogloss:1.09704
[36m(train_xgb_ray_tune pid=1839870)[0m [297]	train-mlogloss:1.31995	validation-mlogloss:1.29705
[36m(train_xgb_ray_tune pid=1839862)[0m [283]	train-mlogloss:1.01060	validation-mlogloss:1.06964
[36m(train_xgb_ray_tune pid=1839871)[0m [305]	train-mlogloss:0.89899	validation-mlogloss:1.04376
[36m(train_xgb_ray_tune pid=1839848)[0m [330]	train-mlogloss:1.04869	validation-mlogloss:1.09242
[36m(train_xgb_ray_tune pid=1839893)[0m [202]	train-mlogloss:0.97246	validation-mlogloss:1.08473
[36m(train_xgb_ray_tune pid=1839880)[0m [252]	train-mlogloss:1.53747	validation-mlogloss:1.53247
[36m(train_xgb_ray_tune pid=1839849)[0m [201]	train-mlogloss:1.28629	validation-mlogloss:1.30971
[36m(train_xgb_ray_tune pid=1839879)[0m [314]	train-mlogloss:1.09386	validation-mlogloss:1.11012
[36m(train_xgb_ray_tune pid=1839869)[0m [272]	train-mlogloss:0.98626	validation-mlogloss:1.07279
[36m(train_xgb_ray_tune pid=1839855)[0m [220]	train-mlogloss:0.91653	validation-mlogloss:1.05295
[36m(train_xgb_ray_tune pid=1839894)[0m [204]	train-mlogloss:1.00188	validation-mlogloss:1.09823
[36m(train_xgb_ray_tune pid=1839846)[0m [261]	train-mlogloss:1.18416	validation-mlogloss:1.19855
[36m(train_xgb_ray_tune pid=1839845)[0m [426]	train-mlogloss:0.84387	validation-mlogloss:1.03637
[36m(train_xgb_ray_tune pid=1839877)[0m [303]	train-mlogloss:1.18218	validation-mlogloss:1.17531
[36m(train_xgb_ray_tune pid=1839847)[0m [204]	train-mlogloss:1.01226	validation-mlogloss:1.09638
[36m(train_xgb_ray_tune pid=1839870)[0m [298]	train-mlogloss:1.31957	validation-mlogloss:1.29678
[36m(train_xgb_ray_tune pid=1839862)[0m [284]	train-mlogloss:1.01054	validation-mlogloss:1.06966
[36m(train_xgb_ray_tune pid=1839871)[0m [306]	train-mlogloss:0.89795	validation-mlogloss:1.04321
[36m(train_xgb_ray_tune pid=1839848)[0m [331]	train-mlogloss:1.04821	validation-mlogloss:1.09229
[36m(train_xgb_ray_tune pid=1839879)[0m [315]	train-mlogloss:1.09342	validation-mlogloss:1.10976
[36m(train_xgb_ray_tune pid=1839869)[0m [273]	train-mlogloss:0.98602	validation-mlogloss:1.07269
[36m(train_xgb_ray_tune pid=1839893)[0m [203]	train-mlogloss:0.97088	validation-mlogloss:1.08427
[36m(train_xgb_ray_tune pid=1839880)[0m [253]	train-mlogloss:1.53565	validation-mlogloss:1.53053
[36m(train_xgb_ray_tune pid=1839846)[0m [262]	train-mlogloss:1.18322	validation-mlogloss:1.19788
[36m(train_xgb_ray_tune pid=1839877)[0m [304]	train-mlogloss:1.18167	validation-mlogloss:1.17497
[36m(train_xgb_ray_tune pid=1839845)[0m [427]	train-mlogloss:0.84325	validation-mlogloss:1.03622
[36m(train_xgb_ray_tune pid=1839855)[0m [221]	train-mlogloss:0.91584	validation-mlogloss:1.05253
[36m(train_xgb_ray_tune pid=1839877)[0m [305]	train-mlogloss:1.18121	validation-mlogloss:1.17456
[36m(train_xgb_ray_tune pid=1839870)[0m [299]	train-mlogloss:1.31918	validation-mlogloss:1.29648
[36m(train_xgb_ray_tune pid=1839862)[0m [285]	train-mlogloss:1.01041	validation-mlogloss:1.06956
[36m(train_xgb_ray_tune pid=1839879)[0m [316]	train-mlogloss:1.09313	validation-mlogloss:1.10955
[36m(train_xgb_ray_tune pid=1839869)[0m [274]	train-mlogloss:0.98591	validation-mlogloss:1.07273
[36m(train_xgb_ray_tune pid=1839893)[0m [204]	train-mlogloss:0.96947	validation-mlogloss:1.08375
[36m(train_xgb_ray_tune pid=1839894)[0m [205]	train-mlogloss:1.00039	validation-mlogloss:1.09754
[36m(train_xgb_ray_tune pid=1839880)[0m [254]	train-mlogloss:1.53401	validation-mlogloss:1.52878
[36m(train_xgb_ray_tune pid=1839849)[0m [202]	train-mlogloss:1.28445	validation-mlogloss:1.30777
[36m(train_xgb_ray_tune pid=1839846)[0m [263]	train-mlogloss:1.18223	validation-mlogloss:1.19706
[36m(train_xgb_ray_tune pid=1839871)[0m [307]	train-mlogloss:0.89707	validation-mlogloss:1.04293
[36m(train_xgb_ray_tune pid=1839848)[0m [332]	train-mlogloss:1.04765	validation-mlogloss:1.09213
[36m(train_xgb_ray_tune pid=1839845)[0m [428]	train-mlogloss:0.84263	validation-mlogloss:1.03612
[36m(train_xgb_ray_tune pid=1839862)[0m [286]	train-mlogloss:1.01037	validation-mlogloss:1.06953
[36m(train_xgb_ray_tune pid=1839846)[0m [264]	train-mlogloss:1.18145	validation-mlogloss:1.19649
[36m(train_xgb_ray_tune pid=1839845)[0m [429]	train-mlogloss:0.84196	validation-mlogloss:1.03604
[36m(train_xgb_ray_tune pid=1839855)[0m [222]	train-mlogloss:0.91542	validation-mlogloss:1.05232
[36m(train_xgb_ray_tune pid=1839877)[0m [306]	train-mlogloss:1.18058	validation-mlogloss:1.17399
[36m(train_xgb_ray_tune pid=1839877)[0m [307]	train-mlogloss:1.18011	validation-mlogloss:1.17358
[36m(train_xgb_ray_tune pid=1839847)[0m [205]	train-mlogloss:1.01100	validation-mlogloss:1.09559
[36m(train_xgb_ray_tune pid=1839870)[0m [300]	train-mlogloss:1.31872	validation-mlogloss:1.29602
[36m(train_xgb_ray_tune pid=1839862)[0m [287]	train-mlogloss:1.01032	validation-mlogloss:1.06950
[36m(train_xgb_ray_tune pid=1839871)[0m [308]	train-mlogloss:0.89618	validation-mlogloss:1.04270
[36m(train_xgb_ray_tune pid=1839848)[0m [333]	train-mlogloss:1.04722	validation-mlogloss:1.09190
[36m(train_xgb_ray_tune pid=1839879)[0m [317]	train-mlogloss:1.09275	validation-mlogloss:1.10916
[36m(train_xgb_ray_tune pid=1839869)[0m [275]	train-mlogloss:0.98574	validation-mlogloss:1.07280
[36m(train_xgb_ray_tune pid=1839893)[0m [205]	train-mlogloss:0.96826	validation-mlogloss:1.08305
[36m(train_xgb_ray_tune pid=1839880)[0m [255]	train-mlogloss:1.53220	validation-mlogloss:1.52679
[36m(train_xgb_ray_tune pid=1839846)[0m [265]	train-mlogloss:1.18044	validation-mlogloss:1.19556
[36m(train_xgb_ray_tune pid=1839845)[0m [430]	train-mlogloss:0.84134	validation-mlogloss:1.03599
[36m(train_xgb_ray_tune pid=1839855)[0m [223]	train-mlogloss:0.91483	validation-mlogloss:1.05217
[36m(train_xgb_ray_tune pid=1839877)[0m [308]	train-mlogloss:1.17978	validation-mlogloss:1.17330
[36m(train_xgb_ray_tune pid=1839870)[0m [301]	train-mlogloss:1.31816	validation-mlogloss:1.29549
[36m(train_xgb_ray_tune pid=1839862)[0m [288]	train-mlogloss:1.01022	validation-mlogloss:1.06939
[36m(train_xgb_ray_tune pid=1839848)[0m [334]	train-mlogloss:1.04672	validation-mlogloss:1.09164
[36m(train_xgb_ray_tune pid=1839848)[0m [335]	train-mlogloss:1.04624	validation-mlogloss:1.09142
[36m(train_xgb_ray_tune pid=1839879)[0m [318]	train-mlogloss:1.09226	validation-mlogloss:1.10893
[36m(train_xgb_ray_tune pid=1839869)[0m [276]	train-mlogloss:0.98557	validation-mlogloss:1.07268
[36m(train_xgb_ray_tune pid=1839893)[0m [206]	train-mlogloss:0.96675	validation-mlogloss:1.08213
[36m(train_xgb_ray_tune pid=1839894)[0m [206]	train-mlogloss:0.99892	validation-mlogloss:1.09666
[36m(train_xgb_ray_tune pid=1839849)[0m [203]	train-mlogloss:1.28260	validation-mlogloss:1.30597
[36m(train_xgb_ray_tune pid=1839871)[0m [309]	train-mlogloss:0.89533	validation-mlogloss:1.04278
[36m(train_xgb_ray_tune pid=1839846)[0m [266]	train-mlogloss:1.17958	validation-mlogloss:1.19483
[36m(train_xgb_ray_tune pid=1839845)[0m [431]	train-mlogloss:0.84068	validation-mlogloss:1.03591
[36m(train_xgb_ray_tune pid=1839855)[0m [224]	train-mlogloss:0.91414	validation-mlogloss:1.05168
[36m(train_xgb_ray_tune pid=1839877)[0m [309]	train-mlogloss:1.17932	validation-mlogloss:1.17286
[36m(train_xgb_ray_tune pid=1839847)[0m [206]	train-mlogloss:1.00962	validation-mlogloss:1.09472
[36m(train_xgb_ray_tune pid=1839870)[0m [302]	train-mlogloss:1.31773	validation-mlogloss:1.29509
[36m(train_xgb_ray_tune pid=1839862)[0m [289]	train-mlogloss:1.01012	validation-mlogloss:1.06926
[36m(train_xgb_ray_tune pid=1839848)[0m [336]	train-mlogloss:1.04575	validation-mlogloss:1.09126
[36m(train_xgb_ray_tune pid=1839879)[0m [319]	train-mlogloss:1.09168	validation-mlogloss:1.10860
[36m(train_xgb_ray_tune pid=1839869)[0m [277]	train-mlogloss:0.98530	validation-mlogloss:1.07248
[36m(train_xgb_ray_tune pid=1839893)[0m [207]	train-mlogloss:0.96545	validation-mlogloss:1.08163
[36m(train_xgb_ray_tune pid=1839880)[0m [256]	train-mlogloss:1.53044	validation-mlogloss:1.52502
[36m(train_xgb_ray_tune pid=1839845)[0m [432]	train-mlogloss:0.84006	validation-mlogloss:1.03582
[36m(train_xgb_ray_tune pid=1839877)[0m [310]	train-mlogloss:1.17883	validation-mlogloss:1.17250
[36m(train_xgb_ray_tune pid=1839847)[0m [207]	train-mlogloss:1.00819	validation-mlogloss:1.09392
[36m(train_xgb_ray_tune pid=1839870)[0m [303]	train-mlogloss:1.31723	validation-mlogloss:1.29460
[36m(train_xgb_ray_tune pid=1839862)[0m [290]	train-mlogloss:1.01001	validation-mlogloss:1.06919
[36m(train_xgb_ray_tune pid=1839871)[0m [310]	train-mlogloss:0.89445	validation-mlogloss:1.04247
[36m(train_xgb_ray_tune pid=1839848)[0m [337]	train-mlogloss:1.04521	validation-mlogloss:1.09091
[36m(train_xgb_ray_tune pid=1839848)[0m [338]	train-mlogloss:1.04475	validation-mlogloss:1.09063
[36m(train_xgb_ray_tune pid=1839879)[0m [320]	train-mlogloss:1.09130	validation-mlogloss:1.10824
[36m(train_xgb_ray_tune pid=1839869)[0m [278]	train-mlogloss:0.98501	validation-mlogloss:1.07235
[36m(train_xgb_ray_tune pid=1839869)[0m [279]	train-mlogloss:0.98481	validation-mlogloss:1.07220
[36m(train_xgb_ray_tune pid=1839893)[0m [208]	train-mlogloss:0.96414	validation-mlogloss:1.08110
[36m(train_xgb_ray_tune pid=1839894)[0m [207]	train-mlogloss:0.99754	validation-mlogloss:1.09598
[36m(train_xgb_ray_tune pid=1839880)[0m [257]	train-mlogloss:1.52872	validation-mlogloss:1.52318
[36m(train_xgb_ray_tune pid=1839849)[0m [204]	train-mlogloss:1.28075	validation-mlogloss:1.30405
[36m(train_xgb_ray_tune pid=1839846)[0m [267]	train-mlogloss:1.17865	validation-mlogloss:1.19405
[36m(train_xgb_ray_tune pid=1839855)[0m [225]	train-mlogloss:0.91347	validation-mlogloss:1.05154
[36m(train_xgb_ray_tune pid=1839871)[0m [311]	train-mlogloss:0.89365	validation-mlogloss:1.04229

Trial status: 15 RUNNING | 5 TERMINATED
Current time: 2025-10-25 19:43:42. Total running time: 1min 0s
Logical resource usage: 15.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         num_boost_round     max_depth     learning_rate     subsample     colsample_bytree     gamma     min_child_weight     reg_alpha     reg_lambda     random_state     iter     total time (s)     validation_accuracy │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_0195f    RUNNING                    490             3        0.0180686       0.386165             0.401018   1.91019                   26     28.4719          1.13302             2009                                                     │
│ trial_0195f    RUNNING                    216             3        0.0417666       0.538654             0.569217   3.2315                    21      1.13863       168.641               3282                                                     │
│ trial_0195f    RUNNING                    472             2        0.0362057       0.691935             0.596683   3.41711                   50      0.229961       27.507               6718                                                     │
│ trial_0195f    RUNNING                    267             3        0.0379462       0.678655             0.339382   8.27026                   46      0.155793       16.3947              6848                                                     │
│ trial_0195f    RUNNING                    406             2        0.0188112       0.475566             0.620105   8.00317                   41     17.1877          1.8251              5173                                                     │
│ trial_0195f    RUNNING                    450             2        0.0382595       0.603751             0.569897   3.0477                    54      0.103932        7.40936             1023                                                     │
│ trial_0195f    RUNNING                    315             3        0.0439828       0.390514             0.352631   6.90055                   11      1.81695        43.3285              2402                                                     │
│ trial_0195f    RUNNING                    331             2        0.0317944       0.313674             0.67536    4.41856                   53      5.32658        48.6357              7554                                                     │
│ trial_0195f    RUNNING                    414             2        0.0202599       0.695361             0.509306   1.56986                   58     93.6182          2.27817             1172                                                     │
│ trial_0195f    RUNNING                    489             2        0.0257862       0.674977             0.554238   4.86723                   23     15.2685         12.2752              3488                                                     │
│ trial_0195f    RUNNING                    502             3        0.0343541       0.330756             0.302921   8.9828                    17      0.114231        1.95791             1454                                                     │
│ trial_0195f    RUNNING                    512             3        0.0413879       0.513759             0.697578   2.60018                   55      0.548801       90.1764               512                                                     │
│ trial_0195f    RUNNING                    481             3        0.034166        0.68654              0.613953   2.28701                   49      0.108157      119.964               8159                                                     │
│ trial_0195f    RUNNING                    558             2        0.00744142      0.549635             0.625081   8.77248                   41     14.3318          1.34804             8995                                                     │
│ trial_0195f    RUNNING                    527             3        0.0135209       0.653459             0.597106   6.02627                   57     21.8788          1.27441             3227                                                     │
│ trial_0195f    TERMINATED                 195             2        0.00514406      0.580216             0.441027   5.8561                    50     57.2455         85.8182              8258        1            38.1458                0.507317 │
│ trial_0195f    TERMINATED                 111             3        0.0257285       0.332271             0.433404   9.4826                    26      0.114293       56.1492              7789        1            30.7858                0.595122 │
│ trial_0195f    TERMINATED                 253             2        0.0231462       0.46082              0.450991   5.86543                   12      0.55736         6.01178             7339        1            49.3951                0.593001 │
│ trial_0195f    TERMINATED                 210             3        0.0277019       0.451699             0.324143   8.75302                   48      3.3268          3.18417             6615        1            37.5941                0.616331 │
│ trial_0195f    TERMINATED                 127             2        0.0211436       0.673953             0.614406   5.99959                   45     55.8846          3.0865              7679        1            36.624                 0.545281 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=1839845)[0m [433]	train-mlogloss:0.83943	validation-mlogloss:1.03567
[36m(train_xgb_ray_tune pid=1839877)[0m [311]	train-mlogloss:1.17844	validation-mlogloss:1.17217
[36m(train_xgb_ray_tune pid=1839847)[0m [208]	train-mlogloss:1.00689	validation-mlogloss:1.09316
[36m(train_xgb_ray_tune pid=1839870)[0m [304]	train-mlogloss:1.31692	validation-mlogloss:1.29432
[36m(train_xgb_ray_tune pid=1839862)[0m [291]	train-mlogloss:1.00990	validation-mlogloss:1.06910
[36m(train_xgb_ray_tune pid=1839871)[0m [312]	train-mlogloss:0.89290	validation-mlogloss:1.04202
[36m(train_xgb_ray_tune pid=1839848)[0m [339]	train-mlogloss:1.04425	validation-mlogloss:1.09043
[36m(train_xgb_ray_tune pid=1839879)[0m [321]	train-mlogloss:1.09088	validation-mlogloss:1.10793
[36m(train_xgb_ray_tune pid=1839869)[0m [280]	train-mlogloss:0.98480	validation-mlogloss:1.07219
[36m(train_xgb_ray_tune pid=1839893)[0m [209]	train-mlogloss:0.96285	validation-mlogloss:1.08044
[36m(train_xgb_ray_tune pid=1839894)[0m [208]	train-mlogloss:0.99604	validation-mlogloss:1.09518
[36m(train_xgb_ray_tune pid=1839846)[0m [268]	train-mlogloss:1.17780	validation-mlogloss:1.19338
[36m(train_xgb_ray_tune pid=1839845)[0m [434]	train-mlogloss:0.83881	validation-mlogloss:1.03552
[36m(train_xgb_ray_tune pid=1839855)[0m [226]	train-mlogloss:0.91266	validation-mlogloss:1.05112
[36m(train_xgb_ray_tune pid=1839877)[0m [312]	train-mlogloss:1.17789	validation-mlogloss:1.17170
[36m(train_xgb_ray_tune pid=1839870)[0m [305]	train-mlogloss:1.31647	validation-mlogloss:1.29395
[36m(train_xgb_ray_tune pid=1839862)[0m [292]	train-mlogloss:1.00964	validation-mlogloss:1.06872
[36m(train_xgb_ray_tune pid=1839871)[0m [313]	train-mlogloss:0.89215	validation-mlogloss:1.04178
[36m(train_xgb_ray_tune pid=1839848)[0m [340]	train-mlogloss:1.04371	validation-mlogloss:1.09025
[36m(train_xgb_ray_tune pid=1839879)[0m [322]	train-mlogloss:1.09038	validation-mlogloss:1.10759
[36m(train_xgb_ray_tune pid=1839869)[0m [281]	train-mlogloss:0.98453	validation-mlogloss:1.07210
[36m(train_xgb_ray_tune pid=1839893)[0m [210]	train-mlogloss:0.96144	validation-mlogloss:1.07964
[36m(train_xgb_ray_tune pid=1839880)[0m [258]	train-mlogloss:1.52696	validation-mlogloss:1.52130
[36m(train_xgb_ray_tune pid=1839880)[0m [259]	train-mlogloss:1.52525	validation-mlogloss:1.51954
[36m(train_xgb_ray_tune pid=1839849)[0m [205]	train-mlogloss:1.27894	validation-mlogloss:1.30218
[36m(train_xgb_ray_tune pid=1839845)[0m [435]	train-mlogloss:0.83813	validation-mlogloss:1.03544
[36m(train_xgb_ray_tune pid=1839869)[0m [282]	train-mlogloss:0.98421	validation-mlogloss:1.07177
[36m(train_xgb_ray_tune pid=1839847)[0m [209]	train-mlogloss:1.00561	validation-mlogloss:1.09258
[36m(train_xgb_ray_tune pid=1839846)[0m [269]	train-mlogloss:1.17687	validation-mlogloss:1.19249
[36m(train_xgb_ray_tune pid=1839845)[0m [436]	train-mlogloss:0.83748	validation-mlogloss:1.03544
[36m(train_xgb_ray_tune pid=1839855)[0m [227]	train-mlogloss:0.91210	validation-mlogloss:1.05067
[36m(train_xgb_ray_tune pid=1839877)[0m [313]	train-mlogloss:1.17735	validation-mlogloss:1.17125
[36m(train_xgb_ray_tune pid=1839870)[0m [306]	train-mlogloss:1.31602	validation-mlogloss:1.29355
[36m(train_xgb_ray_tune pid=1839862)[0m [293]	train-mlogloss:1.00964	validation-mlogloss:1.06872
[36m(train_xgb_ray_tune pid=1839871)[0m [314]	train-mlogloss:0.89117	validation-mlogloss:1.04194
[36m(train_xgb_ray_tune pid=1839848)[0m [341]	train-mlogloss:1.04320	validation-mlogloss:1.08999
[36m(train_xgb_ray_tune pid=1839879)[0m [323]	train-mlogloss:1.08988	validation-mlogloss:1.10714
[36m(train_xgb_ray_tune pid=1839869)[0m [283]	train-mlogloss:0.98404	validation-mlogloss:1.07164
[36m(train_xgb_ray_tune pid=1839893)[0m [211]	train-mlogloss:0.96023	validation-mlogloss:1.07918
[36m(train_xgb_ray_tune pid=1839894)[0m [209]	train-mlogloss:0.99460	validation-mlogloss:1.09432
[36m(train_xgb_ray_tune pid=1839880)[0m [260]	train-mlogloss:1.52345	validation-mlogloss:1.51761
[36m(train_xgb_ray_tune pid=1839846)[0m [270]	train-mlogloss:1.17598	validation-mlogloss:1.19165
[36m(train_xgb_ray_tune pid=1839845)[0m [437]	train-mlogloss:0.83699	validation-mlogloss:1.03543
[36m(train_xgb_ray_tune pid=1839877)[0m [314]	train-mlogloss:1.17697	validation-mlogloss:1.17089
[36m(train_xgb_ray_tune pid=1839847)[0m [210]	train-mlogloss:1.00439	validation-mlogloss:1.09179
[36m(train_xgb_ray_tune pid=1839870)[0m [307]	train-mlogloss:1.31555	validation-mlogloss:1.29310
[36m(train_xgb_ray_tune pid=1839862)[0m [294]	train-mlogloss:1.00940	validation-mlogloss:1.06852
[36m(train_xgb_ray_tune pid=1839871)[0m [315]	train-mlogloss:0.89037	validation-mlogloss:1.04189
[36m(train_xgb_ray_tune pid=1839848)[0m [342]	train-mlogloss:1.04267	validation-mlogloss:1.08967
[36m(train_xgb_ray_tune pid=1839879)[0m [324]	train-mlogloss:1.08957	validation-mlogloss:1.10704
[36m(train_xgb_ray_tune pid=1839869)[0m [284]	train-mlogloss:0.98371	validation-mlogloss:1.07148
[36m(train_xgb_ray_tune pid=1839893)[0m [212]	train-mlogloss:0.95889	validation-mlogloss:1.07858
[36m(train_xgb_ray_tune pid=1839894)[0m [210]	train-mlogloss:0.99318	validation-mlogloss:1.09357
[36m(train_xgb_ray_tune pid=1839880)[0m [261]	train-mlogloss:1.52176	validation-mlogloss:1.51581
[36m(train_xgb_ray_tune pid=1839849)[0m [206]	train-mlogloss:1.27715	validation-mlogloss:1.30039
[36m(train_xgb_ray_tune pid=1839846)[0m [271]	train-mlogloss:1.17517	validation-mlogloss:1.19098
[36m(train_xgb_ray_tune pid=1839845)[0m [438]	train-mlogloss:0.83632	validation-mlogloss:1.03505
[36m(train_xgb_ray_tune pid=1839855)[0m [228]	train-mlogloss:0.91156	validation-mlogloss:1.05043
[36m(train_xgb_ray_tune pid=1839877)[0m [315]	train-mlogloss:1.17663	validation-mlogloss:1.17058
[36m(train_xgb_ray_tune pid=1839847)[0m [211]	train-mlogloss:1.00309	validation-mlogloss:1.09087
[36m(train_xgb_ray_tune pid=1839870)[0m [308]	train-mlogloss:1.31516	validation-mlogloss:1.29276
[36m(train_xgb_ray_tune pid=1839862)[0m [295]	train-mlogloss:1.00906	validation-mlogloss:1.06841
[36m(train_xgb_ray_tune pid=1839871)[0m [316]	train-mlogloss:0.88950	validation-mlogloss:1.04141
[36m(train_xgb_ray_tune pid=1839871)[0m [317]	train-mlogloss:0.88876	validation-mlogloss:1.04130
[36m(train_xgb_ray_tune pid=1839848)[0m [343]	train-mlogloss:1.04214	validation-mlogloss:1.08940
[36m(train_xgb_ray_tune pid=1839879)[0m [325]	train-mlogloss:1.08912	validation-mlogloss:1.10682
[36m(train_xgb_ray_tune pid=1839869)[0m [285]	train-mlogloss:0.98333	validation-mlogloss:1.07128
[36m(train_xgb_ray_tune pid=1839893)[0m [213]	train-mlogloss:0.95764	validation-mlogloss:1.07837
[36m(train_xgb_ray_tune pid=1839846)[0m [272]	train-mlogloss:1.17431	validation-mlogloss:1.19022
[36m(train_xgb_ray_tune pid=1839845)[0m [439]	train-mlogloss:0.83576	validation-mlogloss:1.03505
[36m(train_xgb_ray_tune pid=1839877)[0m [316]	train-mlogloss:1.17627	validation-mlogloss:1.17030
[36m(train_xgb_ray_tune pid=1839847)[0m [212]	train-mlogloss:1.00202	validation-mlogloss:1.09031
[36m(train_xgb_ray_tune pid=1839870)[0m [309]	train-mlogloss:1.31492	validation-mlogloss:1.29255
[36m(train_xgb_ray_tune pid=1839862)[0m [296]	train-mlogloss:1.00885	validation-mlogloss:1.06836
[36m(train_xgb_ray_tune pid=1839871)[0m [318]	train-mlogloss:0.88782	validation-mlogloss:1.04083
[36m(train_xgb_ray_tune pid=1839848)[0m [344]	train-mlogloss:1.04162	validation-mlogloss:1.08912
[36m(train_xgb_ray_tune pid=1839879)[0m [326]	train-mlogloss:1.08853	validation-mlogloss:1.10654
[36m(train_xgb_ray_tune pid=1839869)[0m [286]	train-mlogloss:0.98325	validation-mlogloss:1.07129
[36m(train_xgb_ray_tune pid=1839893)[0m [214]	train-mlogloss:0.95636	validation-mlogloss:1.07792
[36m(train_xgb_ray_tune pid=1839894)[0m [211]	train-mlogloss:0.99172	validation-mlogloss:1.09300
[36m(train_xgb_ray_tune pid=1839880)[0m [262]	train-mlogloss:1.52008	validation-mlogloss:1.51404
[36m(train_xgb_ray_tune pid=1839849)[0m [207]	train-mlogloss:1.27531	validation-mlogloss:1.29861
[36m(train_xgb_ray_tune pid=1839855)[0m [229]	train-mlogloss:0.91092	validation-mlogloss:1.05019
[36m(train_xgb_ray_tune pid=1839862)[0m [297]	train-mlogloss:1.00873	validation-mlogloss:1.06824
[36m(train_xgb_ray_tune pid=1839845)[0m [440]	train-mlogloss:0.83517	validation-mlogloss:1.03477
[36m(train_xgb_ray_tune pid=1839877)[0m [317]	train-mlogloss:1.17581	validation-mlogloss:1.16979
[36m(train_xgb_ray_tune pid=1839847)[0m [213]	train-mlogloss:1.00082	validation-mlogloss:1.08959
[36m(train_xgb_ray_tune pid=1839870)[0m [310]	train-mlogloss:1.31463	validation-mlogloss:1.29230
[36m(train_xgb_ray_tune pid=1839862)[0m [298]	train-mlogloss:1.00869	validation-mlogloss:1.06815
[36m(train_xgb_ray_tune pid=1839871)[0m [319]	train-mlogloss:0.88701	validation-mlogloss:1.04075
[36m(train_xgb_ray_tune pid=1839848)[0m [345]	train-mlogloss:1.04118	validation-mlogloss:1.08885
[36m(train_xgb_ray_tune pid=1839879)[0m [327]	train-mlogloss:1.08808	validation-mlogloss:1.10636
[36m(train_xgb_ray_tune pid=1839869)[0m [287]	train-mlogloss:0.98292	validation-mlogloss:1.07112
[36m(train_xgb_ray_tune pid=1839893)[0m [215]	train-mlogloss:0.95501	validation-mlogloss:1.07751
[36m(train_xgb_ray_tune pid=1839880)[0m [263]	train-mlogloss:1.51836	validation-mlogloss:1.51226
[36m(train_xgb_ray_tune pid=1839846)[0m [273]	train-mlogloss:1.17348	validation-mlogloss:1.18965
[36m(train_xgb_ray_tune pid=1839845)[0m [441]	train-mlogloss:0.83462	validation-mlogloss:1.03477
[36m(train_xgb_ray_tune pid=1839855)[0m [230]	train-mlogloss:0.91031	validation-mlogloss:1.04998
[36m(train_xgb_ray_tune pid=1839877)[0m [318]	train-mlogloss:1.17546	validation-mlogloss:1.16952
[36m(train_xgb_ray_tune pid=1839847)[0m [214]	train-mlogloss:0.99961	validation-mlogloss:1.08912
[36m(train_xgb_ray_tune pid=1839870)[0m [311]	train-mlogloss:1.31432	validation-mlogloss:1.29202
[36m(train_xgb_ray_tune pid=1839862)[0m [299]	train-mlogloss:1.00833	validation-mlogloss:1.06808
[36m(train_xgb_ray_tune pid=1839871)[0m [320]	train-mlogloss:0.88618	validation-mlogloss:1.04055
[36m(train_xgb_ray_tune pid=1839871)[0m [321]	train-mlogloss:0.88534	validation-mlogloss:1.04032
[36m(train_xgb_ray_tune pid=1839879)[0m [328]	train-mlogloss:1.08777	validation-mlogloss:1.10614
[36m(train_xgb_ray_tune pid=1839869)[0m [288]	train-mlogloss:0.98278	validation-mlogloss:1.07109
[36m(train_xgb_ray_tune pid=1839893)[0m [216]	train-mlogloss:0.95361	validation-mlogloss:1.07728
[36m(train_xgb_ray_tune pid=1839894)[0m [212]	train-mlogloss:0.99034	validation-mlogloss:1.09249
[36m(train_xgb_ray_tune pid=1839849)[0m [208]	train-mlogloss:1.27361	validation-mlogloss:1.29696
[36m(train_xgb_ray_tune pid=1839846)[0m [274]	train-mlogloss:1.17266	validation-mlogloss:1.18889
[36m(train_xgb_ray_tune pid=1839845)[0m [442]	train-mlogloss:0.83401	validation-mlogloss:1.03476
[36m(train_xgb_ray_tune pid=1839855)[0m [231]	train-mlogloss:0.90969	validation-mlogloss:1.04980
[36m(train_xgb_ray_tune pid=1839877)[0m [319]	train-mlogloss:1.17505	validation-mlogloss:1.16924
[36m(train_xgb_ray_tune pid=1839862)[0m [300]	train-mlogloss:1.00810	validation-mlogloss:1.06799
[36m(train_xgb_ray_tune pid=1839871)[0m [322]	train-mlogloss:0.88454	validation-mlogloss:1.03997
[36m(train_xgb_ray_tune pid=1839848)[0m [346]	train-mlogloss:1.04067	validation-mlogloss:1.08860
[36m(train_xgb_ray_tune pid=1839879)[0m [329]	train-mlogloss:1.08738	validation-mlogloss:1.10590
[36m(train_xgb_ray_tune pid=1839869)[0m [289]	train-mlogloss:0.98263	validation-mlogloss:1.07111
[36m(train_xgb_ray_tune pid=1839893)[0m [217]	train-mlogloss:0.95235	validation-mlogloss:1.07656
[36m(train_xgb_ray_tune pid=1839894)[0m [213]	train-mlogloss:0.98893	validation-mlogloss:1.09172
[36m(train_xgb_ray_tune pid=1839880)[0m [264]	train-mlogloss:1.51674	validation-mlogloss:1.51058
[36m(train_xgb_ray_tune pid=1839845)[0m [443]	train-mlogloss:0.83339	validation-mlogloss:1.03441
[36m(train_xgb_ray_tune pid=1839870)[0m [312]	train-mlogloss:1.31385	validation-mlogloss:1.29154
[36m(train_xgb_ray_tune pid=1839847)[0m [215]	train-mlogloss:0.99842	validation-mlogloss:1.08882
[36m(train_xgb_ray_tune pid=1839848)[0m [347]	train-mlogloss:1.04015	validation-mlogloss:1.08842
[36m(train_xgb_ray_tune pid=1839846)[0m [275]	train-mlogloss:1.17187	validation-mlogloss:1.18828
[36m(train_xgb_ray_tune pid=1839845)[0m [444]	train-mlogloss:0.83271	validation-mlogloss:1.03428
[36m(train_xgb_ray_tune pid=1839855)[0m [232]	train-mlogloss:0.90939	validation-mlogloss:1.04959
[36m(train_xgb_ray_tune pid=1839877)[0m [320]	train-mlogloss:1.17465	validation-mlogloss:1.16888
[36m(train_xgb_ray_tune pid=1839870)[0m [313]	train-mlogloss:1.31340	validation-mlogloss:1.29120
[36m(train_xgb_ray_tune pid=1839862)[0m [301]	train-mlogloss:1.00784	validation-mlogloss:1.06786
[36m(train_xgb_ray_tune pid=1839871)[0m [323]	train-mlogloss:0.88363	validation-mlogloss:1.03957
[36m(train_xgb_ray_tune pid=1839879)[0m [330]	train-mlogloss:1.08678	validation-mlogloss:1.10541
[36m(train_xgb_ray_tune pid=1839869)[0m [290]	train-mlogloss:0.98226	validation-mlogloss:1.07105
[36m(train_xgb_ray_tune pid=1839894)[0m [214]	train-mlogloss:0.98748	validation-mlogloss:1.09098
[36m(train_xgb_ray_tune pid=1839880)[0m [265]	train-mlogloss:1.51504	validation-mlogloss:1.50875
[36m(train_xgb_ray_tune pid=1839849)[0m [209]	train-mlogloss:1.27183	validation-mlogloss:1.29522
[36m(train_xgb_ray_tune pid=1839871)[0m [324]	train-mlogloss:0.88289	validation-mlogloss:1.03905
[36m(train_xgb_ray_tune pid=1839848)[0m [348]	train-mlogloss:1.03968	validation-mlogloss:1.08818
[36m(train_xgb_ray_tune pid=1839869)[0m [291]	train-mlogloss:0.98186	validation-mlogloss:1.07093
[36m(train_xgb_ray_tune pid=1839893)[0m [218]	train-mlogloss:0.95108	validation-mlogloss:1.07605
[36m(train_xgb_ray_tune pid=1839845)[0m [445]	train-mlogloss:0.83210	validation-mlogloss:1.03435
[36m(train_xgb_ray_tune pid=1839855)[0m [233]	train-mlogloss:0.90875	validation-mlogloss:1.04888
[36m(train_xgb_ray_tune pid=1839877)[0m [321]	train-mlogloss:1.17417	validation-mlogloss:1.16843
[36m(train_xgb_ray_tune pid=1839870)[0m [314]	train-mlogloss:1.31312	validation-mlogloss:1.29092
[36m(train_xgb_ray_tune pid=1839862)[0m [302]	train-mlogloss:1.00771	validation-mlogloss:1.06774
[36m(train_xgb_ray_tune pid=1839871)[0m [325]	train-mlogloss:0.88217	validation-mlogloss:1.03900
[36m(train_xgb_ray_tune pid=1839869)[0m [292]	train-mlogloss:0.98153	validation-mlogloss:1.07100
[36m(train_xgb_ray_tune pid=1839849)[0m [210]	train-mlogloss:1.27009	validation-mlogloss:1.29359
[36m(train_xgb_ray_tune pid=1839846)[0m [276]	train-mlogloss:1.17110	validation-mlogloss:1.18764
[36m(train_xgb_ray_tune pid=1839848)[0m [349]	train-mlogloss:1.03913	validation-mlogloss:1.08793
[36m(train_xgb_ray_tune pid=1839893)[0m [219]	train-mlogloss:0.94982	validation-mlogloss:1.07572
[36m(train_xgb_ray_tune pid=1839894)[0m [215]	train-mlogloss:0.98610	validation-mlogloss:1.09009
[36m(train_xgb_ray_tune pid=1839845)[0m [446]	train-mlogloss:0.83147	validation-mlogloss:1.03431
[36m(train_xgb_ray_tune pid=1839855)[0m [234]	train-mlogloss:0.90805	validation-mlogloss:1.04913
[36m(train_xgb_ray_tune pid=1839877)[0m [322]	train-mlogloss:1.17379	validation-mlogloss:1.16808
[36m(train_xgb_ray_tune pid=1839870)[0m [315]	train-mlogloss:1.31269	validation-mlogloss:1.29051
[36m(train_xgb_ray_tune pid=1839862)[0m [303]	train-mlogloss:1.00759	validation-mlogloss:1.06757
[36m(train_xgb_ray_tune pid=1839871)[0m [326]	train-mlogloss:0.88134	validation-mlogloss:1.03911
[36m(train_xgb_ray_tune pid=1839869)[0m [293]	train-mlogloss:0.98149	validation-mlogloss:1.07093
[36m(train_xgb_ray_tune pid=1839880)[0m [266]	train-mlogloss:1.51332	validation-mlogloss:1.50681
[36m(train_xgb_ray_tune pid=1839846)[0m [277]	train-mlogloss:1.17037	validation-mlogloss:1.18713
[36m(train_xgb_ray_tune pid=1839845)[0m [447]	train-mlogloss:0.83085	validation-mlogloss:1.03429
[36m(train_xgb_ray_tune pid=1839877)[0m [323]	train-mlogloss:1.17329	validation-mlogloss:1.16760
[36m(train_xgb_ray_tune pid=1839870)[0m [316]	train-mlogloss:1.31222	validation-mlogloss:1.28994
[36m(train_xgb_ray_tune pid=1839862)[0m [304]	train-mlogloss:1.00732	validation-mlogloss:1.06742
[36m(train_xgb_ray_tune pid=1839862)[0m [305]	train-mlogloss:1.00709	validation-mlogloss:1.06723
[36m(train_xgb_ray_tune pid=1839871)[0m [327]	train-mlogloss:0.88047	validation-mlogloss:1.03891
[36m(train_xgb_ray_tune pid=1839871)[0m [328]	train-mlogloss:0.87959	validation-mlogloss:1.03885
[36m(train_xgb_ray_tune pid=1839848)[0m [350]	train-mlogloss:1.03866	validation-mlogloss:1.08781
[36m(train_xgb_ray_tune pid=1839869)[0m [294]	train-mlogloss:0.98115	validation-mlogloss:1.07074
[36m(train_xgb_ray_tune pid=1839847)[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_xgb_ray_tune pid=1839847)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=1839893)[0m [220]	train-mlogloss:0.94863	validation-mlogloss:1.07514
[36m(train_xgb_ray_tune pid=1839894)[0m [216]	train-mlogloss:0.98463	validation-mlogloss:1.08917
[36m(train_xgb_ray_tune pid=1839880)[0m [267]	train-mlogloss:1.51161	validation-mlogloss:1.50494
[36m(train_xgb_ray_tune pid=1839849)[0m [211]	train-mlogloss:1.26836	validation-mlogloss:1.29196

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:43. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             58.6451 │
│ time_total_s                 58.6451 │
│ training_iteration                 1 │
│ validation_accuracy          0.61676 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:43. Total running time: 1min 2s
[36m(train_xgb_ray_tune pid=1839846)[0m [278]	train-mlogloss:1.16955	validation-mlogloss:1.18647
[36m(train_xgb_ray_tune pid=1839845)[0m [448]	train-mlogloss:0.83020	validation-mlogloss:1.03428
[36m(train_xgb_ray_tune pid=1839855)[0m [235]	train-mlogloss:0.90757	validation-mlogloss:1.04896
[36m(train_xgb_ray_tune pid=1839877)[0m [324]	train-mlogloss:1.17309	validation-mlogloss:1.16746
[36m(train_xgb_ray_tune pid=1839870)[0m [317]	train-mlogloss:1.31190	validation-mlogloss:1.28967
[36m(train_xgb_ray_tune pid=1839862)[0m [306]	train-mlogloss:1.00693	validation-mlogloss:1.06719
[36m(train_xgb_ray_tune pid=1839871)[0m [329]	train-mlogloss:0.87875	validation-mlogloss:1.03845
[36m(train_xgb_ray_tune pid=1839848)[0m [351]	train-mlogloss:1.03810	validation-mlogloss:1.08745
[36m(train_xgb_ray_tune pid=1839869)[0m [295]	train-mlogloss:0.98111	validation-mlogloss:1.07066
[36m(train_xgb_ray_tune pid=1839893)[0m [221]	train-mlogloss:0.94739	validation-mlogloss:1.07469
[36m(train_xgb_ray_tune pid=1839846)[0m [279]	train-mlogloss:1.16890	validation-mlogloss:1.18593
[36m(train_xgb_ray_tune pid=1839845)[0m [449]	train-mlogloss:0.82962	validation-mlogloss:1.03430
[36m(train_xgb_ray_tune pid=1839855)[0m [236]	train-mlogloss:0.90693	validation-mlogloss:1.04873
[36m(train_xgb_ray_tune pid=1839877)[0m [325]	train-mlogloss:1.17265	validation-mlogloss:1.16715
[36m(train_xgb_ray_tune pid=1839870)[0m [318]	train-mlogloss:1.31164	validation-mlogloss:1.28942
[36m(train_xgb_ray_tune pid=1839862)[0m [307]	train-mlogloss:1.00673	validation-mlogloss:1.06707
[36m(train_xgb_ray_tune pid=1839871)[0m [330]	train-mlogloss:0.87799	validation-mlogloss:1.03829
[36m(train_xgb_ray_tune pid=1839871)[0m [331]	train-mlogloss:0.87720	validation-mlogloss:1.03799
[36m(train_xgb_ray_tune pid=1839848)[0m [352]	train-mlogloss:1.03772	validation-mlogloss:1.08723
[36m(train_xgb_ray_tune pid=1839848)[0m [353]	train-mlogloss:1.03719	validation-mlogloss:1.08694
[36m(train_xgb_ray_tune pid=1839869)[0m [296]	train-mlogloss:0.98082	validation-mlogloss:1.07057
[36m(train_xgb_ray_tune pid=1839893)[0m [222]	train-mlogloss:0.94611	validation-mlogloss:1.07386
[36m(train_xgb_ray_tune pid=1839894)[0m [217]	train-mlogloss:0.98324	validation-mlogloss:1.08836
[36m(train_xgb_ray_tune pid=1839880)[0m [268]	train-mlogloss:1.50999	validation-mlogloss:1.50327
[36m(train_xgb_ray_tune pid=1839849)[0m [212]	train-mlogloss:1.26661	validation-mlogloss:1.29030
[36m(train_xgb_ray_tune pid=1839845)[0m [450]	train-mlogloss:0.82909	validation-mlogloss:1.03427
[36m(train_xgb_ray_tune pid=1839862)[0m [308]	train-mlogloss:1.00643	validation-mlogloss:1.06705
[36m(train_xgb_ray_tune pid=1839869)[0m [297]	train-mlogloss:0.98048	validation-mlogloss:1.07066
[36m(train_xgb_ray_tune pid=1839893)[0m [223]	train-mlogloss:0.94487	validation-mlogloss:1.07316
[36m(train_xgb_ray_tune pid=1839849)[0m [213]	train-mlogloss:1.26497	validation-mlogloss:1.28876
[36m(train_xgb_ray_tune pid=1839845)[0m [451]	train-mlogloss:0.82848	validation-mlogloss:1.03405
[36m(train_xgb_ray_tune pid=1839855)[0m [237]	train-mlogloss:0.90664	validation-mlogloss:1.04863
[36m(train_xgb_ray_tune pid=1839877)[0m [326]	train-mlogloss:1.17221	validation-mlogloss:1.16681
[36m(train_xgb_ray_tune pid=1839870)[0m [319]	train-mlogloss:1.31123	validation-mlogloss:1.28913
[36m(train_xgb_ray_tune pid=1839862)[0m [309]	train-mlogloss:1.00639	validation-mlogloss:1.06696
[36m(train_xgb_ray_tune pid=1839871)[0m [332]	train-mlogloss:0.87633	validation-mlogloss:1.03790
[36m(train_xgb_ray_tune pid=1839848)[0m [354]	train-mlogloss:1.03671	validation-mlogloss:1.08660
[36m(train_xgb_ray_tune pid=1839848)[0m [355]	train-mlogloss:1.03615	validation-mlogloss:1.08620
[36m(train_xgb_ray_tune pid=1839869)[0m [298]	train-mlogloss:0.98030	validation-mlogloss:1.07055
[36m(train_xgb_ray_tune pid=1839893)[0m [224]	train-mlogloss:0.94363	validation-mlogloss:1.07273
[36m(train_xgb_ray_tune pid=1839880)[0m [269]	train-mlogloss:1.50834	validation-mlogloss:1.50154
[36m(train_xgb_ray_tune pid=1839849)[0m [214]	train-mlogloss:1.26325	validation-mlogloss:1.28716
[36m(train_xgb_ray_tune pid=1839846)[0m [280]	train-mlogloss:1.16803	validation-mlogloss:1.18526
[36m(train_xgb_ray_tune pid=1839871)[0m [333]	train-mlogloss:0.87542	validation-mlogloss:1.03768
[36m(train_xgb_ray_tune pid=1839894)[0m [218]	train-mlogloss:0.98182	validation-mlogloss:1.08772
[36m(train_xgb_ray_tune pid=1839877)[0m [327]	train-mlogloss:1.17181	validation-mlogloss:1.16657
[36m(train_xgb_ray_tune pid=1839855)[0m [238]	train-mlogloss:0.90616	validation-mlogloss:1.04849
[36m(train_xgb_ray_tune pid=1839862)[0m [310]	train-mlogloss:1.00628	validation-mlogloss:1.06688
[36m(train_xgb_ray_tune pid=1839845)[0m [452]	train-mlogloss:0.82794	validation-mlogloss:1.03392
[36m(train_xgb_ray_tune pid=1839877)[0m [328]	train-mlogloss:1.17140	validation-mlogloss:1.16625
[36m(train_xgb_ray_tune pid=1839870)[0m [320]	train-mlogloss:1.31097	validation-mlogloss:1.28890
[36m(train_xgb_ray_tune pid=1839862)[0m [311]	train-mlogloss:1.00616	validation-mlogloss:1.06671
[36m(train_xgb_ray_tune pid=1839871)[0m [334]	train-mlogloss:0.87445	validation-mlogloss:1.03737
[36m(train_xgb_ray_tune pid=1839848)[0m [356]	train-mlogloss:1.03565	validation-mlogloss:1.08600
[36m(train_xgb_ray_tune pid=1839869)[0m [299]	train-mlogloss:0.98002	validation-mlogloss:1.07046
[36m(train_xgb_ray_tune pid=1839869)[0m [300]	train-mlogloss:0.97955	validation-mlogloss:1.07022
[36m(train_xgb_ray_tune pid=1839893)[0m [225]	train-mlogloss:0.94228	validation-mlogloss:1.07250
[36m(train_xgb_ray_tune pid=1839880)[0m [270]	train-mlogloss:1.50674	validation-mlogloss:1.49990
[36m(train_xgb_ray_tune pid=1839849)[0m [215]	train-mlogloss:1.26148	validation-mlogloss:1.28541
[36m(train_xgb_ray_tune pid=1839846)[0m [281]	train-mlogloss:1.16723	validation-mlogloss:1.18455
[36m(train_xgb_ray_tune pid=1839845)[0m [453]	train-mlogloss:0.82736	validation-mlogloss:1.03359
[36m(train_xgb_ray_tune pid=1839855)[0m [239]	train-mlogloss:0.90568	validation-mlogloss:1.04821
[36m(train_xgb_ray_tune pid=1839848)[0m [357]	train-mlogloss:1.03520	validation-mlogloss:1.08564
[36m(train_xgb_ray_tune pid=1839845)[0m [454]	train-mlogloss:0.82670	validation-mlogloss:1.03372
[36m(train_xgb_ray_tune pid=1839877)[0m [329]	train-mlogloss:1.17092	validation-mlogloss:1.16583
[36m(train_xgb_ray_tune pid=1839877)[0m [330]	train-mlogloss:1.17055	validation-mlogloss:1.16552
[36m(train_xgb_ray_tune pid=1839870)[0m [321]	train-mlogloss:1.31068	validation-mlogloss:1.28866
[36m(train_xgb_ray_tune pid=1839862)[0m [312]	train-mlogloss:1.00599	validation-mlogloss:1.06652
[36m(train_xgb_ray_tune pid=1839848)[0m [358]	train-mlogloss:1.03472	validation-mlogloss:1.08540
[36m(train_xgb_ray_tune pid=1839869)[0m [301]	train-mlogloss:0.97937	validation-mlogloss:1.07036
[36m(train_xgb_ray_tune pid=1839879)[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_xgb_ray_tune pid=1839879)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=1839869)[0m [302]	train-mlogloss:0.97926	validation-mlogloss:1.07026
[36m(train_xgb_ray_tune pid=1839893)[0m [226]	train-mlogloss:0.94102	validation-mlogloss:1.07202
[36m(train_xgb_ray_tune pid=1839894)[0m [219]	train-mlogloss:0.98038	validation-mlogloss:1.08693
[36m(train_xgb_ray_tune pid=1839880)[0m [271]	train-mlogloss:1.50508	validation-mlogloss:1.49816
[36m(train_xgb_ray_tune pid=1839855)[0m [240]	train-mlogloss:0.90527	validation-mlogloss:1.04810
[36m(train_xgb_ray_tune pid=1839870)[0m [322]	train-mlogloss:1.31048	validation-mlogloss:1.28842
[36m(train_xgb_ray_tune pid=1839871)[0m [335]	train-mlogloss:0.87372	validation-mlogloss:1.03721
[36m(train_xgb_ray_tune pid=1839849)[0m [216]	train-mlogloss:1.25984	validation-mlogloss:1.28383
[36m(train_xgb_ray_tune pid=1839862)[0m [313]	train-mlogloss:1.00593	validation-mlogloss:1.06649

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:44. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.2976 │
│ time_total_s                 59.2976 │
│ training_iteration                 1 │
│ validation_accuracy          0.59152 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=1839846)[0m [282]	train-mlogloss:1.16638	validation-mlogloss:1.18390

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:44. Total running time: 1min 2s
[36m(train_xgb_ray_tune pid=1839845)[0m [455]	train-mlogloss:0.82604	validation-mlogloss:1.03360
[36m(train_xgb_ray_tune pid=1839855)[0m [241]	train-mlogloss:0.90490	validation-mlogloss:1.04805
[36m(train_xgb_ray_tune pid=1839877)[0m [331]	train-mlogloss:1.17028	validation-mlogloss:1.16529
[36m(train_xgb_ray_tune pid=1839877)[0m [332]	train-mlogloss:1.17000	validation-mlogloss:1.16511
[36m(train_xgb_ray_tune pid=1839870)[0m [323]	train-mlogloss:1.31018	validation-mlogloss:1.28816
[36m(train_xgb_ray_tune pid=1839862)[0m [314]	train-mlogloss:1.00581	validation-mlogloss:1.06636
[36m(train_xgb_ray_tune pid=1839871)[0m [336]	train-mlogloss:0.87294	validation-mlogloss:1.03709
[36m(train_xgb_ray_tune pid=1839848)[0m [359]	train-mlogloss:1.03426	validation-mlogloss:1.08521
[36m(train_xgb_ray_tune pid=1839869)[0m [303]	train-mlogloss:0.97923	validation-mlogloss:1.07026
[36m(train_xgb_ray_tune pid=1839869)[0m [304]	train-mlogloss:0.97918	validation-mlogloss:1.07026
[36m(train_xgb_ray_tune pid=1839893)[0m [227]	train-mlogloss:0.93987	validation-mlogloss:1.07113
[36m(train_xgb_ray_tune pid=1839894)[0m [220]	train-mlogloss:0.97914	validation-mlogloss:1.08637
[36m(train_xgb_ray_tune pid=1839880)[0m [272]	train-mlogloss:1.50347	validation-mlogloss:1.49648
[36m(train_xgb_ray_tune pid=1839846)[0m [283]	train-mlogloss:1.16562	validation-mlogloss:1.18338
[36m(train_xgb_ray_tune pid=1839845)[0m [456]	train-mlogloss:0.82544	validation-mlogloss:1.03347
[36m(train_xgb_ray_tune pid=1839855)[0m [242]	train-mlogloss:0.90462	validation-mlogloss:1.04796
[36m(train_xgb_ray_tune pid=1839877)[0m [333]	train-mlogloss:1.16960	validation-mlogloss:1.16471
[36m(train_xgb_ray_tune pid=1839870)[0m [324]	train-mlogloss:1.30996	validation-mlogloss:1.28792
[36m(train_xgb_ray_tune pid=1839870)[0m [325]	train-mlogloss:1.30974	validation-mlogloss:1.28771
[36m(train_xgb_ray_tune pid=1839848)[0m [360]	train-mlogloss:1.03387	validation-mlogloss:1.08489
[36m(train_xgb_ray_tune pid=1839869)[0m [305]	train-mlogloss:0.97864	validation-mlogloss:1.06983
[36m(train_xgb_ray_tune pid=1839880)[0m [273]	train-mlogloss:1.50191	validation-mlogloss:1.49479
[36m(train_xgb_ray_tune pid=1839849)[0m [217]	train-mlogloss:1.25823	validation-mlogloss:1.28227
[36m(train_xgb_ray_tune pid=1839880)[0m [274]	train-mlogloss:1.50025	validation-mlogloss:1.49311
[36m(train_xgb_ray_tune pid=1839846)[0m [284]	train-mlogloss:1.16485	validation-mlogloss:1.18278
[36m(train_xgb_ray_tune pid=1839845)[0m [457]	train-mlogloss:0.82482	validation-mlogloss:1.03355
[36m(train_xgb_ray_tune pid=1839855)[0m [243]	train-mlogloss:0.90420	validation-mlogloss:1.04772
[36m(train_xgb_ray_tune pid=1839855)[0m [244]	train-mlogloss:0.90366	validation-mlogloss:1.04762
[36m(train_xgb_ray_tune pid=1839877)[0m [334]	train-mlogloss:1.16917	validation-mlogloss:1.16432
[36m(train_xgb_ray_tune pid=1839870)[0m [326]	train-mlogloss:1.30953	validation-mlogloss:1.28751
[36m(train_xgb_ray_tune pid=1839871)[0m [337]	train-mlogloss:0.87209	validation-mlogloss:1.03689
[36m(train_xgb_ray_tune pid=1839848)[0m [361]	train-mlogloss:1.03336	validation-mlogloss:1.08467
[36m(train_xgb_ray_tune pid=1839869)[0m [306]	train-mlogloss:0.97832	validation-mlogloss:1.06980
[36m(train_xgb_ray_tune pid=1839893)[0m [228]	train-mlogloss:0.93872	validation-mlogloss:1.07070
[36m(train_xgb_ray_tune pid=1839894)[0m [221]	train-mlogloss:0.97782	validation-mlogloss:1.08578
[36m(train_xgb_ray_tune pid=1839880)[0m [275]	train-mlogloss:1.49869	validation-mlogloss:1.49143
[36m(train_xgb_ray_tune pid=1839871)[0m [338]	train-mlogloss:0.87128	validation-mlogloss:1.03675
[36m(train_xgb_ray_tune pid=1839845)[0m [458]	train-mlogloss:0.82424	validation-mlogloss:1.03337
[36m(train_xgb_ray_tune pid=1839855)[0m [245]	train-mlogloss:0.90333	validation-mlogloss:1.04735
[36m(train_xgb_ray_tune pid=1839877)[0m [335]	train-mlogloss:1.16885	validation-mlogloss:1.16399
[36m(train_xgb_ray_tune pid=1839870)[0m [327]	train-mlogloss:1.30936	validation-mlogloss:1.28733
[36m(train_xgb_ray_tune pid=1839870)[0m [328]	train-mlogloss:1.30915	validation-mlogloss:1.28714
[36m(train_xgb_ray_tune pid=1839871)[0m [339]	train-mlogloss:0.87043	validation-mlogloss:1.03654
[36m(train_xgb_ray_tune pid=1839848)[0m [362]	train-mlogloss:1.03310	validation-mlogloss:1.08445
[36m(train_xgb_ray_tune pid=1839848)[0m [363]	train-mlogloss:1.03275	validation-mlogloss:1.08432
[36m(train_xgb_ray_tune pid=1839869)[0m [307]	train-mlogloss:0.97795	validation-mlogloss:1.06969
[36m(train_xgb_ray_tune pid=1839880)[0m [276]	train-mlogloss:1.49708	validation-mlogloss:1.48971
[36m(train_xgb_ray_tune pid=1839849)[0m [218]	train-mlogloss:1.25657	validation-mlogloss:1.28058
[36m(train_xgb_ray_tune pid=1839845)[0m [459]	train-mlogloss:0.82368	validation-mlogloss:1.03351
[36m(train_xgb_ray_tune pid=1839869)[0m [308]	train-mlogloss:0.97781	validation-mlogloss:1.06957
[36m(train_xgb_ray_tune pid=1839845)[0m [460]	train-mlogloss:0.82304	validation-mlogloss:1.03361
[36m(train_xgb_ray_tune pid=1839855)[0m [246]	train-mlogloss:0.90291	validation-mlogloss:1.04714
[36m(train_xgb_ray_tune pid=1839855)[0m [247]	train-mlogloss:0.90237	validation-mlogloss:1.04695
[36m(train_xgb_ray_tune pid=1839877)[0m [336]	train-mlogloss:1.16863	validation-mlogloss:1.16388
[36m(train_xgb_ray_tune pid=1839870)[0m [329]	train-mlogloss:1.30866	validation-mlogloss:1.28664
[36m(train_xgb_ray_tune pid=1839870)[0m [330]	train-mlogloss:1.30837	validation-mlogloss:1.28641
[36m(train_xgb_ray_tune pid=1839871)[0m [340]	train-mlogloss:0.86944	validation-mlogloss:1.03608
[36m(train_xgb_ray_tune pid=1839848)[0m [364]	train-mlogloss:1.03243	validation-mlogloss:1.08407
[36m(train_xgb_ray_tune pid=1839869)[0m [309]	train-mlogloss:0.97759	validation-mlogloss:1.06935
[36m(train_xgb_ray_tune pid=1839893)[0m [229]	train-mlogloss:0.93763	validation-mlogloss:1.07021
[36m(train_xgb_ray_tune pid=1839894)[0m [222]	train-mlogloss:0.97642	validation-mlogloss:1.08515
[36m(train_xgb_ray_tune pid=1839880)[0m [277]	train-mlogloss:1.49551	validation-mlogloss:1.48815

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:45. Total running time: 1min 3s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.7607 │
│ time_total_s                 59.7607 │
│ training_iteration                 1 │
│ validation_accuracy          0.61251 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=1839846)[0m [285]	train-mlogloss:1.16425	validation-mlogloss:1.18234
[36m(train_xgb_ray_tune pid=1839845)[0m [461]	train-mlogloss:0.82251	validation-mlogloss:1.03327

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:45. Total running time: 1min 3s
[36m(train_xgb_ray_tune pid=1839855)[0m [248]	train-mlogloss:0.90191	validation-mlogloss:1.04697
[36m(train_xgb_ray_tune pid=1839877)[0m [337]	train-mlogloss:1.16831	validation-mlogloss:1.16356
[36m(train_xgb_ray_tune pid=1839870)[0m [331]	train-mlogloss:1.30810	validation-mlogloss:1.28623
[36m(train_xgb_ray_tune pid=1839871)[0m [341]	train-mlogloss:0.86866	validation-mlogloss:1.03588
[36m(train_xgb_ray_tune pid=1839871)[0m [342]	train-mlogloss:0.86784	validation-mlogloss:1.03540
[36m(train_xgb_ray_tune pid=1839848)[0m [365]	train-mlogloss:1.03204	validation-mlogloss:1.08389
[36m(train_xgb_ray_tune pid=1839848)[0m [366]	train-mlogloss:1.03153	validation-mlogloss:1.08385
[36m(train_xgb_ray_tune pid=1839869)[0m [310]	train-mlogloss:0.97736	validation-mlogloss:1.06932
[36m(train_xgb_ray_tune pid=1839894)[0m [223]	train-mlogloss:0.97517	validation-mlogloss:1.08454
[36m(train_xgb_ray_tune pid=1839880)[0m [278]	train-mlogloss:1.49390	validation-mlogloss:1.48647
[36m(train_xgb_ray_tune pid=1839880)[0m [279]	train-mlogloss:1.49230	validation-mlogloss:1.48476
[36m(train_xgb_ray_tune pid=1839849)[0m [219]	train-mlogloss:1.25507	validation-mlogloss:1.27924
[36m(train_xgb_ray_tune pid=1839845)[0m [462]	train-mlogloss:0.82192	validation-mlogloss:1.03330
[36m(train_xgb_ray_tune pid=1839855)[0m [249]	train-mlogloss:0.90120	validation-mlogloss:1.04659
[36m(train_xgb_ray_tune pid=1839877)[0m [338]	train-mlogloss:1.16805	validation-mlogloss:1.16334
[36m(train_xgb_ray_tune pid=1839870)[0m [332]	train-mlogloss:1.30771	validation-mlogloss:1.28593
[36m(train_xgb_ray_tune pid=1839870)[0m [333]	train-mlogloss:1.30741	validation-mlogloss:1.28573
[36m(train_xgb_ray_tune pid=1839871)[0m [343]	train-mlogloss:0.86707	validation-mlogloss:1.03530
[36m(train_xgb_ray_tune pid=1839848)[0m [367]	train-mlogloss:1.03110	validation-mlogloss:1.08359
[36m(train_xgb_ray_tune pid=1839869)[0m [311]	train-mlogloss:0.97716	validation-mlogloss:1.06914
[36m(train_xgb_ray_tune pid=1839893)[0m [230]	train-mlogloss:0.93645	validation-mlogloss:1.06941
[36m(train_xgb_ray_tune pid=1839880)[0m [280]	train-mlogloss:1.49079	validation-mlogloss:1.48325
[36m(train_xgb_ray_tune pid=1839849)[0m [220]	train-mlogloss:1.25351	validation-mlogloss:1.27781
[36m(train_xgb_ray_tune pid=1839845)[0m [463]	train-mlogloss:0.82128	validation-mlogloss:1.03325
[36m(train_xgb_ray_tune pid=1839855)[0m [250]	train-mlogloss:0.90084	validation-mlogloss:1.04631
[36m(train_xgb_ray_tune pid=1839855)[0m [251]	train-mlogloss:0.90042	validation-mlogloss:1.04636
[36m(train_xgb_ray_tune pid=1839877)[0m [339]	train-mlogloss:1.16779	validation-mlogloss:1.16319
[36m(train_xgb_ray_tune pid=1839877)[0m [340]	train-mlogloss:1.16750	validation-mlogloss:1.16294
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[36m(train_xgb_ray_tune pid=1839871)[0m [344]	train-mlogloss:0.86642	validation-mlogloss:1.03519
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[36m(train_xgb_ray_tune pid=1839849)[0m [228]	train-mlogloss:1.24100	validation-mlogloss:1.26592
[36m(train_xgb_ray_tune pid=1839877)[0m [360]	train-mlogloss:1.16127	validation-mlogloss:1.15785
[36m(train_xgb_ray_tune pid=1839877)[0m [361]	train-mlogloss:1.16101	validation-mlogloss:1.15766
[36m(train_xgb_ray_tune pid=1839870)[0m [356]	train-mlogloss:1.30156	validation-mlogloss:1.28037
[36m(train_xgb_ray_tune pid=1839870)[0m [357]	train-mlogloss:1.30138	validation-mlogloss:1.28018
[36m(train_xgb_ray_tune pid=1839871)[0m [363]	train-mlogloss:0.85130	validation-mlogloss:1.03295
[36m(train_xgb_ray_tune pid=1839871)[0m [364]	train-mlogloss:0.85045	validation-mlogloss:1.03297
[36m(train_xgb_ray_tune pid=1839848)[0m [390]	train-mlogloss:1.02205	validation-mlogloss:1.07880
[36m(train_xgb_ray_tune pid=1839848)[0m [391]	train-mlogloss:1.02173	validation-mlogloss:1.07862
[36m(train_xgb_ray_tune pid=1839869)[0m [327]	train-mlogloss:0.97419	validation-mlogloss:1.06708
[36m(train_xgb_ray_tune pid=1839893)[0m [245]	train-mlogloss:0.91929	validation-mlogloss:1.06422
[36m(train_xgb_ray_tune pid=1839880)[0m [298]	train-mlogloss:1.46411	validation-mlogloss:1.45534
[36m(train_xgb_ray_tune pid=1839849)[0m [229]	train-mlogloss:1.23951	validation-mlogloss:1.26455
[36m(train_xgb_ray_tune pid=1839877)[0m [362]	train-mlogloss:1.16078	validation-mlogloss:1.15745
[36m(train_xgb_ray_tune pid=1839877)[0m [363]	train-mlogloss:1.16055	validation-mlogloss:1.15728
[36m(train_xgb_ray_tune pid=1839870)[0m [358]	train-mlogloss:1.30111	validation-mlogloss:1.27990
[36m(train_xgb_ray_tune pid=1839870)[0m [359]	train-mlogloss:1.30094	validation-mlogloss:1.27971
[36m(train_xgb_ray_tune pid=1839871)[0m [365]	train-mlogloss:0.84969	validation-mlogloss:1.03299
[36m(train_xgb_ray_tune pid=1839848)[0m [392]	train-mlogloss:1.02146	validation-mlogloss:1.07860
[36m(train_xgb_ray_tune pid=1839848)[0m [393]	train-mlogloss:1.02123	validation-mlogloss:1.07851
[36m(train_xgb_ray_tune pid=1839869)[0m [328]	train-mlogloss:0.97397	validation-mlogloss:1.06692
[36m(train_xgb_ray_tune pid=1839893)[0m [246]	train-mlogloss:0.91817	validation-mlogloss:1.06359
[36m(train_xgb_ray_tune pid=1839894)[0m [232]	train-mlogloss:0.96337	validation-mlogloss:1.07884
[36m(train_xgb_ray_tune pid=1839880)[0m [299]	train-mlogloss:1.46271	validation-mlogloss:1.45385

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:47. Total running time: 1min 5s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             62.0752 │
│ time_total_s                 62.0752 │
│ training_iteration                 1 │
│ validation_accuracy          0.61994 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:47. Total running time: 1min 5s
[36m(train_xgb_ray_tune pid=1839846)[0m [290]	train-mlogloss:1.16052	validation-mlogloss:1.17937
[36m(train_xgb_ray_tune pid=1839877)[0m [364]	train-mlogloss:1.16024	validation-mlogloss:1.15706
[36m(train_xgb_ray_tune pid=1839870)[0m [360]	train-mlogloss:1.30094	validation-mlogloss:1.27971
[36m(train_xgb_ray_tune pid=1839870)[0m [361]	train-mlogloss:1.30066	validation-mlogloss:1.27938
[36m(train_xgb_ray_tune pid=1839871)[0m [366]	train-mlogloss:0.84898	validation-mlogloss:1.03290
[36m(train_xgb_ray_tune pid=1839871)[0m [367]	train-mlogloss:0.84824	validation-mlogloss:1.03276
[36m(train_xgb_ray_tune pid=1839848)[0m [394]	train-mlogloss:1.02095	validation-mlogloss:1.07831
[36m(train_xgb_ray_tune pid=1839848)[0m [395]	train-mlogloss:1.02064	validation-mlogloss:1.07814
[36m(train_xgb_ray_tune pid=1839869)[0m [329]	train-mlogloss:0.97372	validation-mlogloss:1.06680
[36m(train_xgb_ray_tune pid=1839893)[0m [247]	train-mlogloss:0.91715	validation-mlogloss:1.06325
[36m(train_xgb_ray_tune pid=1839894)[0m [233]	train-mlogloss:0.96213	validation-mlogloss:1.07844
[36m(train_xgb_ray_tune pid=1839880)[0m [300]	train-mlogloss:1.46132	validation-mlogloss:1.45239
[36m(train_xgb_ray_tune pid=1839880)[0m [301]	train-mlogloss:1.45993	validation-mlogloss:1.45089
[36m(train_xgb_ray_tune pid=1839849)[0m [230]	train-mlogloss:1.23807	validation-mlogloss:1.26321
[36m(train_xgb_ray_tune pid=1839877)[0m [365]	train-mlogloss:1.16014	validation-mlogloss:1.15700
[36m(train_xgb_ray_tune pid=1839877)[0m [366]	train-mlogloss:1.15993	validation-mlogloss:1.15681
[36m(train_xgb_ray_tune pid=1839870)[0m [362]	train-mlogloss:1.30051	validation-mlogloss:1.27925
[36m(train_xgb_ray_tune pid=1839871)[0m [368]	train-mlogloss:0.84756	validation-mlogloss:1.03261
[36m(train_xgb_ray_tune pid=1839848)[0m [396]	train-mlogloss:1.02032	validation-mlogloss:1.07809
[36m(train_xgb_ray_tune pid=1839869)[0m [330]	train-mlogloss:0.97356	validation-mlogloss:1.06679
[36m(train_xgb_ray_tune pid=1839893)[0m [248]	train-mlogloss:0.91607	validation-mlogloss:1.06305
[36m(train_xgb_ray_tune pid=1839880)[0m [302]	train-mlogloss:1.45852	validation-mlogloss:1.44936
[36m(train_xgb_ray_tune pid=1839849)[0m [231]	train-mlogloss:1.23650	validation-mlogloss:1.26170

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:47. Total running time: 1min 5s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             62.1233 │
│ time_total_s                 62.1233 │
│ training_iteration                 1 │
│ validation_accuracy          0.61166 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:47. Total running time: 1min 5s
[36m(train_xgb_ray_tune pid=1839877)[0m [367]	train-mlogloss:1.15961	validation-mlogloss:1.15642
[36m(train_xgb_ray_tune pid=1839877)[0m [368]	train-mlogloss:1.15935	validation-mlogloss:1.15622
[36m(train_xgb_ray_tune pid=1839870)[0m [363]	train-mlogloss:1.30035	validation-mlogloss:1.27913
[36m(train_xgb_ray_tune pid=1839870)[0m [364]	train-mlogloss:1.30015	validation-mlogloss:1.27897
[36m(train_xgb_ray_tune pid=1839871)[0m [369]	train-mlogloss:0.84679	validation-mlogloss:1.03263
[36m(train_xgb_ray_tune pid=1839848)[0m [397]	train-mlogloss:1.01990	validation-mlogloss:1.07790
[36m(train_xgb_ray_tune pid=1839848)[0m [398]	train-mlogloss:1.01960	validation-mlogloss:1.07765
[36m(train_xgb_ray_tune pid=1839869)[0m [331]	train-mlogloss:0.97328	validation-mlogloss:1.06658
[36m(train_xgb_ray_tune pid=1839893)[0m [249]	train-mlogloss:0.91502	validation-mlogloss:1.06269
[36m(train_xgb_ray_tune pid=1839894)[0m [234]	train-mlogloss:0.96094	validation-mlogloss:1.07783
[36m(train_xgb_ray_tune pid=1839880)[0m [303]	train-mlogloss:1.45709	validation-mlogloss:1.44790
[36m(train_xgb_ray_tune pid=1839849)[0m [232]	train-mlogloss:1.23508	validation-mlogloss:1.26036
[36m(train_xgb_ray_tune pid=1839869)[0m [332]	train-mlogloss:0.97293	validation-mlogloss:1.06661
[36m(train_xgb_ray_tune pid=1839846)[0m [291]	train-mlogloss:1.15987	validation-mlogloss:1.17890
[36m(train_xgb_ray_tune pid=1839877)[0m [369]	train-mlogloss:1.15916	validation-mlogloss:1.15606
[36m(train_xgb_ray_tune pid=1839870)[0m [365]	train-mlogloss:1.29999	validation-mlogloss:1.27882
[36m(train_xgb_ray_tune pid=1839870)[0m [366]	train-mlogloss:1.29971	validation-mlogloss:1.27862
[36m(train_xgb_ray_tune pid=1839871)[0m [370]	train-mlogloss:0.84604	validation-mlogloss:1.03245
[36m(train_xgb_ray_tune pid=1839871)[0m [371]	train-mlogloss:0.84540	validation-mlogloss:1.03200
[36m(train_xgb_ray_tune pid=1839848)[0m [399]	train-mlogloss:1.01929	validation-mlogloss:1.07739
[36m(train_xgb_ray_tune pid=1839869)[0m [333]	train-mlogloss:0.97279	validation-mlogloss:1.06653
[36m(train_xgb_ray_tune pid=1839893)[0m [250]	train-mlogloss:0.91400	validation-mlogloss:1.06236
[36m(train_xgb_ray_tune pid=1839894)[0m [235]	train-mlogloss:0.95972	validation-mlogloss:1.07716
[36m(train_xgb_ray_tune pid=1839880)[0m [304]	train-mlogloss:1.45567	validation-mlogloss:1.44644
[36m(train_xgb_ray_tune pid=1839880)[0m [305]	train-mlogloss:1.45427	validation-mlogloss:1.44501
[36m(train_xgb_ray_tune pid=1839849)[0m [233]	train-mlogloss:1.23368	validation-mlogloss:1.25906
[36m(train_xgb_ray_tune pid=1839877)[0m [370]	train-mlogloss:1.15888	validation-mlogloss:1.15578
[36m(train_xgb_ray_tune pid=1839849)[0m [234]	train-mlogloss:1.23221	validation-mlogloss:1.25780
[36m(train_xgb_ray_tune pid=1839869)[0m [334]	train-mlogloss:0.97251	validation-mlogloss:1.06641
[36m(train_xgb_ray_tune pid=1839877)[0m [371]	train-mlogloss:1.15856	validation-mlogloss:1.15550
[36m(train_xgb_ray_tune pid=1839870)[0m [367]	train-mlogloss:1.29958	validation-mlogloss:1.27845
[36m(train_xgb_ray_tune pid=1839870)[0m [368]	train-mlogloss:1.29939	validation-mlogloss:1.27832
[36m(train_xgb_ray_tune pid=1839871)[0m [372]	train-mlogloss:0.84460	validation-mlogloss:1.03176
[36m(train_xgb_ray_tune pid=1839871)[0m [373]	train-mlogloss:0.84398	validation-mlogloss:1.03162
[36m(train_xgb_ray_tune pid=1839848)[0m [400]	train-mlogloss:1.01894	validation-mlogloss:1.07721
[36m(train_xgb_ray_tune pid=1839848)[0m [401]	train-mlogloss:1.01870	validation-mlogloss:1.07716
[36m(train_xgb_ray_tune pid=1839869)[0m [335]	train-mlogloss:0.97243	validation-mlogloss:1.06635
[36m(train_xgb_ray_tune pid=1839893)[0m [251]	train-mlogloss:0.91298	validation-mlogloss:1.06188
[36m(train_xgb_ray_tune pid=1839894)[0m [236]	train-mlogloss:0.95848	validation-mlogloss:1.07680
[36m(train_xgb_ray_tune pid=1839880)[0m [306]	train-mlogloss:1.45288	validation-mlogloss:1.44362
[36m(train_xgb_ray_tune pid=1839849)[0m [235]	train-mlogloss:1.23082	validation-mlogloss:1.25650
[36m(train_xgb_ray_tune pid=1839869)[0m [336]	train-mlogloss:0.97229	validation-mlogloss:1.06625
[36m(train_xgb_ray_tune pid=1839893)[0m [252]	train-mlogloss:0.91190	validation-mlogloss:1.06149
[36m(train_xgb_ray_tune pid=1839877)[0m [372]	train-mlogloss:1.15847	validation-mlogloss:1.15542
[36m(train_xgb_ray_tune pid=1839877)[0m [373]	train-mlogloss:1.15817	validation-mlogloss:1.15512
[36m(train_xgb_ray_tune pid=1839870)[0m [369]	train-mlogloss:1.29927	validation-mlogloss:1.27824
[36m(train_xgb_ray_tune pid=1839870)[0m [370]	train-mlogloss:1.29908	validation-mlogloss:1.27808
[36m(train_xgb_ray_tune pid=1839871)[0m [374]	train-mlogloss:0.84318	validation-mlogloss:1.03146
[36m(train_xgb_ray_tune pid=1839848)[0m [402]	train-mlogloss:1.01839	validation-mlogloss:1.07705
[36m(train_xgb_ray_tune pid=1839848)[0m [403]	train-mlogloss:1.01800	validation-mlogloss:1.07682
[36m(train_xgb_ray_tune pid=1839869)[0m [337]	train-mlogloss:0.97225	validation-mlogloss:1.06619
[36m(train_xgb_ray_tune pid=1839893)[0m [253]	train-mlogloss:0.91074	validation-mlogloss:1.06090
[36m(train_xgb_ray_tune pid=1839894)[0m [237]	train-mlogloss:0.95721	validation-mlogloss:1.07643
[36m(train_xgb_ray_tune pid=1839880)[0m [307]	train-mlogloss:1.45152	validation-mlogloss:1.44226
[36m(train_xgb_ray_tune pid=1839849)[0m [236]	train-mlogloss:1.22938	validation-mlogloss:1.25512
[36m(train_xgb_ray_tune pid=1839869)[0m [338]	train-mlogloss:0.97197	validation-mlogloss:1.06590
[36m(train_xgb_ray_tune pid=1839846)[0m [292]	train-mlogloss:1.15913	validation-mlogloss:1.17828
[36m(train_xgb_ray_tune pid=1839877)[0m [374]	train-mlogloss:1.15794	validation-mlogloss:1.15494
[36m(train_xgb_ray_tune pid=1839877)[0m [375]	train-mlogloss:1.15771	validation-mlogloss:1.15474
[36m(train_xgb_ray_tune pid=1839870)[0m [371]	train-mlogloss:1.29889	validation-mlogloss:1.27793
[36m(train_xgb_ray_tune pid=1839870)[0m [372]	train-mlogloss:1.29885	validation-mlogloss:1.27792
[36m(train_xgb_ray_tune pid=1839871)[0m [375]	train-mlogloss:0.84254	validation-mlogloss:1.03140
[36m(train_xgb_ray_tune pid=1839871)[0m [376]	train-mlogloss:0.84184	validation-mlogloss:1.03134
[36m(train_xgb_ray_tune pid=1839848)[0m [404]	train-mlogloss:1.01758	validation-mlogloss:1.07653
[36m(train_xgb_ray_tune pid=1839869)[0m [339]	train-mlogloss:0.97197	validation-mlogloss:1.06589
[36m(train_xgb_ray_tune pid=1839869)[0m [340]	train-mlogloss:0.97182	validation-mlogloss:1.06593
[36m(train_xgb_ray_tune pid=1839893)[0m [254]	train-mlogloss:0.90962	validation-mlogloss:1.06069
[36m(train_xgb_ray_tune pid=1839894)[0m [238]	train-mlogloss:0.95601	validation-mlogloss:1.07627
[36m(train_xgb_ray_tune pid=1839880)[0m [308]	train-mlogloss:1.45015	validation-mlogloss:1.44086
[36m(train_xgb_ray_tune pid=1839880)[0m [309]	train-mlogloss:1.44868	validation-mlogloss:1.43926
[36m(train_xgb_ray_tune pid=1839849)[0m [237]	train-mlogloss:1.22798	validation-mlogloss:1.25370
[36m(train_xgb_ray_tune pid=1839848)[0m [405]	train-mlogloss:1.01723	validation-mlogloss:1.07629
[36m(train_xgb_ray_tune pid=1839877)[0m [376]	train-mlogloss:1.15766	validation-mlogloss:1.15470
[36m(train_xgb_ray_tune pid=1839877)[0m [377]	train-mlogloss:1.15751	validation-mlogloss:1.15454
[36m(train_xgb_ray_tune pid=1839870)[0m [373]	train-mlogloss:1.29863	validation-mlogloss:1.27770
[36m(train_xgb_ray_tune pid=1839870)[0m [374]	train-mlogloss:1.29855	validation-mlogloss:1.27762
[36m(train_xgb_ray_tune pid=1839871)[0m [377]	train-mlogloss:0.84110	validation-mlogloss:1.03130
[36m(train_xgb_ray_tune pid=1839848)[0m [406]	train-mlogloss:1.01696	validation-mlogloss:1.07617
[36m(train_xgb_ray_tune pid=1839869)[0m [341]	train-mlogloss:0.97174	validation-mlogloss:1.06590
[36m(train_xgb_ray_tune pid=1839869)[0m [342]	train-mlogloss:0.97160	validation-mlogloss:1.06582
[36m(train_xgb_ray_tune pid=1839893)[0m [255]	train-mlogloss:0.90858	validation-mlogloss:1.06020
[36m(train_xgb_ray_tune pid=1839894)[0m [239]	train-mlogloss:0.95481	validation-mlogloss:1.07585
[36m(train_xgb_ray_tune pid=1839880)[0m [310]	train-mlogloss:1.44734	validation-mlogloss:1.43793
[36m(train_xgb_ray_tune pid=1839849)[0m [238]	train-mlogloss:1.22662	validation-mlogloss:1.25229
[36m(train_xgb_ray_tune pid=1839877)[0m [378]	train-mlogloss:1.15743	validation-mlogloss:1.15443
[36m(train_xgb_ray_tune pid=1839870)[0m [375]	train-mlogloss:1.29837	validation-mlogloss:1.27744
[36m(train_xgb_ray_tune pid=1839870)[0m [376]	train-mlogloss:1.29812	validation-mlogloss:1.27722
[36m(train_xgb_ray_tune pid=1839871)[0m [378]	train-mlogloss:0.84045	validation-mlogloss:1.03104
[36m(train_xgb_ray_tune pid=1839871)[0m [379]	train-mlogloss:0.83969	validation-mlogloss:1.03073
[36m(train_xgb_ray_tune pid=1839848)[0m [407]	train-mlogloss:1.01666	validation-mlogloss:1.07614
[36m(train_xgb_ray_tune pid=1839848)[0m [408]	train-mlogloss:1.01641	validation-mlogloss:1.07596
[36m(train_xgb_ray_tune pid=1839869)[0m [343]	train-mlogloss:0.97157	validation-mlogloss:1.06586
[36m(train_xgb_ray_tune pid=1839869)[0m [344]	train-mlogloss:0.97140	validation-mlogloss:1.06574
[36m(train_xgb_ray_tune pid=1839893)[0m [256]	train-mlogloss:0.90757	validation-mlogloss:1.05980
[36m(train_xgb_ray_tune pid=1839894)[0m [240]	train-mlogloss:0.95363	validation-mlogloss:1.07532
[36m(train_xgb_ray_tune pid=1839880)[0m [311]	train-mlogloss:1.44595	validation-mlogloss:1.43655
[36m(train_xgb_ray_tune pid=1839880)[0m [312]	train-mlogloss:1.44457	validation-mlogloss:1.43512
[36m(train_xgb_ray_tune pid=1839849)[0m [239]	train-mlogloss:1.22526	validation-mlogloss:1.25116
[36m(train_xgb_ray_tune pid=1839877)[0m [379]	train-mlogloss:1.15718	validation-mlogloss:1.15424
[36m(train_xgb_ray_tune pid=1839894)[0m [241]	train-mlogloss:0.95235	validation-mlogloss:1.07472
[36m(train_xgb_ray_tune pid=1839877)[0m [380]	train-mlogloss:1.15704	validation-mlogloss:1.15413
[36m(train_xgb_ray_tune pid=1839870)[0m [377]	train-mlogloss:1.29793	validation-mlogloss:1.27701
[36m(train_xgb_ray_tune pid=1839871)[0m [380]	train-mlogloss:0.83902	validation-mlogloss:1.03081
[36m(train_xgb_ray_tune pid=1839848)[0m [409]	train-mlogloss:1.01622	validation-mlogloss:1.07583
[36m(train_xgb_ray_tune pid=1839848)[0m [410]	train-mlogloss:1.01584	validation-mlogloss:1.07564
[36m(train_xgb_ray_tune pid=1839869)[0m [345]	train-mlogloss:0.97100	validation-mlogloss:1.06569
[36m(train_xgb_ray_tune pid=1839869)[0m [346]	train-mlogloss:0.97093	validation-mlogloss:1.06572
[36m(train_xgb_ray_tune pid=1839893)[0m [257]	train-mlogloss:0.90654	validation-mlogloss:1.05961
[36m(train_xgb_ray_tune pid=1839894)[0m [242]	train-mlogloss:0.95105	validation-mlogloss:1.07426
[36m(train_xgb_ray_tune pid=1839880)[0m [313]	train-mlogloss:1.44323	validation-mlogloss:1.43375
[36m(train_xgb_ray_tune pid=1839849)[0m [240]	train-mlogloss:1.22385	validation-mlogloss:1.24997
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[36m(train_xgb_ray_tune pid=1839877)[0m [402]	train-mlogloss:1.15302	validation-mlogloss:1.15093
[36m(train_xgb_ray_tune pid=1839893)[0m [271]	train-mlogloss:0.89220	validation-mlogloss:1.05522
[36m(train_xgb_ray_tune pid=1839871)[0m [399]	train-mlogloss:0.82536	validation-mlogloss:1.02869
[36m(train_xgb_ray_tune pid=1839848)[0m [433]	train-mlogloss:1.01045	validation-mlogloss:1.07323
[36m(train_xgb_ray_tune pid=1839877)[0m [403]	train-mlogloss:1.15291	validation-mlogloss:1.15081
[36m(train_xgb_ray_tune pid=1839870)[0m [402]	train-mlogloss:1.29420	validation-mlogloss:1.27379
[36m(train_xgb_ray_tune pid=1839870)[0m [403]	train-mlogloss:1.29414	validation-mlogloss:1.27376
[36m(train_xgb_ray_tune pid=1839871)[0m [400]	train-mlogloss:0.82465	validation-mlogloss:1.02839
[36m(train_xgb_ray_tune pid=1839848)[0m [434]	train-mlogloss:1.01019	validation-mlogloss:1.07314
[36m(train_xgb_ray_tune pid=1839869)[0m [371]	train-mlogloss:0.96745	validation-mlogloss:1.06433
[36m(train_xgb_ray_tune pid=1839869)[0m [372]	train-mlogloss:0.96717	validation-mlogloss:1.06420
[36m(train_xgb_ray_tune pid=1839893)[0m [272]	train-mlogloss:0.89117	validation-mlogloss:1.05511
[36m(train_xgb_ray_tune pid=1839894)[0m [256]	train-mlogloss:0.93470	validation-mlogloss:1.06763
[36m(train_xgb_ray_tune pid=1839880)[0m [331]	train-mlogloss:1.41993	validation-mlogloss:1.40973
[36m(train_xgb_ray_tune pid=1839849)[0m [254]	train-mlogloss:1.20491	validation-mlogloss:1.23244
[36m(train_xgb_ray_tune pid=1839877)[0m [404]	train-mlogloss:1.15274	validation-mlogloss:1.15070
[36m(train_xgb_ray_tune pid=1839877)[0m [405]	train-mlogloss:1.15258	validation-mlogloss:1.15056
[36m(train_xgb_ray_tune pid=1839870)[0m [404]	train-mlogloss:1.29402	validation-mlogloss:1.27362
[36m(train_xgb_ray_tune pid=1839870)[0m [405]	train-mlogloss:1.29382	validation-mlogloss:1.27344
[36m(train_xgb_ray_tune pid=1839871)[0m [401]	train-mlogloss:0.82396	validation-mlogloss:1.02846
[36m(train_xgb_ray_tune pid=1839871)[0m [402]	train-mlogloss:0.82329	validation-mlogloss:1.02859
[36m(train_xgb_ray_tune pid=1839848)[0m [435]	train-mlogloss:1.00989	validation-mlogloss:1.07297
[36m(train_xgb_ray_tune pid=1839848)[0m [436]	train-mlogloss:1.00960	validation-mlogloss:1.07278
[36m(train_xgb_ray_tune pid=1839877)[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 4x across cluster][0m
[36m(train_xgb_ray_tune pid=1839877)[0m   _log_deprecation_warning([32m [repeated 4x across cluster][0m
[36m(train_xgb_ray_tune pid=1839869)[0m [373]	train-mlogloss:0.96696	validation-mlogloss:1.06423
[36m(train_xgb_ray_tune pid=1839869)[0m [374]	train-mlogloss:0.96671	validation-mlogloss:1.06418
[36m(train_xgb_ray_tune pid=1839893)[0m [273]	train-mlogloss:0.89010	validation-mlogloss:1.05474
[36m(train_xgb_ray_tune pid=1839894)[0m [257]	train-mlogloss:0.93349	validation-mlogloss:1.06719
[36m(train_xgb_ray_tune pid=1839880)[0m [332]	train-mlogloss:1.41875	validation-mlogloss:1.40850
[36m(train_xgb_ray_tune pid=1839849)[0m [255]	train-mlogloss:1.20364	validation-mlogloss:1.23118
[36m(train_xgb_ray_tune pid=1839880)[0m [333]	train-mlogloss:1.41752	validation-mlogloss:1.40722
[36m(train_xgb_ray_tune pid=1839894)[0m [258]	train-mlogloss:0.93230	validation-mlogloss:1.06673
[36m(train_xgb_ray_tune pid=1839846)[0m [298]	train-mlogloss:1.15534	validation-mlogloss:1.17527
[36m(train_xgb_ray_tune pid=1839870)[0m [406]	train-mlogloss:1.29366	validation-mlogloss:1.27327
[36m(train_xgb_ray_tune pid=1839871)[0m [403]	train-mlogloss:0.82255	validation-mlogloss:1.02855
[36m(train_xgb_ray_tune pid=1839848)[0m [437]	train-mlogloss:1.00928	validation-mlogloss:1.07267
[36m(train_xgb_ray_tune pid=1839848)[0m [438]	train-mlogloss:1.00918	validation-mlogloss:1.07265
[36m(train_xgb_ray_tune pid=1839869)[0m [375]	train-mlogloss:0.96660	validation-mlogloss:1.06409
[36m(train_xgb_ray_tune pid=1839869)[0m [376]	train-mlogloss:0.96653	validation-mlogloss:1.06397
[36m(train_xgb_ray_tune pid=1839893)[0m [274]	train-mlogloss:0.88909	validation-mlogloss:1.05445
[36m(train_xgb_ray_tune pid=1839894)[0m [259]	train-mlogloss:0.93115	validation-mlogloss:1.06629
[36m(train_xgb_ray_tune pid=1839880)[0m [334]	train-mlogloss:1.41633	validation-mlogloss:1.40600
[36m(train_xgb_ray_tune pid=1839849)[0m [256]	train-mlogloss:1.20242	validation-mlogloss:1.23013
[36m(train_xgb_ray_tune pid=1839870)[0m [407]	train-mlogloss:1.29348	validation-mlogloss:1.27310
[36m(train_xgb_ray_tune pid=1839870)[0m [408]	train-mlogloss:1.29344	validation-mlogloss:1.27307
[36m(train_xgb_ray_tune pid=1839870)[0m [409]	train-mlogloss:1.29327	validation-mlogloss:1.27291
[36m(train_xgb_ray_tune pid=1839871)[0m [404]	train-mlogloss:0.82177	validation-mlogloss:1.02861
[36m(train_xgb_ray_tune pid=1839871)[0m [405]	train-mlogloss:0.82107	validation-mlogloss:1.02843
[36m(train_xgb_ray_tune pid=1839848)[0m [439]	train-mlogloss:1.00898	validation-mlogloss:1.07257
[36m(train_xgb_ray_tune pid=1839848)[0m [440]	train-mlogloss:1.00874	validation-mlogloss:1.07244
[36m(train_xgb_ray_tune pid=1839869)[0m [377]	train-mlogloss:0.96638	validation-mlogloss:1.06389
[36m(train_xgb_ray_tune pid=1839869)[0m [378]	train-mlogloss:0.96629	validation-mlogloss:1.06390
[36m(train_xgb_ray_tune pid=1839893)[0m [275]	train-mlogloss:0.88806	validation-mlogloss:1.05393
[36m(train_xgb_ray_tune pid=1839894)[0m [260]	train-mlogloss:0.93004	validation-mlogloss:1.06588
[36m(train_xgb_ray_tune pid=1839880)[0m [335]	train-mlogloss:1.41508	validation-mlogloss:1.40464
[36m(train_xgb_ray_tune pid=1839849)[0m [257]	train-mlogloss:1.20120	validation-mlogloss:1.22887
[36m(train_xgb_ray_tune pid=1839870)[0m [410]	train-mlogloss:1.29314	validation-mlogloss:1.27282
[36m(train_xgb_ray_tune pid=1839871)[0m [406]	train-mlogloss:0.82042	validation-mlogloss:1.02834
[36m(train_xgb_ray_tune pid=1839848)[0m [441]	train-mlogloss:1.00852	validation-mlogloss:1.07234
[36m(train_xgb_ray_tune pid=1839848)[0m [442]	train-mlogloss:1.00837	validation-mlogloss:1.07227
[36m(train_xgb_ray_tune pid=1839869)[0m [379]	train-mlogloss:0.96618	validation-mlogloss:1.06379
[36m(train_xgb_ray_tune pid=1839869)[0m [380]	train-mlogloss:0.96610	validation-mlogloss:1.06373
[36m(train_xgb_ray_tune pid=1839893)[0m [276]	train-mlogloss:0.88715	validation-mlogloss:1.05379
[36m(train_xgb_ray_tune pid=1839894)[0m [261]	train-mlogloss:0.92889	validation-mlogloss:1.06546
[36m(train_xgb_ray_tune pid=1839880)[0m [336]	train-mlogloss:1.41390	validation-mlogloss:1.40344
[36m(train_xgb_ray_tune pid=1839880)[0m [337]	train-mlogloss:1.41266	validation-mlogloss:1.40215
[36m(train_xgb_ray_tune pid=1839849)[0m [258]	train-mlogloss:1.19992	validation-mlogloss:1.22788
[36m(train_xgb_ray_tune pid=1839870)[0m [411]	train-mlogloss:1.29300	validation-mlogloss:1.27264
[36m(train_xgb_ray_tune pid=1839869)[0m [381]	train-mlogloss:0.96609	validation-mlogloss:1.06373
[36m(train_xgb_ray_tune pid=1839849)[0m [259]	train-mlogloss:1.19870	validation-mlogloss:1.22676
[36m(train_xgb_ray_tune pid=1839846)[0m [299]	train-mlogloss:1.15483	validation-mlogloss:1.17486
[36m(train_xgb_ray_tune pid=1839870)[0m [412]	train-mlogloss:1.29298	validation-mlogloss:1.27263
[36m(train_xgb_ray_tune pid=1839870)[0m [413]	train-mlogloss:1.29278	validation-mlogloss:1.27247
[36m(train_xgb_ray_tune pid=1839871)[0m [407]	train-mlogloss:0.81983	validation-mlogloss:1.02823
[36m(train_xgb_ray_tune pid=1839871)[0m [408]	train-mlogloss:0.81926	validation-mlogloss:1.02824
[36m(train_xgb_ray_tune pid=1839848)[0m [443]	train-mlogloss:1.00812	validation-mlogloss:1.07214
[36m(train_xgb_ray_tune pid=1839848)[0m [444]	train-mlogloss:1.00793	validation-mlogloss:1.07209
[36m(train_xgb_ray_tune pid=1839869)[0m [382]	train-mlogloss:0.96600	validation-mlogloss:1.06367
[36m(train_xgb_ray_tune pid=1839893)[0m [277]	train-mlogloss:0.88618	validation-mlogloss:1.05368
[36m(train_xgb_ray_tune pid=1839894)[0m [262]	train-mlogloss:0.92775	validation-mlogloss:1.06510
[36m(train_xgb_ray_tune pid=1839880)[0m [338]	train-mlogloss:1.41143	validation-mlogloss:1.40084
[36m(train_xgb_ray_tune pid=1839849)[0m [260]	train-mlogloss:1.19741	validation-mlogloss:1.22553
[36m(train_xgb_ray_tune pid=1839869)[0m [383]	train-mlogloss:0.96589	validation-mlogloss:1.06356
[36m(train_xgb_ray_tune pid=1839871)[0m [409]	train-mlogloss:0.81864	validation-mlogloss:1.02836
[36m(train_xgb_ray_tune pid=1839848)[0m [445]	train-mlogloss:1.00786	validation-mlogloss:1.07205
[36m(train_xgb_ray_tune pid=1839848)[0m [446]	train-mlogloss:1.00762	validation-mlogloss:1.07180

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:50. Total running time: 1min 8s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             65.0449 │
│ time_total_s                 65.0449 │
│ training_iteration                 1 │
│ validation_accuracy          0.58961 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=1839869)[0m [384]	train-mlogloss:0.96575	validation-mlogloss:1.06350
[36m(train_xgb_ray_tune pid=1839869)[0m [385]	train-mlogloss:0.96573	validation-mlogloss:1.06344
[36m(train_xgb_ray_tune pid=1839893)[0m [278]	train-mlogloss:0.88517	validation-mlogloss:1.05306
[36m(train_xgb_ray_tune pid=1839893)[0m [279]	train-mlogloss:0.88430	validation-mlogloss:1.05273
[36m(train_xgb_ray_tune pid=1839894)[0m [263]	train-mlogloss:0.92672	validation-mlogloss:1.06477

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:50. Total running time: 1min 8s
[36m(train_xgb_ray_tune pid=1839880)[0m [339]	train-mlogloss:1.41026	validation-mlogloss:1.39963
[36m(train_xgb_ray_tune pid=1839880)[0m [340]	train-mlogloss:1.40906	validation-mlogloss:1.39829
[36m(train_xgb_ray_tune pid=1839849)[0m [261]	train-mlogloss:1.19612	validation-mlogloss:1.22427
[36m(train_xgb_ray_tune pid=1839846)[0m [300]	train-mlogloss:1.15405	validation-mlogloss:1.17428
[36m(train_xgb_ray_tune pid=1839871)[0m [410]	train-mlogloss:0.81793	validation-mlogloss:1.02815
[36m(train_xgb_ray_tune pid=1839871)[0m [411]	train-mlogloss:0.81724	validation-mlogloss:1.02792
[36m(train_xgb_ray_tune pid=1839848)[0m [447]	train-mlogloss:1.00736	validation-mlogloss:1.07178
[36m(train_xgb_ray_tune pid=1839848)[0m [448]	train-mlogloss:1.00711	validation-mlogloss:1.07166
[36m(train_xgb_ray_tune pid=1839869)[0m [386]	train-mlogloss:0.96573	validation-mlogloss:1.06344
[36m(train_xgb_ray_tune pid=1839869)[0m [387]	train-mlogloss:0.96532	validation-mlogloss:1.06311
[36m(train_xgb_ray_tune pid=1839893)[0m [280]	train-mlogloss:0.88333	validation-mlogloss:1.05250
[36m(train_xgb_ray_tune pid=1839894)[0m [264]	train-mlogloss:0.92564	validation-mlogloss:1.06421
[36m(train_xgb_ray_tune pid=1839894)[0m [265]	train-mlogloss:0.92451	validation-mlogloss:1.06400
[36m(train_xgb_ray_tune pid=1839880)[0m [341]	train-mlogloss:1.40788	validation-mlogloss:1.39700
[36m(train_xgb_ray_tune pid=1839849)[0m [262]	train-mlogloss:1.19492	validation-mlogloss:1.22321
[36m(train_xgb_ray_tune pid=1839846)[0m [301]	train-mlogloss:1.15344	validation-mlogloss:1.17380
[36m(train_xgb_ray_tune pid=1839846)[0m [302]	train-mlogloss:1.15284	validation-mlogloss:1.17325
[36m(train_xgb_ray_tune pid=1839871)[0m [412]	train-mlogloss:0.81656	validation-mlogloss:1.02765
[36m(train_xgb_ray_tune pid=1839848)[0m [449]	train-mlogloss:1.00692	validation-mlogloss:1.07162
[36m(train_xgb_ray_tune pid=1839848)[0m [450]	train-mlogloss:1.00668	validation-mlogloss:1.07148
[36m(train_xgb_ray_tune pid=1839869)[0m [388]	train-mlogloss:0.96529	validation-mlogloss:1.06317
[36m(train_xgb_ray_tune pid=1839869)[0m [389]	train-mlogloss:0.96516	validation-mlogloss:1.06307
[36m(train_xgb_ray_tune pid=1839893)[0m [281]	train-mlogloss:0.88236	validation-mlogloss:1.05216
[36m(train_xgb_ray_tune pid=1839894)[0m [266]	train-mlogloss:0.92342	validation-mlogloss:1.06355
[36m(train_xgb_ray_tune pid=1839880)[0m [342]	train-mlogloss:1.40666	validation-mlogloss:1.39569
[36m(train_xgb_ray_tune pid=1839849)[0m [263]	train-mlogloss:1.19372	validation-mlogloss:1.22207
[36m(train_xgb_ray_tune pid=1839846)[0m [303]	train-mlogloss:1.15227	validation-mlogloss:1.17286
[36m(train_xgb_ray_tune pid=1839871)[0m [413]	train-mlogloss:0.81591	validation-mlogloss:1.02718
[36m(train_xgb_ray_tune pid=1839871)[0m [414]	train-mlogloss:0.81530	validation-mlogloss:1.02683
[36m(train_xgb_ray_tune pid=1839848)[0m [451]	train-mlogloss:1.00654	validation-mlogloss:1.07144
[36m(train_xgb_ray_tune pid=1839848)[0m [452]	train-mlogloss:1.00638	validation-mlogloss:1.07135
[36m(train_xgb_ray_tune pid=1839869)[0m [390]	train-mlogloss:0.96516	validation-mlogloss:1.06306
[36m(train_xgb_ray_tune pid=1839869)[0m [391]	train-mlogloss:0.96488	validation-mlogloss:1.06296
[36m(train_xgb_ray_tune pid=1839893)[0m [282]	train-mlogloss:0.88140	validation-mlogloss:1.05162
[36m(train_xgb_ray_tune pid=1839894)[0m [267]	train-mlogloss:0.92238	validation-mlogloss:1.06339
[36m(train_xgb_ray_tune pid=1839880)[0m [343]	train-mlogloss:1.40549	validation-mlogloss:1.39455
[36m(train_xgb_ray_tune pid=1839880)[0m [344]	train-mlogloss:1.40437	validation-mlogloss:1.39340
[36m(train_xgb_ray_tune pid=1839849)[0m [264]	train-mlogloss:1.19254	validation-mlogloss:1.22089
[36m(train_xgb_ray_tune pid=1839846)[0m [304]	train-mlogloss:1.15169	validation-mlogloss:1.17243
[36m(train_xgb_ray_tune pid=1839846)[0m [305]	train-mlogloss:1.15111	validation-mlogloss:1.17190
[36m(train_xgb_ray_tune pid=1839871)[0m [415]	train-mlogloss:0.81472	validation-mlogloss:1.02672
[36m(train_xgb_ray_tune pid=1839871)[0m [416]	train-mlogloss:0.81399	validation-mlogloss:1.02626
[36m(train_xgb_ray_tune pid=1839848)[0m [453]	train-mlogloss:1.00603	validation-mlogloss:1.07112
[36m(train_xgb_ray_tune pid=1839848)[0m [454]	train-mlogloss:1.00576	validation-mlogloss:1.07099
[36m(train_xgb_ray_tune pid=1839869)[0m [392]	train-mlogloss:0.96479	validation-mlogloss:1.06290
[36m(train_xgb_ray_tune pid=1839869)[0m [393]	train-mlogloss:0.96479	validation-mlogloss:1.06289
[36m(train_xgb_ray_tune pid=1839893)[0m [283]	train-mlogloss:0.88039	validation-mlogloss:1.05127
[36m(train_xgb_ray_tune pid=1839894)[0m [268]	train-mlogloss:0.92132	validation-mlogloss:1.06276
[36m(train_xgb_ray_tune pid=1839880)[0m [345]	train-mlogloss:1.40321	validation-mlogloss:1.39217
[36m(train_xgb_ray_tune pid=1839849)[0m [265]	train-mlogloss:1.19131	validation-mlogloss:1.21976

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:51. Total running time: 1min 9s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              65.537 │
│ time_total_s                  65.537 │
│ training_iteration                 1 │
│ validation_accuracy          0.57243 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:51. Total running time: 1min 9s
[36m(train_xgb_ray_tune pid=1839846)[0m [306]	train-mlogloss:1.15051	validation-mlogloss:1.17137
[36m(train_xgb_ray_tune pid=1839871)[0m [417]	train-mlogloss:0.81334	validation-mlogloss:1.02596
[36m(train_xgb_ray_tune pid=1839848)[0m [455]	train-mlogloss:1.00567	validation-mlogloss:1.07097
[36m(train_xgb_ray_tune pid=1839848)[0m [456]	train-mlogloss:1.00560	validation-mlogloss:1.07090
[36m(train_xgb_ray_tune pid=1839869)[0m [394]	train-mlogloss:0.96440	validation-mlogloss:1.06285
[36m(train_xgb_ray_tune pid=1839869)[0m [395]	train-mlogloss:0.96421	validation-mlogloss:1.06278
[36m(train_xgb_ray_tune pid=1839893)[0m [284]	train-mlogloss:0.87941	validation-mlogloss:1.05110
[36m(train_xgb_ray_tune pid=1839894)[0m [269]	train-mlogloss:0.92022	validation-mlogloss:1.06239
[36m(train_xgb_ray_tune pid=1839880)[0m [346]	train-mlogloss:1.40205	validation-mlogloss:1.39100
[36m(train_xgb_ray_tune pid=1839880)[0m [347]	train-mlogloss:1.40091	validation-mlogloss:1.38985
[36m(train_xgb_ray_tune pid=1839849)[0m [266]	train-mlogloss:1.19017	validation-mlogloss:1.21867
[36m(train_xgb_ray_tune pid=1839849)[0m [267]	train-mlogloss:1.18895	validation-mlogloss:1.21763
[36m(train_xgb_ray_tune pid=1839846)[0m [307]	train-mlogloss:1.14983	validation-mlogloss:1.17080
[36m(train_xgb_ray_tune pid=1839846)[0m [308]	train-mlogloss:1.14924	validation-mlogloss:1.17026
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[36m(train_xgb_ray_tune pid=1839848)[0m [486]	train-mlogloss:1.00055	validation-mlogloss:1.06846
[36m(train_xgb_ray_tune pid=1839869)[0m [426]	train-mlogloss:0.96123	validation-mlogloss:1.06179
[36m(train_xgb_ray_tune pid=1839869)[0m [427]	train-mlogloss:0.96123	validation-mlogloss:1.06180
[36m(train_xgb_ray_tune pid=1839893)[0m [301]	train-mlogloss:0.86377	validation-mlogloss:1.04589
[36m(train_xgb_ray_tune pid=1839894)[0m [286]	train-mlogloss:0.90217	validation-mlogloss:1.05610
[36m(train_xgb_ray_tune pid=1839880)[0m [367]	train-mlogloss:1.37865	validation-mlogloss:1.36679
[36m(train_xgb_ray_tune pid=1839880)[0m [368]	train-mlogloss:1.37763	validation-mlogloss:1.36576
[36m(train_xgb_ray_tune pid=1839849)[0m [283]	train-mlogloss:1.17135	validation-mlogloss:1.20210
[36m(train_xgb_ray_tune pid=1839846)[0m [329]	train-mlogloss:1.13762	validation-mlogloss:1.16074
[36m(train_xgb_ray_tune pid=1839871)[0m [443]	train-mlogloss:0.79656	validation-mlogloss:1.02354
[36m(train_xgb_ray_tune pid=1839848)[0m [487]	train-mlogloss:1.00042	validation-mlogloss:1.06838
[36m(train_xgb_ray_tune pid=1839848)[0m [488]	train-mlogloss:1.00023	validation-mlogloss:1.06834
[36m(train_xgb_ray_tune pid=1839869)[0m [428]	train-mlogloss:0.96098	validation-mlogloss:1.06166
[36m(train_xgb_ray_tune pid=1839869)[0m [429]	train-mlogloss:0.96091	validation-mlogloss:1.06165
[36m(train_xgb_ray_tune pid=1839893)[0m [302]	train-mlogloss:0.86288	validation-mlogloss:1.04568
[36m(train_xgb_ray_tune pid=1839893)[0m [303]	train-mlogloss:0.86199	validation-mlogloss:1.04563
[36m(train_xgb_ray_tune pid=1839894)[0m [287]	train-mlogloss:0.90124	validation-mlogloss:1.05594
[36m(train_xgb_ray_tune pid=1839894)[0m [288]	train-mlogloss:0.90027	validation-mlogloss:1.05589
[36m(train_xgb_ray_tune pid=1839880)[0m [369]	train-mlogloss:1.37655	validation-mlogloss:1.36465
[36m(train_xgb_ray_tune pid=1839849)[0m [284]	train-mlogloss:1.17022	validation-mlogloss:1.20116
[36m(train_xgb_ray_tune pid=1839846)[0m [330]	train-mlogloss:1.13715	validation-mlogloss:1.16040
[36m(train_xgb_ray_tune pid=1839846)[0m [331]	train-mlogloss:1.13660	validation-mlogloss:1.15999
[36m(train_xgb_ray_tune pid=1839871)[0m [444]	train-mlogloss:0.79588	validation-mlogloss:1.02348
[36m(train_xgb_ray_tune pid=1839871)[0m [445]	train-mlogloss:0.79532	validation-mlogloss:1.02366
[36m(train_xgb_ray_tune pid=1839869)[0m [430]	train-mlogloss:0.96088	validation-mlogloss:1.06163
[36m(train_xgb_ray_tune pid=1839869)[0m [431]	train-mlogloss:0.96068	validation-mlogloss:1.06158
[36m(train_xgb_ray_tune pid=1839869)[0m [432]	train-mlogloss:0.96068	validation-mlogloss:1.06158
[36m(train_xgb_ray_tune pid=1839893)[0m [304]	train-mlogloss:0.86107	validation-mlogloss:1.04559
[36m(train_xgb_ray_tune pid=1839894)[0m [289]	train-mlogloss:0.89928	validation-mlogloss:1.05562
[36m(train_xgb_ray_tune pid=1839880)[0m [370]	train-mlogloss:1.37550	validation-mlogloss:1.36357
[36m(train_xgb_ray_tune pid=1839880)[0m [371]	train-mlogloss:1.37443	validation-mlogloss:1.36246
[36m(train_xgb_ray_tune pid=1839849)[0m [285]	train-mlogloss:1.16922	validation-mlogloss:1.20030
[36m(train_xgb_ray_tune pid=1839849)[0m [286]	train-mlogloss:1.16821	validation-mlogloss:1.19940
[36m(train_xgb_ray_tune pid=1839846)[0m [332]	train-mlogloss:1.13615	validation-mlogloss:1.15968
[36m(train_xgb_ray_tune pid=1839871)[0m [446]	train-mlogloss:0.79463	validation-mlogloss:1.02329
[36m(train_xgb_ray_tune pid=1839871)[0m [447]	train-mlogloss:0.79404	validation-mlogloss:1.02337
[36m(train_xgb_ray_tune pid=1839869)[0m [433]	train-mlogloss:0.96044	validation-mlogloss:1.06152
[36m(train_xgb_ray_tune pid=1839869)[0m [434]	train-mlogloss:0.96041	validation-mlogloss:1.06151
[36m(train_xgb_ray_tune pid=1839893)[0m [305]	train-mlogloss:0.86029	validation-mlogloss:1.04539
[36m(train_xgb_ray_tune pid=1839894)[0m [290]	train-mlogloss:0.89825	validation-mlogloss:1.05538
[36m(train_xgb_ray_tune pid=1839880)[0m [372]	train-mlogloss:1.37338	validation-mlogloss:1.36128
[36m(train_xgb_ray_tune pid=1839849)[0m [287]	train-mlogloss:1.16725	validation-mlogloss:1.19853
[36m(train_xgb_ray_tune pid=1839846)[0m [333]	train-mlogloss:1.13559	validation-mlogloss:1.15924
[36m(train_xgb_ray_tune pid=1839846)[0m [334]	train-mlogloss:1.13524	validation-mlogloss:1.15897
[36m(train_xgb_ray_tune pid=1839871)[0m [448]	train-mlogloss:0.79336	validation-mlogloss:1.02324
[36m(train_xgb_ray_tune pid=1839871)[0m [449]	train-mlogloss:0.79269	validation-mlogloss:1.02285
[36m(train_xgb_ray_tune pid=1839869)[0m [435]	train-mlogloss:0.96020	validation-mlogloss:1.06138
[36m(train_xgb_ray_tune pid=1839869)[0m [436]	train-mlogloss:0.96013	validation-mlogloss:1.06131
[36m(train_xgb_ray_tune pid=1839893)[0m [306]	train-mlogloss:0.85945	validation-mlogloss:1.04494
[36m(train_xgb_ray_tune pid=1839894)[0m [291]	train-mlogloss:0.89729	validation-mlogloss:1.05519
[36m(train_xgb_ray_tune pid=1839880)[0m [373]	train-mlogloss:1.37227	validation-mlogloss:1.36017
[36m(train_xgb_ray_tune pid=1839880)[0m [374]	train-mlogloss:1.37125	validation-mlogloss:1.35912
[36m(train_xgb_ray_tune pid=1839849)[0m [288]	train-mlogloss:1.16624	validation-mlogloss:1.19763
[36m(train_xgb_ray_tune pid=1839846)[0m [335]	train-mlogloss:1.13486	validation-mlogloss:1.15860
[36m(train_xgb_ray_tune pid=1839846)[0m [336]	train-mlogloss:1.13452	validation-mlogloss:1.15837
[36m(train_xgb_ray_tune pid=1839869)[0m [437]	train-mlogloss:0.96010	validation-mlogloss:1.06129
[36m(train_xgb_ray_tune pid=1839869)[0m [438]	train-mlogloss:0.96003	validation-mlogloss:1.06127
[36m(train_xgb_ray_tune pid=1839869)[0m [439]	train-mlogloss:0.95997	validation-mlogloss:1.06128
[36m(train_xgb_ray_tune pid=1839893)[0m [307]	train-mlogloss:0.85854	validation-mlogloss:1.04482
[36m(train_xgb_ray_tune pid=1839893)[0m [308]	train-mlogloss:0.85757	validation-mlogloss:1.04462
[36m(train_xgb_ray_tune pid=1839894)[0m [292]	train-mlogloss:0.89630	validation-mlogloss:1.05489
[36m(train_xgb_ray_tune pid=1839894)[0m [293]	train-mlogloss:0.89530	validation-mlogloss:1.05462
[36m(train_xgb_ray_tune pid=1839880)[0m [375]	train-mlogloss:1.37017	validation-mlogloss:1.35807
[36m(train_xgb_ray_tune pid=1839849)[0m [289]	train-mlogloss:1.16526	validation-mlogloss:1.19670
[36m(train_xgb_ray_tune pid=1839880)[0m [376]	train-mlogloss:1.36911	validation-mlogloss:1.35692
[36m(train_xgb_ray_tune pid=1839846)[0m [337]	train-mlogloss:1.13413	validation-mlogloss:1.15804
[36m(train_xgb_ray_tune pid=1839869)[0m [440]	train-mlogloss:0.95979	validation-mlogloss:1.06141
[36m(train_xgb_ray_tune pid=1839869)[0m [441]	train-mlogloss:0.95972	validation-mlogloss:1.06135
[36m(train_xgb_ray_tune pid=1839893)[0m [309]	train-mlogloss:0.85676	validation-mlogloss:1.04399
[36m(train_xgb_ray_tune pid=1839894)[0m [294]	train-mlogloss:0.89430	validation-mlogloss:1.05425
[36m(train_xgb_ray_tune pid=1839880)[0m [377]	train-mlogloss:1.36805	validation-mlogloss:1.35586
[36m(train_xgb_ray_tune pid=1839849)[0m [290]	train-mlogloss:1.16432	validation-mlogloss:1.19590
[36m(train_xgb_ray_tune pid=1839849)[0m [291]	train-mlogloss:1.16319	validation-mlogloss:1.19502
[36m(train_xgb_ray_tune pid=1839846)[0m [338]	train-mlogloss:1.13375	validation-mlogloss:1.15773

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:53. Total running time: 1min 11s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             68.0264 │
│ time_total_s                 68.0264 │
│ training_iteration                 1 │
│ validation_accuracy          0.60297 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:53. Total running time: 1min 11s
[36m(train_xgb_ray_tune pid=1839846)[0m [339]	train-mlogloss:1.13320	validation-mlogloss:1.15726
[36m(train_xgb_ray_tune pid=1839869)[0m [442]	train-mlogloss:0.95968	validation-mlogloss:1.06132
[36m(train_xgb_ray_tune pid=1839869)[0m [443]	train-mlogloss:0.95956	validation-mlogloss:1.06122
[36m(train_xgb_ray_tune pid=1839869)[0m [444]	train-mlogloss:0.95944	validation-mlogloss:1.06111
[36m(train_xgb_ray_tune pid=1839893)[0m [310]	train-mlogloss:0.85586	validation-mlogloss:1.04385
[36m(train_xgb_ray_tune pid=1839894)[0m [295]	train-mlogloss:0.89335	validation-mlogloss:1.05414
[36m(train_xgb_ray_tune pid=1839880)[0m [378]	train-mlogloss:1.36704	validation-mlogloss:1.35483
[36m(train_xgb_ray_tune pid=1839880)[0m [379]	train-mlogloss:1.36600	validation-mlogloss:1.35375
[36m(train_xgb_ray_tune pid=1839849)[0m [292]	train-mlogloss:1.16222	validation-mlogloss:1.19418
[36m(train_xgb_ray_tune pid=1839846)[0m [340]	train-mlogloss:1.13282	validation-mlogloss:1.15696
[36m(train_xgb_ray_tune pid=1839846)[0m [341]	train-mlogloss:1.13254	validation-mlogloss:1.15678
[36m(train_xgb_ray_tune pid=1839869)[0m [445]	train-mlogloss:0.95944	validation-mlogloss:1.06111
[36m(train_xgb_ray_tune pid=1839869)[0m [446]	train-mlogloss:0.95942	validation-mlogloss:1.06108
[36m(train_xgb_ray_tune pid=1839893)[0m [311]	train-mlogloss:0.85496	validation-mlogloss:1.04354
[36m(train_xgb_ray_tune pid=1839893)[0m [312]	train-mlogloss:0.85413	validation-mlogloss:1.04355
[36m(train_xgb_ray_tune pid=1839894)[0m [296]	train-mlogloss:0.89240	validation-mlogloss:1.05377
[36m(train_xgb_ray_tune pid=1839894)[0m [297]	train-mlogloss:0.89143	validation-mlogloss:1.05366
[36m(train_xgb_ray_tune pid=1839880)[0m [380]	train-mlogloss:1.36495	validation-mlogloss:1.35268
[36m(train_xgb_ray_tune pid=1839849)[0m [293]	train-mlogloss:1.16118	validation-mlogloss:1.19347
[36m(train_xgb_ray_tune pid=1839846)[0m [342]	train-mlogloss:1.13214	validation-mlogloss:1.15647
[36m(train_xgb_ray_tune pid=1839846)[0m [343]	train-mlogloss:1.13174	validation-mlogloss:1.15623
[36m(train_xgb_ray_tune pid=1839869)[0m [447]	train-mlogloss:0.95936	validation-mlogloss:1.06105
[36m(train_xgb_ray_tune pid=1839869)[0m [448]	train-mlogloss:0.95928	validation-mlogloss:1.06107
[36m(train_xgb_ray_tune pid=1839869)[0m [449]	train-mlogloss:0.95921	validation-mlogloss:1.06111
[36m(train_xgb_ray_tune pid=1839893)[0m [313]	train-mlogloss:0.85328	validation-mlogloss:1.04322
[36m(train_xgb_ray_tune pid=1839894)[0m [298]	train-mlogloss:0.89044	validation-mlogloss:1.05330
[36m(train_xgb_ray_tune pid=1839880)[0m [381]	train-mlogloss:1.36391	validation-mlogloss:1.35163
[36m(train_xgb_ray_tune pid=1839880)[0m [382]	train-mlogloss:1.36293	validation-mlogloss:1.35060
[36m(train_xgb_ray_tune pid=1839849)[0m [294]	train-mlogloss:1.16020	validation-mlogloss:1.19262
[36m(train_xgb_ray_tune pid=1839849)[0m [295]	train-mlogloss:1.15924	validation-mlogloss:1.19179

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:53. Total running time: 1min 12s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             68.2561 │
│ time_total_s                 68.2561 │
│ training_iteration                 1 │
│ validation_accuracy          0.61803 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:53. Total running time: 1min 12s
[36m(train_xgb_ray_tune pid=1839846)[0m [344]	train-mlogloss:1.13131	validation-mlogloss:1.15593
[36m(train_xgb_ray_tune pid=1839869)[0m [450]	train-mlogloss:0.95921	validation-mlogloss:1.06112
[36m(train_xgb_ray_tune pid=1839869)[0m [451]	train-mlogloss:0.95921	validation-mlogloss:1.06112
[36m(train_xgb_ray_tune pid=1839893)[0m [314]	train-mlogloss:0.85258	validation-mlogloss:1.04307
[36m(train_xgb_ray_tune pid=1839894)[0m [299]	train-mlogloss:0.88940	validation-mlogloss:1.05286
[36m(train_xgb_ray_tune pid=1839880)[0m [383]	train-mlogloss:1.36196	validation-mlogloss:1.34956
[36m(train_xgb_ray_tune pid=1839849)[0m [296]	train-mlogloss:1.15828	validation-mlogloss:1.19097
[36m(train_xgb_ray_tune pid=1839880)[0m [384]	train-mlogloss:1.36098	validation-mlogloss:1.34855
[36m(train_xgb_ray_tune pid=1839846)[0m [345]	train-mlogloss:1.13081	validation-mlogloss:1.15537
[36m(train_xgb_ray_tune pid=1839893)[0m [315]	train-mlogloss:0.85178	validation-mlogloss:1.04280
[36m(train_xgb_ray_tune pid=1839846)[0m [346]	train-mlogloss:1.13044	validation-mlogloss:1.15510
[36m(train_xgb_ray_tune pid=1839869)[0m [452]	train-mlogloss:0.95916	validation-mlogloss:1.06108
[36m(train_xgb_ray_tune pid=1839869)[0m [453]	train-mlogloss:0.95900	validation-mlogloss:1.06101
[36m(train_xgb_ray_tune pid=1839869)[0m [454]	train-mlogloss:0.95885	validation-mlogloss:1.06099
[36m(train_xgb_ray_tune pid=1839893)[0m [316]	train-mlogloss:0.85092	validation-mlogloss:1.04270
[36m(train_xgb_ray_tune pid=1839894)[0m [300]	train-mlogloss:0.88844	validation-mlogloss:1.05252
[36m(train_xgb_ray_tune pid=1839894)[0m [301]	train-mlogloss:0.88746	validation-mlogloss:1.05229
[36m(train_xgb_ray_tune pid=1839880)[0m [385]	train-mlogloss:1.35993	validation-mlogloss:1.34752
[36m(train_xgb_ray_tune pid=1839849)[0m [297]	train-mlogloss:1.15729	validation-mlogloss:1.19022
[36m(train_xgb_ray_tune pid=1839846)[0m [347]	train-mlogloss:1.13011	validation-mlogloss:1.15483
[36m(train_xgb_ray_tune pid=1839846)[0m [348]	train-mlogloss:1.12966	validation-mlogloss:1.15447
[36m(train_xgb_ray_tune pid=1839869)[0m [455]	train-mlogloss:0.95876	validation-mlogloss:1.06099
[36m(train_xgb_ray_tune pid=1839869)[0m [456]	train-mlogloss:0.95876	validation-mlogloss:1.06102
[36m(train_xgb_ray_tune pid=1839869)[0m [457]	train-mlogloss:0.95868	validation-mlogloss:1.06084
[36m(train_xgb_ray_tune pid=1839893)[0m [317]	train-mlogloss:0.85009	validation-mlogloss:1.04236
[36m(train_xgb_ray_tune pid=1839894)[0m [302]	train-mlogloss:0.88641	validation-mlogloss:1.05206
[36m(train_xgb_ray_tune pid=1839880)[0m [386]	train-mlogloss:1.35891	validation-mlogloss:1.34650
[36m(train_xgb_ray_tune pid=1839880)[0m [387]	train-mlogloss:1.35793	validation-mlogloss:1.34552
[36m(train_xgb_ray_tune pid=1839849)[0m [298]	train-mlogloss:1.15633	validation-mlogloss:1.18936
[36m(train_xgb_ray_tune pid=1839849)[0m [299]	train-mlogloss:1.15542	validation-mlogloss:1.18858
[36m(train_xgb_ray_tune pid=1839846)[0m [349]	train-mlogloss:1.12912	validation-mlogloss:1.15412
[36m(train_xgb_ray_tune pid=1839846)[0m [350]	train-mlogloss:1.12879	validation-mlogloss:1.15379
[36m(train_xgb_ray_tune pid=1839869)[0m [458]	train-mlogloss:0.95865	validation-mlogloss:1.06083
[36m(train_xgb_ray_tune pid=1839869)[0m [459]	train-mlogloss:0.95856	validation-mlogloss:1.06063
[36m(train_xgb_ray_tune pid=1839893)[0m [318]	train-mlogloss:0.84930	validation-mlogloss:1.04220
[36m(train_xgb_ray_tune pid=1839893)[0m [319]	train-mlogloss:0.84859	validation-mlogloss:1.04205
[36m(train_xgb_ray_tune pid=1839894)[0m [303]	train-mlogloss:0.88549	validation-mlogloss:1.05185
[36m(train_xgb_ray_tune pid=1839894)[0m [304]	train-mlogloss:0.88454	validation-mlogloss:1.05165
[36m(train_xgb_ray_tune pid=1839880)[0m [388]	train-mlogloss:1.35694	validation-mlogloss:1.34443
[36m(train_xgb_ray_tune pid=1839880)[0m [389]	train-mlogloss:1.35594	validation-mlogloss:1.34344
[36m(train_xgb_ray_tune pid=1839849)[0m [300]	train-mlogloss:1.15453	validation-mlogloss:1.18774
[36m(train_xgb_ray_tune pid=1839869)[0m [460]	train-mlogloss:0.95853	validation-mlogloss:1.06065
[36m(train_xgb_ray_tune pid=1839846)[0m [351]	train-mlogloss:1.12840	validation-mlogloss:1.15346
[36m(train_xgb_ray_tune pid=1839846)[0m [352]	train-mlogloss:1.12799	validation-mlogloss:1.15313
[36m(train_xgb_ray_tune pid=1839869)[0m [461]	train-mlogloss:0.95850	validation-mlogloss:1.06064
[36m(train_xgb_ray_tune pid=1839869)[0m [462]	train-mlogloss:0.95825	validation-mlogloss:1.06052
[36m(train_xgb_ray_tune pid=1839893)[0m [320]	train-mlogloss:0.84778	validation-mlogloss:1.04222
[36m(train_xgb_ray_tune pid=1839894)[0m [305]	train-mlogloss:0.88361	validation-mlogloss:1.05128
[36m(train_xgb_ray_tune pid=1839880)[0m [390]	train-mlogloss:1.35501	validation-mlogloss:1.34253
[36m(train_xgb_ray_tune pid=1839880)[0m [391]	train-mlogloss:1.35404	validation-mlogloss:1.34150
[36m(train_xgb_ray_tune pid=1839849)[0m [301]	train-mlogloss:1.15358	validation-mlogloss:1.18702
[36m(train_xgb_ray_tune pid=1839849)[0m [302]	train-mlogloss:1.15260	validation-mlogloss:1.18605
[36m(train_xgb_ray_tune pid=1839846)[0m [353]	train-mlogloss:1.12738	validation-mlogloss:1.15267
[36m(train_xgb_ray_tune pid=1839846)[0m [354]	train-mlogloss:1.12709	validation-mlogloss:1.15253
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[36m(train_xgb_ray_tune pid=1839869)[0m   _log_deprecation_warning([32m [repeated 4x across cluster][0m
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[36m(train_xgb_ray_tune pid=1839849)[0m [324]	train-mlogloss:1.13352	validation-mlogloss:1.16959
[36m(train_xgb_ray_tune pid=1839849)[0m [325]	train-mlogloss:1.13268	validation-mlogloss:1.16891
[36m(train_xgb_ray_tune pid=1839846)[0m [385]	train-mlogloss:1.11758	validation-mlogloss:1.14521
[36m(train_xgb_ray_tune pid=1839846)[0m [386]	train-mlogloss:1.11732	validation-mlogloss:1.14507
[36m(train_xgb_ray_tune pid=1839893)[0m [345]	train-mlogloss:0.82798	validation-mlogloss:1.03826
[36m(train_xgb_ray_tune pid=1839893)[0m [346]	train-mlogloss:0.82720	validation-mlogloss:1.03808
[36m(train_xgb_ray_tune pid=1839894)[0m [330]	train-mlogloss:0.86056	validation-mlogloss:1.04423
[36m(train_xgb_ray_tune pid=1839880)[0m [420]	train-mlogloss:1.32776	validation-mlogloss:1.31519
[36m(train_xgb_ray_tune pid=1839880)[0m [421]	train-mlogloss:1.32684	validation-mlogloss:1.31433
[36m(train_xgb_ray_tune pid=1839849)[0m [326]	train-mlogloss:1.13190	validation-mlogloss:1.16833
[36m(train_xgb_ray_tune pid=1839849)[0m [327]	train-mlogloss:1.13111	validation-mlogloss:1.16765
[36m(train_xgb_ray_tune pid=1839846)[0m [387]	train-mlogloss:1.11694	validation-mlogloss:1.14475
[36m(train_xgb_ray_tune pid=1839893)[0m [347]	train-mlogloss:0.82648	validation-mlogloss:1.03816
[36m(train_xgb_ray_tune pid=1839894)[0m [331]	train-mlogloss:0.85964	validation-mlogloss:1.04411
[36m(train_xgb_ray_tune pid=1839894)[0m [332]	train-mlogloss:0.85869	validation-mlogloss:1.04379
[36m(train_xgb_ray_tune pid=1839880)[0m [422]	train-mlogloss:1.32592	validation-mlogloss:1.31352
[36m(train_xgb_ray_tune pid=1839849)[0m [328]	train-mlogloss:1.13033	validation-mlogloss:1.16708
[36m(train_xgb_ray_tune pid=1839846)[0m [388]	train-mlogloss:1.11658	validation-mlogloss:1.14452
[36m(train_xgb_ray_tune pid=1839880)[0m [423]	train-mlogloss:1.32506	validation-mlogloss:1.31272
[36m(train_xgb_ray_tune pid=1839846)[0m [389]	train-mlogloss:1.11623	validation-mlogloss:1.14421
[36m(train_xgb_ray_tune pid=1839893)[0m [348]	train-mlogloss:0.82580	validation-mlogloss:1.03794
[36m(train_xgb_ray_tune pid=1839893)[0m [349]	train-mlogloss:0.82506	validation-mlogloss:1.03795
[36m(train_xgb_ray_tune pid=1839894)[0m [333]	train-mlogloss:0.85788	validation-mlogloss:1.04350
[36m(train_xgb_ray_tune pid=1839880)[0m [424]	train-mlogloss:1.32425	validation-mlogloss:1.31188
[36m(train_xgb_ray_tune pid=1839849)[0m [329]	train-mlogloss:1.12947	validation-mlogloss:1.16653
[36m(train_xgb_ray_tune pid=1839849)[0m [330]	train-mlogloss:1.12866	validation-mlogloss:1.16584
[36m(train_xgb_ray_tune pid=1839846)[0m [390]	train-mlogloss:1.11591	validation-mlogloss:1.14392

Trial trial_0195f finished iteration 1 at 2025-10-25 19:43:56. Total running time: 1min 14s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             70.9648 │
│ time_total_s                 70.9648 │
│ training_iteration                 1 │
│ validation_accuracy          0.61569 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:43:56. Total running time: 1min 14s
[36m(train_xgb_ray_tune pid=1839846)[0m [391]	train-mlogloss:1.11555	validation-mlogloss:1.14364
[36m(train_xgb_ray_tune pid=1839893)[0m [350]	train-mlogloss:0.82439	validation-mlogloss:1.03763
[36m(train_xgb_ray_tune pid=1839894)[0m [334]	train-mlogloss:0.85708	validation-mlogloss:1.04347
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[36m(train_xgb_ray_tune pid=1839849)[0m [331]	train-mlogloss:1.12781	validation-mlogloss:1.16511
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[36m(train_xgb_ray_tune pid=1839849)[0m [334]	train-mlogloss:1.12548	validation-mlogloss:1.16326
[36m(train_xgb_ray_tune pid=1839846)[0m [397]	train-mlogloss:1.11373	validation-mlogloss:1.14222
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[36m(train_xgb_ray_tune pid=1839893)[0m [355]	train-mlogloss:0.82092	validation-mlogloss:1.03683
[36m(train_xgb_ray_tune pid=1839894)[0m [339]	train-mlogloss:0.85288	validation-mlogloss:1.04282
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[36m(train_xgb_ray_tune pid=1839846)[0m [401]	train-mlogloss:1.11284	validation-mlogloss:1.14171
[36m(train_xgb_ray_tune pid=1839846)[0m [402]	train-mlogloss:1.11262	validation-mlogloss:1.14148
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[36m(train_xgb_ray_tune pid=1839894)[0m [342]	train-mlogloss:0.85040	validation-mlogloss:1.04218
[36m(train_xgb_ray_tune pid=1839894)[0m [343]	train-mlogloss:0.84950	validation-mlogloss:1.04172
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[36m(train_xgb_ray_tune pid=1839846)[0m [404]	train-mlogloss:1.11225	validation-mlogloss:1.14124
[36m(train_xgb_ray_tune pid=1839846)[0m [405]	train-mlogloss:1.11211	validation-mlogloss:1.14118
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[36m(train_xgb_ray_tune pid=1839880)[0m [438]	train-mlogloss:1.31266	validation-mlogloss:1.30041
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[36m(train_xgb_ray_tune pid=1839893)[0m [404]	train-mlogloss:0.78870	validation-mlogloss:1.03105
[36m(train_xgb_ray_tune pid=1839893)[0m [405]	train-mlogloss:0.78828	validation-mlogloss:1.03097
[36m(train_xgb_ray_tune pid=1839894)[0m [387]	train-mlogloss:0.81453	validation-mlogloss:1.03408
[36m(train_xgb_ray_tune pid=1839894)[0m [388]	train-mlogloss:0.81385	validation-mlogloss:1.03387
[36m(train_xgb_ray_tune pid=1839880)[0m [488]	train-mlogloss:1.27648	validation-mlogloss:1.26539
[36m(train_xgb_ray_tune pid=1839880)[0m [489]	train-mlogloss:1.27580	validation-mlogloss:1.26478
[36m(train_xgb_ray_tune pid=1839849)[0m [385]	train-mlogloss:1.09180	validation-mlogloss:1.13679
[36m(train_xgb_ray_tune pid=1839849)[0m [386]	train-mlogloss:1.09131	validation-mlogloss:1.13643
[36m(train_xgb_ray_tune pid=1839846)[0m [472]	train-mlogloss:1.10143	validation-mlogloss:1.13369
[36m(train_xgb_ray_tune pid=1839846)[0m [473]	train-mlogloss:1.10129	validation-mlogloss:1.13368
[36m(train_xgb_ray_tune pid=1839893)[0m [406]	train-mlogloss:0.78761	validation-mlogloss:1.03122
[36m(train_xgb_ray_tune pid=1839894)[0m [389]	train-mlogloss:0.81305	validation-mlogloss:1.03380
[36m(train_xgb_ray_tune pid=1839880)[0m [490]	train-mlogloss:1.27516	validation-mlogloss:1.26426
[36m(train_xgb_ray_tune pid=1839880)[0m [491]	train-mlogloss:1.27452	validation-mlogloss:1.26370
[36m(train_xgb_ray_tune pid=1839849)[0m [387]	train-mlogloss:1.09083	validation-mlogloss:1.13608
[36m(train_xgb_ray_tune pid=1839894)[0m [390]	train-mlogloss:0.81224	validation-mlogloss:1.03361
[36m(train_xgb_ray_tune pid=1839893)[0m [407]	train-mlogloss:0.78707	validation-mlogloss:1.03117
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[36m(train_xgb_ray_tune pid=1839846)[0m [476]	train-mlogloss:1.10077	validation-mlogloss:1.13342
[36m(train_xgb_ray_tune pid=1839893)[0m [408]	train-mlogloss:0.78647	validation-mlogloss:1.03118
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[36m(train_xgb_ray_tune pid=1839849)[0m [390]	train-mlogloss:1.08937	validation-mlogloss:1.13487
[36m(train_xgb_ray_tune pid=1839849)[0m [391]	train-mlogloss:1.08890	validation-mlogloss:1.13451
[36m(train_xgb_ray_tune pid=1839846)[0m [479]	train-mlogloss:1.10040	validation-mlogloss:1.13322
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[36m(train_xgb_ray_tune pid=1839849)[0m [392]	train-mlogloss:1.08841	validation-mlogloss:1.13419
[36m(train_xgb_ray_tune pid=1839846)[0m [481]	train-mlogloss:1.09983	validation-mlogloss:1.13290
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[36m(train_xgb_ray_tune pid=1839893)[0m [413]	train-mlogloss:0.78363	validation-mlogloss:1.03042
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[36m(train_xgb_ray_tune pid=1839849)[0m [393]	train-mlogloss:1.08791	validation-mlogloss:1.13377
[36m(train_xgb_ray_tune pid=1839849)[0m [394]	train-mlogloss:1.08737	validation-mlogloss:1.13327
[36m(train_xgb_ray_tune pid=1839846)[0m [484]	train-mlogloss:1.09948	validation-mlogloss:1.13263
[36m(train_xgb_ray_tune pid=1839846)[0m [485]	train-mlogloss:1.09935	validation-mlogloss:1.13256
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[36m(train_xgb_ray_tune pid=1839893)[0m [415]	train-mlogloss:0.78266	validation-mlogloss:1.03019
[36m(train_xgb_ray_tune pid=1839894)[0m [397]	train-mlogloss:0.80712	validation-mlogloss:1.03213
[36m(train_xgb_ray_tune pid=1839894)[0m [398]	train-mlogloss:0.80639	validation-mlogloss:1.03191
[36m(train_xgb_ray_tune pid=1839880)[0m [499]	train-mlogloss:1.26928	validation-mlogloss:1.25853
[36m(train_xgb_ray_tune pid=1839880)[0m [500]	train-mlogloss:1.26866	validation-mlogloss:1.25791
[36m(train_xgb_ray_tune pid=1839849)[0m [395]	train-mlogloss:1.08690	validation-mlogloss:1.13291
[36m(train_xgb_ray_tune pid=1839849)[0m [396]	train-mlogloss:1.08638	validation-mlogloss:1.13256
[36m(train_xgb_ray_tune pid=1839846)[0m [486]	train-mlogloss:1.09923	validation-mlogloss:1.13248
[36m(train_xgb_ray_tune pid=1839846)[0m [487]	train-mlogloss:1.09901	validation-mlogloss:1.13231
[36m(train_xgb_ray_tune pid=1839846)[0m [488]	train-mlogloss:1.09875	validation-mlogloss:1.13210
[36m(train_xgb_ray_tune pid=1839893)[0m [416]	train-mlogloss:0.78210	validation-mlogloss:1.02996
[36m(train_xgb_ray_tune pid=1839893)[0m [417]	train-mlogloss:0.78163	validation-mlogloss:1.02999
[36m(train_xgb_ray_tune pid=1839894)[0m [399]	train-mlogloss:0.80574	validation-mlogloss:1.03188
[36m(train_xgb_ray_tune pid=1839846)[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_xgb_ray_tune pid=1839846)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=1839880)[0m [501]	train-mlogloss:1.26804	validation-mlogloss:1.25728
[36m(train_xgb_ray_tune pid=1839880)[0m [502]	train-mlogloss:1.26737	validation-mlogloss:1.25665
[36m(train_xgb_ray_tune pid=1839849)[0m [397]	train-mlogloss:1.08590	validation-mlogloss:1.13222
[36m(train_xgb_ray_tune pid=1839846)[0m [489]	train-mlogloss:1.09868	validation-mlogloss:1.13208
[36m(train_xgb_ray_tune pid=1839893)[0m [418]	train-mlogloss:0.78117	validation-mlogloss:1.02978
[36m(train_xgb_ray_tune pid=1839893)[0m [419]	train-mlogloss:0.78052	validation-mlogloss:1.02989
[36m(train_xgb_ray_tune pid=1839894)[0m [400]	train-mlogloss:0.80499	validation-mlogloss:1.03181
[36m(train_xgb_ray_tune pid=1839894)[0m [401]	train-mlogloss:0.80432	validation-mlogloss:1.03164
[36m(train_xgb_ray_tune pid=1839880)[0m [503]	train-mlogloss:1.26669	validation-mlogloss:1.25606
[36m(train_xgb_ray_tune pid=1839880)[0m [504]	train-mlogloss:1.26607	validation-mlogloss:1.25543
[36m(train_xgb_ray_tune pid=1839849)[0m [398]	train-mlogloss:1.08546	validation-mlogloss:1.13192
[36m(train_xgb_ray_tune pid=1839849)[0m [399]	train-mlogloss:1.08503	validation-mlogloss:1.13160
[36m(train_xgb_ray_tune pid=1839893)[0m [420]	train-mlogloss:0.77983	validation-mlogloss:1.02973
[36m(train_xgb_ray_tune pid=1839894)[0m [402]	train-mlogloss:0.80353	validation-mlogloss:1.03156
[36m(train_xgb_ray_tune pid=1839880)[0m [505]	train-mlogloss:1.26545	validation-mlogloss:1.25482
[36m(train_xgb_ray_tune pid=1839880)[0m [506]	train-mlogloss:1.26481	validation-mlogloss:1.25417
[36m(train_xgb_ray_tune pid=1839849)[0m [400]	train-mlogloss:1.08451	validation-mlogloss:1.13117
[36m(train_xgb_ray_tune pid=1839849)[0m [401]	train-mlogloss:1.08410	validation-mlogloss:1.13089
[36m(train_xgb_ray_tune pid=1839893)[0m [421]	train-mlogloss:0.77927	validation-mlogloss:1.02968
[36m(train_xgb_ray_tune pid=1839893)[0m [422]	train-mlogloss:0.77867	validation-mlogloss:1.02946
[36m(train_xgb_ray_tune pid=1839894)[0m [403]	train-mlogloss:0.80280	validation-mlogloss:1.03142
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[36m(train_xgb_ray_tune pid=1839880)[0m [507]	train-mlogloss:1.26415	validation-mlogloss:1.25348
[36m(train_xgb_ray_tune pid=1839880)[0m [508]	train-mlogloss:1.26352	validation-mlogloss:1.25286
[36m(train_xgb_ray_tune pid=1839849)[0m [402]	train-mlogloss:1.08363	validation-mlogloss:1.13062
[36m(train_xgb_ray_tune pid=1839849)[0m [403]	train-mlogloss:1.08319	validation-mlogloss:1.13032
[36m(train_xgb_ray_tune pid=1839893)[0m [423]	train-mlogloss:0.77809	validation-mlogloss:1.02942
[36m(train_xgb_ray_tune pid=1839893)[0m [424]	train-mlogloss:0.77760	validation-mlogloss:1.02918
[36m(train_xgb_ray_tune pid=1839894)[0m [405]	train-mlogloss:0.80133	validation-mlogloss:1.03097
[36m(train_xgb_ray_tune pid=1839894)[0m [406]	train-mlogloss:0.80051	validation-mlogloss:1.03097
[36m(train_xgb_ray_tune pid=1839880)[0m [509]	train-mlogloss:1.26292	validation-mlogloss:1.25227
[36m(train_xgb_ray_tune pid=1839880)[0m [510]	train-mlogloss:1.26228	validation-mlogloss:1.25169
[36m(train_xgb_ray_tune pid=1839849)[0m [404]	train-mlogloss:1.08275	validation-mlogloss:1.12994
[36m(train_xgb_ray_tune pid=1839849)[0m [405]	train-mlogloss:1.08223	validation-mlogloss:1.12951
[36m(train_xgb_ray_tune pid=1839893)[0m [425]	train-mlogloss:0.77711	validation-mlogloss:1.02901
[36m(train_xgb_ray_tune pid=1839893)[0m [426]	train-mlogloss:0.77651	validation-mlogloss:1.02889
[36m(train_xgb_ray_tune pid=1839894)[0m [407]	train-mlogloss:0.79974	validation-mlogloss:1.03081
[36m(train_xgb_ray_tune pid=1839880)[0m [511]	train-mlogloss:1.26169	validation-mlogloss:1.25111
[36m(train_xgb_ray_tune pid=1839880)[0m [512]	train-mlogloss:1.26105	validation-mlogloss:1.25048
[36m(train_xgb_ray_tune pid=1839849)[0m [406]	train-mlogloss:1.08172	validation-mlogloss:1.12911
[36m(train_xgb_ray_tune pid=1839893)[0m [427]	train-mlogloss:0.77587	validation-mlogloss:1.02884
[36m(train_xgb_ray_tune pid=1839894)[0m [408]	train-mlogloss:0.79902	validation-mlogloss:1.03075
[36m(train_xgb_ray_tune pid=1839894)[0m [409]	train-mlogloss:0.79829	validation-mlogloss:1.03072
[36m(train_xgb_ray_tune pid=1839880)[0m [513]	train-mlogloss:1.26040	validation-mlogloss:1.24985
[36m(train_xgb_ray_tune pid=1839880)[0m [514]	train-mlogloss:1.25976	validation-mlogloss:1.24917
[36m(train_xgb_ray_tune pid=1839849)[0m [407]	train-mlogloss:1.08121	validation-mlogloss:1.12874
[36m(train_xgb_ray_tune pid=1839849)[0m [408]	train-mlogloss:1.08080	validation-mlogloss:1.12844

Trial trial_0195f finished iteration 1 at 2025-10-25 19:44:01. Total running time: 1min 19s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             76.5991 │
│ time_total_s                 76.5991 │
│ training_iteration                 1 │
│ validation_accuracy          0.62015 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:44:01. Total running time: 1min 19s
[36m(train_xgb_ray_tune pid=1839893)[0m [428]	train-mlogloss:0.77530	validation-mlogloss:1.02866
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[36m(train_xgb_ray_tune pid=1839894)[0m [410]	train-mlogloss:0.79763	validation-mlogloss:1.03068
[36m(train_xgb_ray_tune pid=1839880)[0m [515]	train-mlogloss:1.25914	validation-mlogloss:1.24857
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[36m(train_xgb_ray_tune pid=1839849)[0m [409]	train-mlogloss:1.08036	validation-mlogloss:1.12814
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[36m(train_xgb_ray_tune pid=1839849)[0m [411]	train-mlogloss:1.07946	validation-mlogloss:1.12759
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[36m(train_xgb_ray_tune pid=1839894)[0m [451]	train-mlogloss:0.77007	validation-mlogloss:1.02575
[36m(train_xgb_ray_tune pid=1839849)[0m [455]	train-mlogloss:1.06378	validation-mlogloss:1.11603
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[36m(train_xgb_ray_tune pid=1839849)[0m [457]	train-mlogloss:1.06328	validation-mlogloss:1.11566
[36m(train_xgb_ray_tune pid=1839893)[0m [474]	train-mlogloss:0.75290	validation-mlogloss:1.02490
[36m(train_xgb_ray_tune pid=1839893)[0m [475]	train-mlogloss:0.75253	validation-mlogloss:1.02503
[36m(train_xgb_ray_tune pid=1839894)[0m [452]	train-mlogloss:0.76947	validation-mlogloss:1.02557
[36m(train_xgb_ray_tune pid=1839894)[0m [453]	train-mlogloss:0.76887	validation-mlogloss:1.02535
[36m(train_xgb_ray_tune pid=1839849)[0m [458]	train-mlogloss:1.06308	validation-mlogloss:1.11546
[36m(train_xgb_ray_tune pid=1839849)[0m [459]	train-mlogloss:1.06286	validation-mlogloss:1.11530

Trial trial_0195f finished iteration 1 at 2025-10-25 19:44:04. Total running time: 1min 22s
[36m(train_xgb_ray_tune pid=1839880)[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_xgb_ray_tune pid=1839880)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             78.8374 │
│ time_total_s                 78.8374 │
│ training_iteration                 1 │
│ validation_accuracy           0.5807 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:44:04. Total running time: 1min 22s
[36m(train_xgb_ray_tune pid=1839893)[0m [476]	train-mlogloss:0.75211	validation-mlogloss:1.02511
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[36m(train_xgb_ray_tune pid=1839849)[0m [460]	train-mlogloss:1.06255	validation-mlogloss:1.11503
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[36m(train_xgb_ray_tune pid=1839893)[0m [482]	train-mlogloss:0.74926	validation-mlogloss:1.02534
[36m(train_xgb_ray_tune pid=1839893)[0m [483]	train-mlogloss:0.74884	validation-mlogloss:1.02507
[36m(train_xgb_ray_tune pid=1839894)[0m [459]	train-mlogloss:0.76506	validation-mlogloss:1.02484
[36m(train_xgb_ray_tune pid=1839894)[0m [460]	train-mlogloss:0.76439	validation-mlogloss:1.02476
[36m(train_xgb_ray_tune pid=1839849)[0m [466]	train-mlogloss:1.06099	validation-mlogloss:1.11381
[36m(train_xgb_ray_tune pid=1839849)[0m [467]	train-mlogloss:1.06065	validation-mlogloss:1.11358
[36m(train_xgb_ray_tune pid=1839849)[0m [468]	train-mlogloss:1.06050	validation-mlogloss:1.11345
[36m(train_xgb_ray_tune pid=1839893)[0m [484]	train-mlogloss:0.74846	validation-mlogloss:1.02504
[36m(train_xgb_ray_tune pid=1839893)[0m [485]	train-mlogloss:0.74800	validation-mlogloss:1.02519
[36m(train_xgb_ray_tune pid=1839894)[0m [461]	train-mlogloss:0.76376	validation-mlogloss:1.02475
[36m(train_xgb_ray_tune pid=1839894)[0m [462]	train-mlogloss:0.76312	validation-mlogloss:1.02481
[36m(train_xgb_ray_tune pid=1839849)[0m [469]	train-mlogloss:1.06022	validation-mlogloss:1.11323
[36m(train_xgb_ray_tune pid=1839849)[0m [470]	train-mlogloss:1.05989	validation-mlogloss:1.11307
[36m(train_xgb_ray_tune pid=1839893)[0m [486]	train-mlogloss:0.74761	validation-mlogloss:1.02525
[36m(train_xgb_ray_tune pid=1839893)[0m [487]	train-mlogloss:0.74726	validation-mlogloss:1.02531
[36m(train_xgb_ray_tune pid=1839894)[0m [463]	train-mlogloss:0.76253	validation-mlogloss:1.02468
[36m(train_xgb_ray_tune pid=1839849)[0m [471]	train-mlogloss:1.05963	validation-mlogloss:1.11289
[36m(train_xgb_ray_tune pid=1839849)[0m [472]	train-mlogloss:1.05933	validation-mlogloss:1.11265
[36m(train_xgb_ray_tune pid=1839894)[0m [464]	train-mlogloss:0.76190	validation-mlogloss:1.02456
[36m(train_xgb_ray_tune pid=1839893)[0m [488]	train-mlogloss:0.74693	validation-mlogloss:1.02555
[36m(train_xgb_ray_tune pid=1839893)[0m [489]	train-mlogloss:0.74645	validation-mlogloss:1.02553
[36m(train_xgb_ray_tune pid=1839894)[0m [465]	train-mlogloss:0.76128	validation-mlogloss:1.02447
[36m(train_xgb_ray_tune pid=1839849)[0m [473]	train-mlogloss:1.05912	validation-mlogloss:1.11251
[36m(train_xgb_ray_tune pid=1839849)[0m [474]	train-mlogloss:1.05886	validation-mlogloss:1.11231
[36m(train_xgb_ray_tune pid=1839849)[0m [475]	train-mlogloss:1.05860	validation-mlogloss:1.11206
[36m(train_xgb_ray_tune pid=1839893)[0m [490]	train-mlogloss:0.74615	validation-mlogloss:1.02562
[36m(train_xgb_ray_tune pid=1839893)[0m [491]	train-mlogloss:0.74580	validation-mlogloss:1.02560
[36m(train_xgb_ray_tune pid=1839894)[0m [466]	train-mlogloss:0.76070	validation-mlogloss:1.02432
[36m(train_xgb_ray_tune pid=1839894)[0m [467]	train-mlogloss:0.76013	validation-mlogloss:1.02410
[36m(train_xgb_ray_tune pid=1839849)[0m [476]	train-mlogloss:1.05840	validation-mlogloss:1.11189
[36m(train_xgb_ray_tune pid=1839849)[0m [477]	train-mlogloss:1.05814	validation-mlogloss:1.11172
[36m(train_xgb_ray_tune pid=1839893)[0m [492]	train-mlogloss:0.74530	validation-mlogloss:1.02565
[36m(train_xgb_ray_tune pid=1839893)[0m [493]	train-mlogloss:0.74495	validation-mlogloss:1.02563
[36m(train_xgb_ray_tune pid=1839894)[0m [468]	train-mlogloss:0.75952	validation-mlogloss:1.02380
[36m(train_xgb_ray_tune pid=1839894)[0m [469]	train-mlogloss:0.75884	validation-mlogloss:1.02367
[36m(train_xgb_ray_tune pid=1839849)[0m [478]	train-mlogloss:1.05792	validation-mlogloss:1.11155
[36m(train_xgb_ray_tune pid=1839849)[0m [479]	train-mlogloss:1.05770	validation-mlogloss:1.11142
[36m(train_xgb_ray_tune pid=1839893)[0m [494]	train-mlogloss:0.74450	validation-mlogloss:1.02550
[36m(train_xgb_ray_tune pid=1839894)[0m [470]	train-mlogloss:0.75824	validation-mlogloss:1.02368
[36m(train_xgb_ray_tune pid=1839894)[0m [471]	train-mlogloss:0.75763	validation-mlogloss:1.02358
[36m(train_xgb_ray_tune pid=1839849)[0m [480]	train-mlogloss:1.05747	validation-mlogloss:1.11120
[36m(train_xgb_ray_tune pid=1839849)[0m [481]	train-mlogloss:1.05723	validation-mlogloss:1.11099
[36m(train_xgb_ray_tune pid=1839849)[0m [482]	train-mlogloss:1.05698	validation-mlogloss:1.11080
[36m(train_xgb_ray_tune pid=1839894)[0m [472]	train-mlogloss:0.75698	validation-mlogloss:1.02346
[36m(train_xgb_ray_tune pid=1839894)[0m [473]	train-mlogloss:0.75638	validation-mlogloss:1.02338
[36m(train_xgb_ray_tune pid=1839849)[0m [483]	train-mlogloss:1.05687	validation-mlogloss:1.11074
[36m(train_xgb_ray_tune pid=1839849)[0m [484]	train-mlogloss:1.05661	validation-mlogloss:1.11058
[36m(train_xgb_ray_tune pid=1839894)[0m [474]	train-mlogloss:0.75576	validation-mlogloss:1.02367
[36m(train_xgb_ray_tune pid=1839894)[0m [475]	train-mlogloss:0.75514	validation-mlogloss:1.02365
[36m(train_xgb_ray_tune pid=1839849)[0m [485]	train-mlogloss:1.05637	validation-mlogloss:1.11041
[36m(train_xgb_ray_tune pid=1839849)[0m [486]	train-mlogloss:1.05616	validation-mlogloss:1.11023
[36m(train_xgb_ray_tune pid=1839849)[0m [487]	train-mlogloss:1.05600	validation-mlogloss:1.11010
[36m(train_xgb_ray_tune pid=1839894)[0m [476]	train-mlogloss:0.75457	validation-mlogloss:1.02339
[36m(train_xgb_ray_tune pid=1839894)[0m [477]	train-mlogloss:0.75391	validation-mlogloss:1.02328
[36m(train_xgb_ray_tune pid=1839849)[0m [488]	train-mlogloss:1.05582	validation-mlogloss:1.11000
[36m(train_xgb_ray_tune pid=1839849)[0m [489]	train-mlogloss:1.05566	validation-mlogloss:1.10994
[36m(train_xgb_ray_tune pid=1839894)[0m [478]	train-mlogloss:0.75332	validation-mlogloss:1.02334
[36m(train_xgb_ray_tune pid=1839894)[0m [479]	train-mlogloss:0.75266	validation-mlogloss:1.02328
2025-10-25 19:44:07,779	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_XGB_acc_gyr_17_classes/XGBoost_hyperparameters_tuning' in 0.0117s.
[36m(train_xgb_ray_tune pid=1839849)[0m [490]	train-mlogloss:1.05537	validation-mlogloss:1.10976
[36m(train_xgb_ray_tune pid=1839849)[0m [491]	train-mlogloss:1.05526	validation-mlogloss:1.10968
[36m(train_xgb_ray_tune pid=1839849)[0m [492]	train-mlogloss:1.05497	validation-mlogloss:1.10948
[36m(train_xgb_ray_tune pid=1839894)[0m [480]	train-mlogloss:0.75206	validation-mlogloss:1.02313
[36m(train_xgb_ray_tune pid=1839849)[0m [493]	train-mlogloss:1.05476	validation-mlogloss:1.10931
[36m(train_xgb_ray_tune pid=1839849)[0m [494]	train-mlogloss:1.05454	validation-mlogloss:1.10913

Trial trial_0195f finished iteration 1 at 2025-10-25 19:44:06. Total running time: 1min 24s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             80.4857 │
│ time_total_s                 80.4857 │
│ training_iteration                 1 │
│ validation_accuracy          0.61294 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:44:06. Total running time: 1min 24s
[36m(train_xgb_ray_tune pid=1839849)[0m [495]	train-mlogloss:1.05443	validation-mlogloss:1.10907
[36m(train_xgb_ray_tune pid=1839849)[0m [496]	train-mlogloss:1.05426	validation-mlogloss:1.10900
[36m(train_xgb_ray_tune pid=1839849)[0m [497]	train-mlogloss:1.05401	validation-mlogloss:1.10880
[36m(train_xgb_ray_tune pid=1839849)[0m [498]	train-mlogloss:1.05374	validation-mlogloss:1.10860
[36m(train_xgb_ray_tune pid=1839849)[0m [499]	train-mlogloss:1.05356	validation-mlogloss:1.10846
[36m(train_xgb_ray_tune pid=1839849)[0m [500]	train-mlogloss:1.05332	validation-mlogloss:1.10827
[36m(train_xgb_ray_tune pid=1839849)[0m [501]	train-mlogloss:1.05309	validation-mlogloss:1.10814
[36m(train_xgb_ray_tune pid=1839849)[0m [502]	train-mlogloss:1.05293	validation-mlogloss:1.10800
[36m(train_xgb_ray_tune pid=1839849)[0m [503]	train-mlogloss:1.05269	validation-mlogloss:1.10781
[36m(train_xgb_ray_tune pid=1839849)[0m [504]	train-mlogloss:1.05245	validation-mlogloss:1.10764
[36m(train_xgb_ray_tune pid=1839849)[0m [505]	train-mlogloss:1.05240	validation-mlogloss:1.10761
[36m(train_xgb_ray_tune pid=1839849)[0m [506]	train-mlogloss:1.05226	validation-mlogloss:1.10750
[36m(train_xgb_ray_tune pid=1839849)[0m [507]	train-mlogloss:1.05212	validation-mlogloss:1.10740

Trial trial_0195f finished iteration 1 at 2025-10-25 19:44:06. Total running time: 1min 24s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             81.0483 │
│ time_total_s                 81.0483 │
│ training_iteration                 1 │
│ validation_accuracy          0.61548 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:44:06. Total running time: 1min 24s
[36m(train_xgb_ray_tune pid=1839849)[0m [508]	train-mlogloss:1.05191	validation-mlogloss:1.10726
[36m(train_xgb_ray_tune pid=1839849)[0m [509]	train-mlogloss:1.05174	validation-mlogloss:1.10708
[36m(train_xgb_ray_tune pid=1839849)[0m [510]	train-mlogloss:1.05157	validation-mlogloss:1.10686
[36m(train_xgb_ray_tune pid=1839849)[0m [511]	train-mlogloss:1.05150	validation-mlogloss:1.10682
[36m(train_xgb_ray_tune pid=1839849)[0m [512]	train-mlogloss:1.05135	validation-mlogloss:1.10671
[36m(train_xgb_ray_tune pid=1839849)[0m [513]	train-mlogloss:1.05119	validation-mlogloss:1.10652
[36m(train_xgb_ray_tune pid=1839849)[0m [514]	train-mlogloss:1.05101	validation-mlogloss:1.10643
[36m(train_xgb_ray_tune pid=1839849)[0m [515]	train-mlogloss:1.05077	validation-mlogloss:1.10624
[36m(train_xgb_ray_tune pid=1839849)[0m [516]	train-mlogloss:1.05059	validation-mlogloss:1.10615
[36m(train_xgb_ray_tune pid=1839849)[0m [517]	train-mlogloss:1.05046	validation-mlogloss:1.10605
[36m(train_xgb_ray_tune pid=1839849)[0m [518]	train-mlogloss:1.05032	validation-mlogloss:1.10599
[36m(train_xgb_ray_tune pid=1839849)[0m [519]	train-mlogloss:1.05014	validation-mlogloss:1.10589
[36m(train_xgb_ray_tune pid=1839849)[0m [520]	train-mlogloss:1.04999	validation-mlogloss:1.10577
[36m(train_xgb_ray_tune pid=1839849)[0m [521]	train-mlogloss:1.04977	validation-mlogloss:1.10565
[36m(train_xgb_ray_tune pid=1839849)[0m [522]	train-mlogloss:1.04962	validation-mlogloss:1.10559
[36m(train_xgb_ray_tune pid=1839849)[0m [523]	train-mlogloss:1.04944	validation-mlogloss:1.10545
[36m(train_xgb_ray_tune pid=1839849)[0m [524]	train-mlogloss:1.04932	validation-mlogloss:1.10535
[36m(train_xgb_ray_tune pid=1839849)[0m [525]	train-mlogloss:1.04918	validation-mlogloss:1.10522
[36m(train_xgb_ray_tune pid=1839849)[0m [526]	train-mlogloss:1.04900	validation-mlogloss:1.10512

Trial trial_0195f finished iteration 1 at 2025-10-25 19:44:07. Total running time: 1min 26s
╭──────────────────────────────────────╮
│ Trial trial_0195f result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             81.9942 │
│ time_total_s                 81.9942 │
│ training_iteration                 1 │
│ validation_accuracy           0.6123 │
╰──────────────────────────────────────╯

Trial trial_0195f completed after 1 iterations at 2025-10-25 19:44:07. Total running time: 1min 26s

Trial status: 20 TERMINATED
Current time: 2025-10-25 19:44:07. Total running time: 1min 26s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Trial name     status         num_boost_round     max_depth     learning_rate     subsample     colsample_bytree     gamma     min_child_weight     reg_alpha     reg_lambda     random_state     iter     total time (s)     validation_accuracy │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│ trial_0195f    TERMINATED                 490             3        0.0180686       0.386165             0.401018   1.91019                   26     28.4719          1.13302             2009        1            76.599                 0.620148 │
│ trial_0195f    TERMINATED                 216             3        0.0417666       0.538654             0.569217   3.2315                    21      1.13863       168.641               3282        1            58.6451                0.616755 │
│ trial_0195f    TERMINATED                 472             2        0.0362057       0.691935             0.596683   3.41711                   50      0.229961       27.507               6718        1            62.1233                0.611665 │
│ trial_0195f    TERMINATED                 267             3        0.0379462       0.678655             0.339382   8.27026                   46      0.155793       16.3947              6848        1            62.0752                0.619936 │
│ trial_0195f    TERMINATED                 406             2        0.0188112       0.475566             0.620105   8.00317                   41     17.1877          1.8251              5173        1            65.0449                0.589608 │
│ trial_0195f    TERMINATED                 450             2        0.0382595       0.603751             0.569897   3.0477                    54      0.103932        7.40936             1023        1            68.2561                0.618028 │
│ trial_0195f    TERMINATED                 315             3        0.0439828       0.390514             0.352631   6.90055                   11      1.81695        43.3285              2402        1            59.7607                0.612513 │
│ trial_0195f    TERMINATED                 331             2        0.0317944       0.313674             0.67536    4.41856                   53      5.32658        48.6357              7554        1            59.2976                0.591516 │
│ trial_0195f    TERMINATED                 195             2        0.00514406      0.580216             0.441027   5.8561                    50     57.2455         85.8182              8258        1            38.1458                0.507317 │
│ trial_0195f    TERMINATED                 414             2        0.0202599       0.695361             0.509306   1.56986                   58     93.6182          2.27817             1172        1            65.5369                0.572428 │
│ trial_0195f    TERMINATED                 111             3        0.0257285       0.332271             0.433404   9.4826                    26      0.114293       56.1492              7789        1            30.7858                0.595122 │
│ trial_0195f    TERMINATED                 253             2        0.0231462       0.46082              0.450991   5.86543                   12      0.55736         6.01178             7339        1            49.3951                0.593001 │
│ trial_0195f    TERMINATED                 489             2        0.0257862       0.674977             0.554238   4.86723                   23     15.2685         12.2752              3488        1            68.0264                0.602969 │
│ trial_0195f    TERMINATED                 210             3        0.0277019       0.451699             0.324143   8.75302                   48      3.3268          3.18417             6615        1            37.5941                0.616331 │
│ trial_0195f    TERMINATED                 502             3        0.0343541       0.330756             0.302921   8.9828                    17      0.114231        1.95791             1454        1            70.9648                0.615695 │
│ trial_0195f    TERMINATED                 127             2        0.0211436       0.673953             0.614406   5.99959                   45     55.8846          3.0865              7679        1            36.624                 0.545281 │
│ trial_0195f    TERMINATED                 512             3        0.0413879       0.513759             0.697578   2.60018                   55      0.548801       90.1764               512        1            80.4857                0.612937 │
│ trial_0195f    TERMINATED                 481             3        0.034166        0.68654              0.613953   2.28701                   49      0.108157      119.964               8159        1            81.0483                0.615483 │
│ trial_0195f    TERMINATED                 558             2        0.00744142      0.549635             0.625081   8.77248                   41     14.3318          1.34804             8995        1            78.8374                0.5807   │
│ trial_0195f    TERMINATED                 527             3        0.0135209       0.653459             0.597106   6.02627                   57     21.8788          1.27441             3227        1            81.9942                0.612301 │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros XGBoost: {'num_boost_round': 490, 'max_depth': 3, 'learning_rate': 0.018068576914383263, 'subsample': 0.38616542692549116, 'colsample_bytree': 0.4010176383068691, 'gamma': 1.9101893983329175, 'min_child_weight': 26, 'reg_alpha': 28.471877540487743, 'reg_lambda': 1.1330210260753901, 'random_state': 2009}
[0]	train-mlogloss:2.67618	validation-mlogloss:2.67746
[1]	train-mlogloss:2.64466	validation-mlogloss:2.64643
[2]	train-mlogloss:2.61287	validation-mlogloss:2.61573
[3]	train-mlogloss:2.58268	validation-mlogloss:2.58648
[4]	train-mlogloss:2.55304	validation-mlogloss:2.55793
[5]	train-mlogloss:2.52597	validation-mlogloss:2.53240
[6]	train-mlogloss:2.49889	validation-mlogloss:2.50677
[7]	train-mlogloss:2.47363	validation-mlogloss:2.48233
[8]	train-mlogloss:2.44880	validation-mlogloss:2.45768
[9]	train-mlogloss:2.42495	validation-mlogloss:2.43468
[10]	train-mlogloss:2.40201	validation-mlogloss:2.41129
[11]	train-mlogloss:2.38014	validation-mlogloss:2.39011
[12]	train-mlogloss:2.35918	validation-mlogloss:2.36925
[13]	train-mlogloss:2.33848	validation-mlogloss:2.34951
[14]	train-mlogloss:2.31747	validation-mlogloss:2.32848
[15]	train-mlogloss:2.29821	validation-mlogloss:2.30935
[16]	train-mlogloss:2.27967	validation-mlogloss:2.29025
[17]	train-mlogloss:2.26162	validation-mlogloss:2.27231
[18]	train-mlogloss:2.24347	validation-mlogloss:2.25418
[19]	train-mlogloss:2.22577	validation-mlogloss:2.23665
[20]	train-mlogloss:2.20806	validation-mlogloss:2.21908
[21]	train-mlogloss:2.19103	validation-mlogloss:2.20262
[22]	train-mlogloss:2.17507	validation-mlogloss:2.18683
[23]	train-mlogloss:2.15879	validation-mlogloss:2.17044
[24]	train-mlogloss:2.14342	validation-mlogloss:2.15550
[25]	train-mlogloss:2.12818	validation-mlogloss:2.14062
[26]	train-mlogloss:2.11312	validation-mlogloss:2.12627
[27]	train-mlogloss:2.09872	validation-mlogloss:2.11167
[28]	train-mlogloss:2.08490	validation-mlogloss:2.09788
[29]	train-mlogloss:2.07160	validation-mlogloss:2.08444
[30]	train-mlogloss:2.05827	validation-mlogloss:2.07060
[31]	train-mlogloss:2.04458	validation-mlogloss:2.05722
[32]	train-mlogloss:2.03083	validation-mlogloss:2.04348
[33]	train-mlogloss:2.01782	validation-mlogloss:2.03069
[34]	train-mlogloss:2.00553	validation-mlogloss:2.01901
[35]	train-mlogloss:1.99333	validation-mlogloss:2.00688
[36]	train-mlogloss:1.98115	validation-mlogloss:1.99480
[37]	train-mlogloss:1.96932	validation-mlogloss:1.98308
[38]	train-mlogloss:1.95748	validation-mlogloss:1.97097
[39]	train-mlogloss:1.94591	validation-mlogloss:1.95931
[40]	train-mlogloss:1.93472	validation-mlogloss:1.94783
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[489]	train-mlogloss:1.09825	validation-mlogloss:1.13586
Saved model to disk.
[36m(train_xgb_ray_tune pid=1839849)[0m /home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/train/_internal/session.py:772: RayDeprecationWarning: `ray.train.report` should be switched to `ray.tune.report` when running in a function passed to Ray Tune. This will be an error in the future. See this issue for more context: https://github.com/ray-project/ray/issues/49454[32m [repeated 3x across cluster][0m
[36m(train_xgb_ray_tune pid=1839849)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
2025-10-25 19:48:45.953077: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-25 19:48:45.965938: 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 00:00:1761414525.979987 1848170 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761414525.984479 1848170 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761414525.995578 1848170 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414525.995606 1848170 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414525.995609 1848170 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414525.995612 1848170 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-25 19:48:45.999114: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[425]	train-mlogloss:1.10911	validation-mlogloss:1.14110
[426]	train-mlogloss:1.10882	validation-mlogloss:1.14089
[427]	train-mlogloss:1.10872	validation-mlogloss:1.14082
[428]	train-mlogloss:1.10835	validation-mlogloss:1.14049
[429]	train-mlogloss:1.10817	validation-mlogloss:1.14035
[430]	train-mlogloss:1.10790	validation-mlogloss:1.14012
[431]	train-mlogloss:1.10766	validation-mlogloss:1.13994
[432]	train-mlogloss:1.10732	validation-mlogloss:1.13972
[433]	train-mlogloss:1.10712	validation-mlogloss:1.13950
[434]	train-mlogloss:1.10681	validation-mlogloss:1.13918
[435]	train-mlogloss:1.10668	validation-mlogloss:1.13908
[436]	train-mlogloss:1.10650	validation-mlogloss:1.13898
[437]	train-mlogloss:1.10635	validation-mlogloss:1.13884
[438]	train-mlogloss:1.10619	validation-mlogloss:1.13869
[439]	train-mlogloss:1.10600	validation-mlogloss:1.13859
[440]	train-mlogloss:1.10575	validation-mlogloss:1.13842
[441]	train-mlogloss:1.10551	validation-mlogloss:1.13826
[442]	train-mlogloss:1.10534	validation-mlogloss:1.13816
[443]	train-mlogloss:1.10519	validation-mlogloss:1.13806
[444]	train-mlogloss:1.10504	validation-mlogloss:1.13798
[445]	train-mlogloss:1.10480	validation-mlogloss:1.13778
[446]	train-mlogloss:1.10476	validation-mlogloss:1.13775
[447]	train-mlogloss:1.10464	validation-mlogloss:1.13764
[448]	train-mlogloss:1.10453	validation-mlogloss:1.13753
[449]	train-mlogloss:1.10440	validation-mlogloss:1.13751
[450]	train-mlogloss:1.10422	validation-mlogloss:1.13732
[451]	train-mlogloss:1.10408	validation-mlogloss:1.13719
[452]	train-mlogloss:1.10397	validation-mlogloss:1.13708
[453]	train-mlogloss:1.10393	validation-mlogloss:1.13705
[454]	train-mlogloss:1.10378	validation-mlogloss:1.13696
[455]	train-mlogloss:1.10369	validation-mlogloss:1.13688
[456]	train-mlogloss:1.10352	validation-mlogloss:1.13674
[457]	train-mlogloss:1.10340	validation-mlogloss:1.13665
[458]	train-mlogloss:1.10321	validation-mlogloss:1.13646
[459]	train-mlogloss:1.10306	validation-mlogloss:1.13641
[460]	train-mlogloss:1.10296	validation-mlogloss:1.13635
[461]	train-mlogloss:1.10272	validation-mlogloss:1.13621
[462]	train-mlogloss:1.10252	validation-mlogloss:1.13598
[463]	train-mlogloss:1.10233	validation-mlogloss:1.13588
[464]	train-mlogloss:1.10224	validation-mlogloss:1.13582
[465]	train-mlogloss:1.10208	validation-mlogloss:1.13569
[466]	train-mlogloss:1.10196	validation-mlogloss:1.13561
[467]	train-mlogloss:1.10185	validation-mlogloss:1.13552
[468]	train-mlogloss:1.10162	validation-mlogloss:1.13542
[469]	train-mlogloss:1.10149	validation-mlogloss:1.13529
[470]	train-mlogloss:1.10134	validation-mlogloss:1.13509
[471]	train-mlogloss:1.10118	validation-mlogloss:1.13500
[472]	train-mlogloss:1.10100	validation-mlogloss:1.13486
[473]	train-mlogloss:1.10088	validation-mlogloss:1.13476
[474]	train-mlogloss:1.10077	validation-mlogloss:1.13466
[475]	train-mlogloss:1.10072	validation-mlogloss:1.13464
[476]	train-mlogloss:1.10065	validation-mlogloss:1.13458
[477]	train-mlogloss:1.10049	validation-mlogloss:1.13449
[478]	train-mlogloss:1.10041	validation-mlogloss:1.13442
[479]	train-mlogloss:1.10030	validation-mlogloss:1.13442
[480]	train-mlogloss:1.10008	validation-mlogloss:1.13421
[481]	train-mlogloss:1.09994	validation-mlogloss:1.13404
[482]	train-mlogloss:1.09987	validation-mlogloss:1.13398
[483]	train-mlogloss:1.09980	validation-mlogloss:1.13391
[484]	train-mlogloss:1.09969	validation-mlogloss:1.13388
[485]	train-mlogloss:1.09965	validation-mlogloss:1.13388
[486]	train-mlogloss:1.09950	validation-mlogloss:1.13373
[487]	train-mlogloss:1.09942	validation-mlogloss:1.13370
[488]	train-mlogloss:1.09936	validation-mlogloss:1.13363
[489]	train-mlogloss:1.09931	validation-mlogloss:1.13355
Saved model to disk.
2025-10-25 19:53:41.769644: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-25 19:53:41.783052: 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 00:00:1761414821.797464 1853989 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761414821.802152 1853989 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761414821.813682 1853989 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414821.813716 1853989 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414821.813720 1853989 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761414821.813723 1853989 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-25 19:53:41.817299: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67441	validation-mlogloss:2.67534
[1]	train-mlogloss:2.64317	validation-mlogloss:2.64517
[2]	train-mlogloss:2.61305	validation-mlogloss:2.61737
[3]	train-mlogloss:2.58540	validation-mlogloss:2.59090
[4]	train-mlogloss:2.55750	validation-mlogloss:2.56356
[5]	train-mlogloss:2.53045	validation-mlogloss:2.53705
[6]	train-mlogloss:2.50445	validation-mlogloss:2.51110
[7]	train-mlogloss:2.47848	validation-mlogloss:2.48565
[8]	train-mlogloss:2.45278	validation-mlogloss:2.45969
[9]	train-mlogloss:2.42869	validation-mlogloss:2.43566
[10]	train-mlogloss:2.40424	validation-mlogloss:2.41219
[11]	train-mlogloss:2.38156	validation-mlogloss:2.39023
[12]	train-mlogloss:2.36050	validation-mlogloss:2.37027
[13]	train-mlogloss:2.33987	validation-mlogloss:2.35002
[14]	train-mlogloss:2.32051	validation-mlogloss:2.33094
[15]	train-mlogloss:2.30033	validation-mlogloss:2.31112
[16]	train-mlogloss:2.28045	validation-mlogloss:2.29121
[17]	train-mlogloss:2.26211	validation-mlogloss:2.27282
[18]	train-mlogloss:2.24445	validation-mlogloss:2.25613
[19]	train-mlogloss:2.22775	validation-mlogloss:2.23965
[20]	train-mlogloss:2.21087	validation-mlogloss:2.22260
[21]	train-mlogloss:2.19439	validation-mlogloss:2.20643
[22]	train-mlogloss:2.17767	validation-mlogloss:2.18986
[23]	train-mlogloss:2.16216	validation-mlogloss:2.17420
[24]	train-mlogloss:2.14607	validation-mlogloss:2.15848
[25]	train-mlogloss:2.13048	validation-mlogloss:2.14322
[26]	train-mlogloss:2.11623	validation-mlogloss:2.12892
[27]	train-mlogloss:2.10152	validation-mlogloss:2.11411
[28]	train-mlogloss:2.08789	validation-mlogloss:2.10064
[29]	train-mlogloss:2.07348	validation-mlogloss:2.08617
[30]	train-mlogloss:2.05968	validation-mlogloss:2.07243
[31]	train-mlogloss:2.04700	validation-mlogloss:2.05983
[32]	train-mlogloss:2.03375	validation-mlogloss:2.04699
[33]	train-mlogloss:2.02098	validation-mlogloss:2.03449
[34]	train-mlogloss:2.00864	validation-mlogloss:2.02215
[35]	train-mlogloss:1.99684	validation-mlogloss:2.01033
[36]	train-mlogloss:1.98542	validation-mlogloss:1.99950
[37]	train-mlogloss:1.97413	validation-mlogloss:1.98853
[38]	train-mlogloss:1.96238	validation-mlogloss:1.97688
[39]	train-mlogloss:1.95126	validation-mlogloss:1.96614
[40]	train-mlogloss:1.93991	validation-mlogloss:1.95499
[41]	train-mlogloss:1.92909	validation-mlogloss:1.94405
[42]	train-mlogloss:1.91871	validation-mlogloss:1.93373
[43]	train-mlogloss:1.90792	validation-mlogloss:1.92280
[44]	train-mlogloss:1.89771	validation-mlogloss:1.91216
[45]	train-mlogloss:1.88765	validation-mlogloss:1.90182
[46]	train-mlogloss:1.87764	validation-mlogloss:1.89187
[47]	train-mlogloss:1.86777	validation-mlogloss:1.88203
[48]	train-mlogloss:1.85808	validation-mlogloss:1.87182
[49]	train-mlogloss:1.84842	validation-mlogloss:1.86259
[50]	train-mlogloss:1.83904	validation-mlogloss:1.85311
[51]	train-mlogloss:1.82976	validation-mlogloss:1.84367
[52]	train-mlogloss:1.82069	validation-mlogloss:1.83483
[53]	train-mlogloss:1.81208	validation-mlogloss:1.82626
[54]	train-mlogloss:1.80336	validation-mlogloss:1.81717
[55]	train-mlogloss:1.79498	validation-mlogloss:1.80880
[56]	train-mlogloss:1.78656	validation-mlogloss:1.80030
[57]	train-mlogloss:1.77839	validation-mlogloss:1.79201
[58]	train-mlogloss:1.77039	validation-mlogloss:1.78382
[59]	train-mlogloss:1.76231	validation-mlogloss:1.77565
[60]	train-mlogloss:1.75468	validation-mlogloss:1.76765
[61]	train-mlogloss:1.74745	validation-mlogloss:1.76054
[62]	train-mlogloss:1.74010	validation-mlogloss:1.75304
[63]	train-mlogloss:1.73251	validation-mlogloss:1.74540
[64]	train-mlogloss:1.72557	validation-mlogloss:1.73840
[65]	train-mlogloss:1.71830	validation-mlogloss:1.73119
[66]	train-mlogloss:1.71136	validation-mlogloss:1.72435
[67]	train-mlogloss:1.70471	validation-mlogloss:1.71771
[68]	train-mlogloss:1.69797	validation-mlogloss:1.71086
[69]	train-mlogloss:1.69097	validation-mlogloss:1.70381
[70]	train-mlogloss:1.68444	validation-mlogloss:1.69718
[71]	train-mlogloss:1.67787	validation-mlogloss:1.69006
[72]	train-mlogloss:1.67122	validation-mlogloss:1.68310
[73]	train-mlogloss:1.66459	validation-mlogloss:1.67641
[74]	train-mlogloss:1.65813	validation-mlogloss:1.66984
[75]	train-mlogloss:1.65173	validation-mlogloss:1.66338
[76]	train-mlogloss:1.64571	validation-mlogloss:1.65764
[77]	train-mlogloss:1.63978	validation-mlogloss:1.65163
[78]	train-mlogloss:1.63406	validation-mlogloss:1.64601
[79]	train-mlogloss:1.62811	validation-mlogloss:1.63995
[80]	train-mlogloss:1.62206	validation-mlogloss:1.63395
[81]	train-mlogloss:1.61645	validation-mlogloss:1.62835
[82]	train-mlogloss:1.61096	validation-mlogloss:1.62266
[83]	train-mlogloss:1.60524	validation-mlogloss:1.61692
[84]	train-mlogloss:1.59971	validation-mlogloss:1.61130
[85]	train-mlogloss:1.59429	validation-mlogloss:1.60565
[86]	train-mlogloss:1.58868	validation-mlogloss:1.60003
[87]	train-mlogloss:1.58315	validation-mlogloss:1.59441
[88]	train-mlogloss:1.57793	validation-mlogloss:1.58890
[89]	train-mlogloss:1.57276	validation-mlogloss:1.58380
[90]	train-mlogloss:1.56778	validation-mlogloss:1.57892
[91]	train-mlogloss:1.56271	validation-mlogloss:1.57366
[92]	train-mlogloss:1.55768	validation-mlogloss:1.56835
[93]	train-mlogloss:1.55282	validation-mlogloss:1.56351
[94]	train-mlogloss:1.54806	validation-mlogloss:1.55875
[95]	train-mlogloss:1.54340	validation-mlogloss:1.55396
[96]	train-mlogloss:1.53896	validation-mlogloss:1.54962
[97]	train-mlogloss:1.53453	validation-mlogloss:1.54509
[98]	train-mlogloss:1.52969	validation-mlogloss:1.54018
[99]	train-mlogloss:1.52524	validation-mlogloss:1.53581
[100]	train-mlogloss:1.52081	validation-mlogloss:1.53124
[101]	train-mlogloss:1.51644	validation-mlogloss:1.52686
[102]	train-mlogloss:1.51212	validation-mlogloss:1.52217
[103]	train-mlogloss:1.50776	validation-mlogloss:1.51783
[104]	train-mlogloss:1.50333	validation-mlogloss:1.51357
[105]	train-mlogloss:1.49904	validation-mlogloss:1.50914
[106]	train-mlogloss:1.49495	validation-mlogloss:1.50516
[107]	train-mlogloss:1.49096	validation-mlogloss:1.50095
[108]	train-mlogloss:1.48709	validation-mlogloss:1.49701
[109]	train-mlogloss:1.48304	validation-mlogloss:1.49273
[110]	train-mlogloss:1.47918	validation-mlogloss:1.48881
[111]	train-mlogloss:1.47542	validation-mlogloss:1.48496
[112]	train-mlogloss:1.47175	validation-mlogloss:1.48132
[113]	train-mlogloss:1.46809	validation-mlogloss:1.47773
[114]	train-mlogloss:1.46428	validation-mlogloss:1.47382
[115]	train-mlogloss:1.46058	validation-mlogloss:1.47010
[116]	train-mlogloss:1.45697	validation-mlogloss:1.46634
[117]	train-mlogloss:1.45354	validation-mlogloss:1.46292
[118]	train-mlogloss:1.45003	validation-mlogloss:1.45944
[119]	train-mlogloss:1.44658	validation-mlogloss:1.45600
[120]	train-mlogloss:1.44328	validation-mlogloss:1.45278
[121]	train-mlogloss:1.43972	validation-mlogloss:1.44909
[122]	train-mlogloss:1.43617	validation-mlogloss:1.44579
[123]	train-mlogloss:1.43287	validation-mlogloss:1.44239
[124]	train-mlogloss:1.42954	validation-mlogloss:1.43916
[125]	train-mlogloss:1.42617	validation-mlogloss:1.43581
[126]	train-mlogloss:1.42269	validation-mlogloss:1.43233
[127]	train-mlogloss:1.41949	validation-mlogloss:1.42931
[128]	train-mlogloss:1.41643	validation-mlogloss:1.42622
[129]	train-mlogloss:1.41338	validation-mlogloss:1.42312
[130]	train-mlogloss:1.41028	validation-mlogloss:1.41994
[131]	train-mlogloss:1.40706	validation-mlogloss:1.41665
[132]	train-mlogloss:1.40402	validation-mlogloss:1.41365
[133]	train-mlogloss:1.40106	validation-mlogloss:1.41077
[134]	train-mlogloss:1.39805	validation-mlogloss:1.40782
[135]	train-mlogloss:1.39495	validation-mlogloss:1.40461
[136]	train-mlogloss:1.39202	validation-mlogloss:1.40177
[137]	train-mlogloss:1.38903	validation-mlogloss:1.39889
[138]	train-mlogloss:1.38608	validation-mlogloss:1.39598
[139]	train-mlogloss:1.38346	validation-mlogloss:1.39328
[140]	train-mlogloss:1.38074	validation-mlogloss:1.39058
[141]	train-mlogloss:1.37795	validation-mlogloss:1.38782
[142]	train-mlogloss:1.37528	validation-mlogloss:1.38516
[143]	train-mlogloss:1.37265	validation-mlogloss:1.38253
[144]	train-mlogloss:1.36993	validation-mlogloss:1.37966
[145]	train-mlogloss:1.36742	validation-mlogloss:1.37714
[146]	train-mlogloss:1.36495	validation-mlogloss:1.37486
[147]	train-mlogloss:1.36231	validation-mlogloss:1.37207
[148]	train-mlogloss:1.35975	validation-mlogloss:1.36941
[149]	train-mlogloss:1.35729	validation-mlogloss:1.36682
[150]	train-mlogloss:1.35473	validation-mlogloss:1.36425
[151]	train-mlogloss:1.35235	validation-mlogloss:1.36195
[152]	train-mlogloss:1.34988	validation-mlogloss:1.35939
[153]	train-mlogloss:1.34744	validation-mlogloss:1.35708
[154]	train-mlogloss:1.34517	validation-mlogloss:1.35482
[155]	train-mlogloss:1.34275	validation-mlogloss:1.35236
[156]	train-mlogloss:1.34042	validation-mlogloss:1.35001
[157]	train-mlogloss:1.33816	validation-mlogloss:1.34782
[158]	train-mlogloss:1.33583	validation-mlogloss:1.34537
[159]	train-mlogloss:1.33374	validation-mlogloss:1.34329
[160]	train-mlogloss:1.33154	validation-mlogloss:1.34118
[161]	train-mlogloss:1.32944	validation-mlogloss:1.33912
[162]	train-mlogloss:1.32717	validation-mlogloss:1.33711
[163]	train-mlogloss:1.32490	validation-mlogloss:1.33476
[164]	train-mlogloss:1.32271	validation-mlogloss:1.33269
[165]	train-mlogloss:1.32067	validation-mlogloss:1.33059
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[182]	train-mlogloss:1.28748	validation-mlogloss:1.29806
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[190]	train-mlogloss:1.27347	validation-mlogloss:1.28488
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[192]	train-mlogloss:1.27012	validation-mlogloss:1.28176
[193]	train-mlogloss:1.26847	validation-mlogloss:1.28019
[194]	train-mlogloss:1.26683	validation-mlogloss:1.27862
[195]	train-mlogloss:1.26522	validation-mlogloss:1.27697
[196]	train-mlogloss:1.26364	validation-mlogloss:1.27549
[197]	train-mlogloss:1.26196	validation-mlogloss:1.27399
[198]	train-mlogloss:1.26053	validation-mlogloss:1.27268
[199]	train-mlogloss:1.25906	validation-mlogloss:1.27132
[200]	train-mlogloss:1.25748	validation-mlogloss:1.26993
[201]	train-mlogloss:1.25600	validation-mlogloss:1.26855
[202]	train-mlogloss:1.25449	validation-mlogloss:1.26703
[203]	train-mlogloss:1.25283	validation-mlogloss:1.26535
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[205]	train-mlogloss:1.24984	validation-mlogloss:1.26264
[206]	train-mlogloss:1.24837	validation-mlogloss:1.26133
[207]	train-mlogloss:1.24688	validation-mlogloss:1.25985
[208]	train-mlogloss:1.24532	validation-mlogloss:1.25832
[209]	train-mlogloss:1.24388	validation-mlogloss:1.25696
[210]	train-mlogloss:1.24253	validation-mlogloss:1.25558
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[475]	train-mlogloss:1.10058	validation-mlogloss:1.13428
[476]	train-mlogloss:1.10039	validation-mlogloss:1.13415
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[478]	train-mlogloss:1.10009	validation-mlogloss:1.13394
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[481]	train-mlogloss:1.09964	validation-mlogloss:1.13368
[482]	train-mlogloss:1.09947	validation-mlogloss:1.13354
[483]	train-mlogloss:1.09941	validation-mlogloss:1.13348
[484]	train-mlogloss:1.09934	validation-mlogloss:1.13349
[485]	train-mlogloss:1.09927	validation-mlogloss:1.13344
[486]	train-mlogloss:1.09921	validation-mlogloss:1.13340
[487]	train-mlogloss:1.09917	validation-mlogloss:1.13339
[488]	train-mlogloss:1.09903	validation-mlogloss:1.13331
[489]	train-mlogloss:1.09886	validation-mlogloss:1.13321
Saved model to disk.
2025-10-25 19:58:29.554217: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-25 19:58:29.567136: 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 00:00:1761415109.581772 1859634 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761415109.586325 1859634 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761415109.597702 1859634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761415109.597737 1859634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761415109.597741 1859634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761415109.597743 1859634 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-25 19:58:29.601316: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[302]	train-mlogloss:1.15195	validation-mlogloss:1.17253
[303]	train-mlogloss:1.15128	validation-mlogloss:1.17196
[304]	train-mlogloss:1.15073	validation-mlogloss:1.17165
[305]	train-mlogloss:1.15005	validation-mlogloss:1.17110
[306]	train-mlogloss:1.14933	validation-mlogloss:1.17047
[307]	train-mlogloss:1.14882	validation-mlogloss:1.17002
[308]	train-mlogloss:1.14839	validation-mlogloss:1.16968
[309]	train-mlogloss:1.14768	validation-mlogloss:1.16914
[310]	train-mlogloss:1.14714	validation-mlogloss:1.16869
[311]	train-mlogloss:1.14654	validation-mlogloss:1.16827
[312]	train-mlogloss:1.14590	validation-mlogloss:1.16774
[313]	train-mlogloss:1.14532	validation-mlogloss:1.16724
[314]	train-mlogloss:1.14489	validation-mlogloss:1.16685
[315]	train-mlogloss:1.14430	validation-mlogloss:1.16629
[316]	train-mlogloss:1.14375	validation-mlogloss:1.16575
[317]	train-mlogloss:1.14321	validation-mlogloss:1.16533
[318]	train-mlogloss:1.14271	validation-mlogloss:1.16493
[319]	train-mlogloss:1.14218	validation-mlogloss:1.16449
[320]	train-mlogloss:1.14174	validation-mlogloss:1.16411
[321]	train-mlogloss:1.14125	validation-mlogloss:1.16372
[322]	train-mlogloss:1.14083	validation-mlogloss:1.16344
[323]	train-mlogloss:1.14043	validation-mlogloss:1.16318
[324]	train-mlogloss:1.13990	validation-mlogloss:1.16281
[325]	train-mlogloss:1.13942	validation-mlogloss:1.16239
[326]	train-mlogloss:1.13894	validation-mlogloss:1.16196
[327]	train-mlogloss:1.13847	validation-mlogloss:1.16149
[328]	train-mlogloss:1.13795	validation-mlogloss:1.16105
[329]	train-mlogloss:1.13747	validation-mlogloss:1.16060
[330]	train-mlogloss:1.13709	validation-mlogloss:1.16021
[331]	train-mlogloss:1.13671	validation-mlogloss:1.15993
[332]	train-mlogloss:1.13639	validation-mlogloss:1.15971
[333]	train-mlogloss:1.13597	validation-mlogloss:1.15944
[334]	train-mlogloss:1.13554	validation-mlogloss:1.15917
[335]	train-mlogloss:1.13502	validation-mlogloss:1.15866
[336]	train-mlogloss:1.13458	validation-mlogloss:1.15825
[337]	train-mlogloss:1.13416	validation-mlogloss:1.15785
[338]	train-mlogloss:1.13366	validation-mlogloss:1.15742
[339]	train-mlogloss:1.13328	validation-mlogloss:1.15709
[340]	train-mlogloss:1.13279	validation-mlogloss:1.15681
[341]	train-mlogloss:1.13226	validation-mlogloss:1.15635
[342]	train-mlogloss:1.13190	validation-mlogloss:1.15612
[343]	train-mlogloss:1.13145	validation-mlogloss:1.15576
[344]	train-mlogloss:1.13115	validation-mlogloss:1.15547
[345]	train-mlogloss:1.13065	validation-mlogloss:1.15498
[346]	train-mlogloss:1.13030	validation-mlogloss:1.15476
[347]	train-mlogloss:1.12995	validation-mlogloss:1.15450
[348]	train-mlogloss:1.12948	validation-mlogloss:1.15419
[349]	train-mlogloss:1.12909	validation-mlogloss:1.15392
[350]	train-mlogloss:1.12862	validation-mlogloss:1.15350
[351]	train-mlogloss:1.12837	validation-mlogloss:1.15327
[352]	train-mlogloss:1.12799	validation-mlogloss:1.15294
[353]	train-mlogloss:1.12768	validation-mlogloss:1.15280
[354]	train-mlogloss:1.12737	validation-mlogloss:1.15259
[355]	train-mlogloss:1.12703	validation-mlogloss:1.15236
[356]	train-mlogloss:1.12671	validation-mlogloss:1.15211
[357]	train-mlogloss:1.12636	validation-mlogloss:1.15182
[358]	train-mlogloss:1.12600	validation-mlogloss:1.15156
[359]	train-mlogloss:1.12570	validation-mlogloss:1.15128
[360]	train-mlogloss:1.12536	validation-mlogloss:1.15099
[361]	train-mlogloss:1.12489	validation-mlogloss:1.15064
[362]	train-mlogloss:1.12457	validation-mlogloss:1.15043
[363]	train-mlogloss:1.12422	validation-mlogloss:1.15019
[364]	train-mlogloss:1.12398	validation-mlogloss:1.15002
[365]	train-mlogloss:1.12365	validation-mlogloss:1.14984
[366]	train-mlogloss:1.12348	validation-mlogloss:1.14969
[367]	train-mlogloss:1.12309	validation-mlogloss:1.14940
[368]	train-mlogloss:1.12273	validation-mlogloss:1.14913
[369]	train-mlogloss:1.12234	validation-mlogloss:1.14891
[370]	train-mlogloss:1.12196	validation-mlogloss:1.14851
[371]	train-mlogloss:1.12165	validation-mlogloss:1.14829
[372]	train-mlogloss:1.12111	validation-mlogloss:1.14779
[373]	train-mlogloss:1.12077	validation-mlogloss:1.14756
[374]	train-mlogloss:1.12042	validation-mlogloss:1.14723
[375]	train-mlogloss:1.12012	validation-mlogloss:1.14699
[376]	train-mlogloss:1.11981	validation-mlogloss:1.14678
[377]	train-mlogloss:1.11946	validation-mlogloss:1.14655
[378]	train-mlogloss:1.11914	validation-mlogloss:1.14625
[379]	train-mlogloss:1.11881	validation-mlogloss:1.14596
[380]	train-mlogloss:1.11857	validation-mlogloss:1.14578
[381]	train-mlogloss:1.11831	validation-mlogloss:1.14559
[382]	train-mlogloss:1.11810	validation-mlogloss:1.14549
[383]	train-mlogloss:1.11795	validation-mlogloss:1.14540
[384]	train-mlogloss:1.11769	validation-mlogloss:1.14517
[385]	train-mlogloss:1.11741	validation-mlogloss:1.14493
[386]	train-mlogloss:1.11712	validation-mlogloss:1.14475
[387]	train-mlogloss:1.11678	validation-mlogloss:1.14447
[388]	train-mlogloss:1.11650	validation-mlogloss:1.14427
[389]	train-mlogloss:1.11628	validation-mlogloss:1.14411
[390]	train-mlogloss:1.11606	validation-mlogloss:1.14387
[391]	train-mlogloss:1.11574	validation-mlogloss:1.14365
[392]	train-mlogloss:1.11559	validation-mlogloss:1.14353
[393]	train-mlogloss:1.11537	validation-mlogloss:1.14338
[394]	train-mlogloss:1.11513	validation-mlogloss:1.14317
[395]	train-mlogloss:1.11481	validation-mlogloss:1.14289
[396]	train-mlogloss:1.11463	validation-mlogloss:1.14273
[397]	train-mlogloss:1.11446	validation-mlogloss:1.14262
[398]	train-mlogloss:1.11412	validation-mlogloss:1.14240
[399]	train-mlogloss:1.11378	validation-mlogloss:1.14215
[400]	train-mlogloss:1.11348	validation-mlogloss:1.14193
[401]	train-mlogloss:1.11327	validation-mlogloss:1.14176
[402]	train-mlogloss:1.11299	validation-mlogloss:1.14149
[403]	train-mlogloss:1.11281	validation-mlogloss:1.14142
[404]	train-mlogloss:1.11263	validation-mlogloss:1.14131
[405]	train-mlogloss:1.11248	validation-mlogloss:1.14119
[406]	train-mlogloss:1.11231	validation-mlogloss:1.14109
[407]	train-mlogloss:1.11207	validation-mlogloss:1.14100
[408]	train-mlogloss:1.11188	validation-mlogloss:1.14088
[409]	train-mlogloss:1.11175	validation-mlogloss:1.14075
[410]	train-mlogloss:1.11161	validation-mlogloss:1.14066
[411]	train-mlogloss:1.11143	validation-mlogloss:1.14052
[412]	train-mlogloss:1.11123	validation-mlogloss:1.14037
[413]	train-mlogloss:1.11104	validation-mlogloss:1.14024
[414]	train-mlogloss:1.11073	validation-mlogloss:1.13996
[415]	train-mlogloss:1.11048	validation-mlogloss:1.13981
[416]	train-mlogloss:1.11032	validation-mlogloss:1.13969
[417]	train-mlogloss:1.11008	validation-mlogloss:1.13946
[418]	train-mlogloss:1.10991	validation-mlogloss:1.13932
[419]	train-mlogloss:1.10975	validation-mlogloss:1.13918
[420]	train-mlogloss:1.10948	validation-mlogloss:1.13900
[421]	train-mlogloss:1.10934	validation-mlogloss:1.13895
[422]	train-mlogloss:1.10917	validation-mlogloss:1.13881
[423]	train-mlogloss:1.10894	validation-mlogloss:1.13860
[424]	train-mlogloss:1.10877	validation-mlogloss:1.13844
[425]	train-mlogloss:1.10848	validation-mlogloss:1.13826
[426]	train-mlogloss:1.10826	validation-mlogloss:1.13810
[427]	train-mlogloss:1.10824	validation-mlogloss:1.13809
[428]	train-mlogloss:1.10807	validation-mlogloss:1.13788
[429]	train-mlogloss:1.10785	validation-mlogloss:1.13775
[430]	train-mlogloss:1.10764	validation-mlogloss:1.13757
[431]	train-mlogloss:1.10744	validation-mlogloss:1.13748
[432]	train-mlogloss:1.10720	validation-mlogloss:1.13733
[433]	train-mlogloss:1.10710	validation-mlogloss:1.13724
[434]	train-mlogloss:1.10686	validation-mlogloss:1.13701
[435]	train-mlogloss:1.10656	validation-mlogloss:1.13686
[436]	train-mlogloss:1.10641	validation-mlogloss:1.13678
[437]	train-mlogloss:1.10628	validation-mlogloss:1.13665
[438]	train-mlogloss:1.10598	validation-mlogloss:1.13653
[439]	train-mlogloss:1.10579	validation-mlogloss:1.13636
[440]	train-mlogloss:1.10560	validation-mlogloss:1.13619
[441]	train-mlogloss:1.10539	validation-mlogloss:1.13604
[442]	train-mlogloss:1.10531	validation-mlogloss:1.13598
[443]	train-mlogloss:1.10508	validation-mlogloss:1.13579
[444]	train-mlogloss:1.10489	validation-mlogloss:1.13566
[445]	train-mlogloss:1.10475	validation-mlogloss:1.13559
[446]	train-mlogloss:1.10464	validation-mlogloss:1.13552
[447]	train-mlogloss:1.10448	validation-mlogloss:1.13540
[448]	train-mlogloss:1.10425	validation-mlogloss:1.13513
[449]	train-mlogloss:1.10395	validation-mlogloss:1.13492
[450]	train-mlogloss:1.10375	validation-mlogloss:1.13481
[451]	train-mlogloss:1.10346	validation-mlogloss:1.13458
[452]	train-mlogloss:1.10327	validation-mlogloss:1.13447
[453]	train-mlogloss:1.10319	validation-mlogloss:1.13441
[454]	train-mlogloss:1.10305	validation-mlogloss:1.13431
[455]	train-mlogloss:1.10284	validation-mlogloss:1.13416
[456]	train-mlogloss:1.10258	validation-mlogloss:1.13408
[457]	train-mlogloss:1.10247	validation-mlogloss:1.13400
[458]	train-mlogloss:1.10231	validation-mlogloss:1.13389
[459]	train-mlogloss:1.10219	validation-mlogloss:1.13381
[460]	train-mlogloss:1.10204	validation-mlogloss:1.13367
[461]	train-mlogloss:1.10198	validation-mlogloss:1.13361
[462]	train-mlogloss:1.10175	validation-mlogloss:1.13347
[463]	train-mlogloss:1.10150	validation-mlogloss:1.13321
[464]	train-mlogloss:1.10138	validation-mlogloss:1.13314
[465]	train-mlogloss:1.10109	validation-mlogloss:1.13289
[466]	train-mlogloss:1.10094	validation-mlogloss:1.13275
[467]	train-mlogloss:1.10090	validation-mlogloss:1.13271
[468]	train-mlogloss:1.10077	validation-mlogloss:1.13265
[469]	train-mlogloss:1.10058	validation-mlogloss:1.13251
[470]	train-mlogloss:1.10042	validation-mlogloss:1.13250
[471]	train-mlogloss:1.10025	validation-mlogloss:1.13238
[472]	train-mlogloss:1.10012	validation-mlogloss:1.13231
[473]	train-mlogloss:1.09997	validation-mlogloss:1.13222
[474]	train-mlogloss:1.09986	validation-mlogloss:1.13214
[475]	train-mlogloss:1.09967	validation-mlogloss:1.13196
[476]	train-mlogloss:1.09956	validation-mlogloss:1.13186
[477]	train-mlogloss:1.09934	validation-mlogloss:1.13164
[478]	train-mlogloss:1.09926	validation-mlogloss:1.13157
[479]	train-mlogloss:1.09915	validation-mlogloss:1.13149
[480]	train-mlogloss:1.09907	validation-mlogloss:1.13147
[481]	train-mlogloss:1.09899	validation-mlogloss:1.13141
[482]	train-mlogloss:1.09879	validation-mlogloss:1.13134
[483]	train-mlogloss:1.09873	validation-mlogloss:1.13128
[484]	train-mlogloss:1.09854	validation-mlogloss:1.13120
[485]	train-mlogloss:1.09843	validation-mlogloss:1.13111
[486]	train-mlogloss:1.09836	validation-mlogloss:1.13111
[487]	train-mlogloss:1.09822	validation-mlogloss:1.13102
[488]	train-mlogloss:1.09816	validation-mlogloss:1.13098
[489]	train-mlogloss:1.09800	validation-mlogloss:1.13087
Saved model to disk.

=== EJECUCIÓN 1 ===

--- TRAIN (ejecución 1) ---

--- TEST (ejecución 1) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.51 [%]
Global accuracy score (test) = 63.23 [%]
Global F1 score (train) = 66.33 [%]
Global F1 score (test) = 62.52 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.49      0.42      0.45       161
 CAMINAR CON MÓVIL O LIBRO       0.41      0.58      0.48       161
       CAMINAR USUAL SPEED       0.44      0.26      0.33       161
            CAMINAR ZIGZAG       0.70      0.58      0.63       161
          DE PIE BARRIENDO       0.73      0.61      0.67       161
   DE PIE DOBLANDO TOALLAS       0.43      0.54      0.48       161
    DE PIE MOVIENDO LIBROS       0.53      0.50      0.51       161
          DE PIE USANDO PC       0.84      0.91      0.87       161
        FASE REPOSO CON K5       0.79      0.93      0.86       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.60      0.39      0.47       161
         SENTADO USANDO PC       0.47      0.61      0.53       161
      SENTADO VIENDO LA TV       0.55      0.38      0.45       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.63      2415
              weighted avg       0.64      0.63      0.63      2415


Accuracy capturado en la ejecución 1: 63.23 [%]
F1-score capturado en la ejecución 1: 62.52 [%]

=== EJECUCIÓN 2 ===

--- TRAIN (ejecución 2) ---

--- TEST (ejecución 2) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.7 [%]
Global accuracy score (test) = 63.31 [%]
Global F1 score (train) = 66.55 [%]
Global F1 score (test) = 62.65 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.50      0.44      0.47       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.55      0.46       161
       CAMINAR USUAL SPEED       0.45      0.30      0.36       161
            CAMINAR ZIGZAG       0.74      0.58      0.65       161
          DE PIE BARRIENDO       0.76      0.63      0.69       161
   DE PIE DOBLANDO TOALLAS       0.43      0.55      0.48       161
    DE PIE MOVIENDO LIBROS       0.53      0.48      0.50       161
          DE PIE USANDO PC       0.84      0.93      0.88       161
        FASE REPOSO CON K5       0.77      0.93      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.58      0.40      0.47       161
         SENTADO USANDO PC       0.48      0.60      0.54       161
      SENTADO VIENDO LA TV       0.49      0.33      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.63      2415
              weighted avg       0.64      0.63      0.63      2415


Accuracy capturado en la ejecución 2: 63.31 [%]
F1-score capturado en la ejecución 2: 62.65 [%]

=== EJECUCIÓN 3 ===

--- TRAIN (ejecución 3) ---

--- TEST (ejecución 3) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.93 [%]
Global accuracy score (test) = 62.73 [%]
Global F1 score (train) = 66.73 [%]
Global F1 score (test) = 61.84 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.48      0.42      0.44       161
 CAMINAR CON MÓVIL O LIBRO       0.41      0.59      0.48       161
       CAMINAR USUAL SPEED       0.47      0.27      0.34       161
            CAMINAR ZIGZAG       0.70      0.58      0.63       161
          DE PIE BARRIENDO       0.73      0.61      0.67       161
   DE PIE DOBLANDO TOALLAS       0.43      0.57      0.49       161
    DE PIE MOVIENDO LIBROS       0.53      0.45      0.49       161
          DE PIE USANDO PC       0.80      0.92      0.86       161
        FASE REPOSO CON K5       0.76      0.92      0.83       161
INCREMENTAL CICLOERGOMETRO       1.00      0.98      0.99       161
           SENTADO LEYENDO       0.60      0.37      0.46       161
         SENTADO USANDO PC       0.48      0.62      0.54       161
      SENTADO VIENDO LA TV       0.49      0.32      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 3: 62.73 [%]
F1-score capturado en la ejecución 3: 61.84 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-10-25 20:31:34.656924: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-25 20:31:34.690699: 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 00:00:1761417094.727271 1901887 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761417094.737214 1901887 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761417094.766945 1901887 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761417094.767058 1901887 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761417094.767115 1901887 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761417094.767170 1901887 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-25 20:31:34.775264: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67603	validation-mlogloss:2.67714
[1]	train-mlogloss:2.64335	validation-mlogloss:2.64652
[2]	train-mlogloss:2.61232	validation-mlogloss:2.61694
[3]	train-mlogloss:2.58311	validation-mlogloss:2.58845
[4]	train-mlogloss:2.55466	validation-mlogloss:2.56078
[5]	train-mlogloss:2.52668	validation-mlogloss:2.53352
[6]	train-mlogloss:2.50102	validation-mlogloss:2.50944
[7]	train-mlogloss:2.47594	validation-mlogloss:2.48529
[8]	train-mlogloss:2.45182	validation-mlogloss:2.46172
[9]	train-mlogloss:2.42870	validation-mlogloss:2.43917
[10]	train-mlogloss:2.40635	validation-mlogloss:2.41716
[11]	train-mlogloss:2.38448	validation-mlogloss:2.39595
[12]	train-mlogloss:2.36287	validation-mlogloss:2.37387
[13]	train-mlogloss:2.34133	validation-mlogloss:2.35358
[14]	train-mlogloss:2.32160	validation-mlogloss:2.33343
[15]	train-mlogloss:2.30164	validation-mlogloss:2.31305
[16]	train-mlogloss:2.28167	validation-mlogloss:2.29293
[17]	train-mlogloss:2.26386	validation-mlogloss:2.27464
[18]	train-mlogloss:2.24616	validation-mlogloss:2.25749
[19]	train-mlogloss:2.22838	validation-mlogloss:2.24061
[20]	train-mlogloss:2.21149	validation-mlogloss:2.22447
[21]	train-mlogloss:2.19519	validation-mlogloss:2.20848
[22]	train-mlogloss:2.17812	validation-mlogloss:2.19179
[23]	train-mlogloss:2.16253	validation-mlogloss:2.17604
[24]	train-mlogloss:2.14653	validation-mlogloss:2.16010
[25]	train-mlogloss:2.13112	validation-mlogloss:2.14458
[26]	train-mlogloss:2.11613	validation-mlogloss:2.13001
[27]	train-mlogloss:2.10123	validation-mlogloss:2.11492
[28]	train-mlogloss:2.08671	validation-mlogloss:2.10019
[29]	train-mlogloss:2.07237	validation-mlogloss:2.08588
[30]	train-mlogloss:2.05868	validation-mlogloss:2.07283
[31]	train-mlogloss:2.04505	validation-mlogloss:2.05884
[32]	train-mlogloss:2.03198	validation-mlogloss:2.04557
[33]	train-mlogloss:2.01871	validation-mlogloss:2.03236
[34]	train-mlogloss:2.00570	validation-mlogloss:2.01895
[35]	train-mlogloss:1.99323	validation-mlogloss:2.00635
[36]	train-mlogloss:1.98153	validation-mlogloss:1.99488
[37]	train-mlogloss:1.97034	validation-mlogloss:1.98373
[38]	train-mlogloss:1.95845	validation-mlogloss:1.97140
[39]	train-mlogloss:1.94708	validation-mlogloss:1.95995
[40]	train-mlogloss:1.93591	validation-mlogloss:1.94896
[41]	train-mlogloss:1.92489	validation-mlogloss:1.93801
[42]	train-mlogloss:1.91448	validation-mlogloss:1.92773
[43]	train-mlogloss:1.90376	validation-mlogloss:1.91702
[44]	train-mlogloss:1.89340	validation-mlogloss:1.90682
[45]	train-mlogloss:1.88331	validation-mlogloss:1.89733
[46]	train-mlogloss:1.87285	validation-mlogloss:1.88695
[47]	train-mlogloss:1.86309	validation-mlogloss:1.87712
[48]	train-mlogloss:1.85369	validation-mlogloss:1.86777
[49]	train-mlogloss:1.84392	validation-mlogloss:1.85816
[50]	train-mlogloss:1.83478	validation-mlogloss:1.84922
[51]	train-mlogloss:1.82587	validation-mlogloss:1.84040
[52]	train-mlogloss:1.81680	validation-mlogloss:1.83139
[53]	train-mlogloss:1.80803	validation-mlogloss:1.82270
[54]	train-mlogloss:1.79926	validation-mlogloss:1.81395
[55]	train-mlogloss:1.79040	validation-mlogloss:1.80486
[56]	train-mlogloss:1.78250	validation-mlogloss:1.79703
[57]	train-mlogloss:1.77439	validation-mlogloss:1.78873
[58]	train-mlogloss:1.76603	validation-mlogloss:1.78040
[59]	train-mlogloss:1.75823	validation-mlogloss:1.77226
[60]	train-mlogloss:1.75031	validation-mlogloss:1.76430
[61]	train-mlogloss:1.74285	validation-mlogloss:1.75653
[62]	train-mlogloss:1.73573	validation-mlogloss:1.74936
[63]	train-mlogloss:1.72787	validation-mlogloss:1.74165
[64]	train-mlogloss:1.72052	validation-mlogloss:1.73425
[65]	train-mlogloss:1.71321	validation-mlogloss:1.72686
[66]	train-mlogloss:1.70592	validation-mlogloss:1.71943
[67]	train-mlogloss:1.69913	validation-mlogloss:1.71248
[68]	train-mlogloss:1.69216	validation-mlogloss:1.70571
[69]	train-mlogloss:1.68531	validation-mlogloss:1.69896
[70]	train-mlogloss:1.67891	validation-mlogloss:1.69281
[71]	train-mlogloss:1.67221	validation-mlogloss:1.68594
[72]	train-mlogloss:1.66551	validation-mlogloss:1.67921
[73]	train-mlogloss:1.65892	validation-mlogloss:1.67282
[74]	train-mlogloss:1.65261	validation-mlogloss:1.66651
[75]	train-mlogloss:1.64653	validation-mlogloss:1.66053
[76]	train-mlogloss:1.64040	validation-mlogloss:1.65405
[77]	train-mlogloss:1.63434	validation-mlogloss:1.64802
[78]	train-mlogloss:1.62844	validation-mlogloss:1.64208
[79]	train-mlogloss:1.62246	validation-mlogloss:1.63601
[80]	train-mlogloss:1.61698	validation-mlogloss:1.63049
[81]	train-mlogloss:1.61158	validation-mlogloss:1.62486
[82]	train-mlogloss:1.60600	validation-mlogloss:1.61934
[83]	train-mlogloss:1.60076	validation-mlogloss:1.61430
[84]	train-mlogloss:1.59548	validation-mlogloss:1.60896
[85]	train-mlogloss:1.59015	validation-mlogloss:1.60379
[86]	train-mlogloss:1.58467	validation-mlogloss:1.59816
[87]	train-mlogloss:1.57936	validation-mlogloss:1.59273
[88]	train-mlogloss:1.57407	validation-mlogloss:1.58701
[89]	train-mlogloss:1.56901	validation-mlogloss:1.58188
[90]	train-mlogloss:1.56388	validation-mlogloss:1.57667
[91]	train-mlogloss:1.55908	validation-mlogloss:1.57180
[92]	train-mlogloss:1.55425	validation-mlogloss:1.56660
[93]	train-mlogloss:1.54963	validation-mlogloss:1.56201
[94]	train-mlogloss:1.54498	validation-mlogloss:1.55709
[95]	train-mlogloss:1.54048	validation-mlogloss:1.55250
[96]	train-mlogloss:1.53595	validation-mlogloss:1.54806
[97]	train-mlogloss:1.53126	validation-mlogloss:1.54341
[98]	train-mlogloss:1.52661	validation-mlogloss:1.53863
[99]	train-mlogloss:1.52208	validation-mlogloss:1.53392
[100]	train-mlogloss:1.51763	validation-mlogloss:1.52954
[101]	train-mlogloss:1.51326	validation-mlogloss:1.52524
[102]	train-mlogloss:1.50920	validation-mlogloss:1.52110
[103]	train-mlogloss:1.50514	validation-mlogloss:1.51700
[104]	train-mlogloss:1.50112	validation-mlogloss:1.51307
[105]	train-mlogloss:1.49724	validation-mlogloss:1.50925
[106]	train-mlogloss:1.49322	validation-mlogloss:1.50518
[107]	train-mlogloss:1.48936	validation-mlogloss:1.50107
[108]	train-mlogloss:1.48550	validation-mlogloss:1.49716
[109]	train-mlogloss:1.48167	validation-mlogloss:1.49327
[110]	train-mlogloss:1.47778	validation-mlogloss:1.48929
[111]	train-mlogloss:1.47394	validation-mlogloss:1.48554
[112]	train-mlogloss:1.47022	validation-mlogloss:1.48192
[113]	train-mlogloss:1.46641	validation-mlogloss:1.47804
[114]	train-mlogloss:1.46260	validation-mlogloss:1.47414
[115]	train-mlogloss:1.45900	validation-mlogloss:1.47037
[116]	train-mlogloss:1.45542	validation-mlogloss:1.46689
[117]	train-mlogloss:1.45170	validation-mlogloss:1.46319
[118]	train-mlogloss:1.44798	validation-mlogloss:1.45952
[119]	train-mlogloss:1.44482	validation-mlogloss:1.45628
[120]	train-mlogloss:1.44129	validation-mlogloss:1.45270
[121]	train-mlogloss:1.43794	validation-mlogloss:1.44936
[122]	train-mlogloss:1.43456	validation-mlogloss:1.44607
[123]	train-mlogloss:1.43118	validation-mlogloss:1.44278
[124]	train-mlogloss:1.42782	validation-mlogloss:1.43937
[125]	train-mlogloss:1.42446	validation-mlogloss:1.43602
[126]	train-mlogloss:1.42118	validation-mlogloss:1.43281
[127]	train-mlogloss:1.41795	validation-mlogloss:1.42967
[128]	train-mlogloss:1.41491	validation-mlogloss:1.42660
[129]	train-mlogloss:1.41196	validation-mlogloss:1.42371
[130]	train-mlogloss:1.40875	validation-mlogloss:1.42028
[131]	train-mlogloss:1.40554	validation-mlogloss:1.41709
[132]	train-mlogloss:1.40261	validation-mlogloss:1.41423
[133]	train-mlogloss:1.39969	validation-mlogloss:1.41146
[134]	train-mlogloss:1.39660	validation-mlogloss:1.40832
[135]	train-mlogloss:1.39374	validation-mlogloss:1.40535
[136]	train-mlogloss:1.39081	validation-mlogloss:1.40260
[137]	train-mlogloss:1.38812	validation-mlogloss:1.39988
[138]	train-mlogloss:1.38540	validation-mlogloss:1.39708
[139]	train-mlogloss:1.38266	validation-mlogloss:1.39425
[140]	train-mlogloss:1.37992	validation-mlogloss:1.39151
[141]	train-mlogloss:1.37706	validation-mlogloss:1.38867
[142]	train-mlogloss:1.37438	validation-mlogloss:1.38579
[143]	train-mlogloss:1.37156	validation-mlogloss:1.38289
[144]	train-mlogloss:1.36886	validation-mlogloss:1.38024
[145]	train-mlogloss:1.36631	validation-mlogloss:1.37765
[146]	train-mlogloss:1.36382	validation-mlogloss:1.37515
[147]	train-mlogloss:1.36134	validation-mlogloss:1.37257
[148]	train-mlogloss:1.35891	validation-mlogloss:1.37020
[149]	train-mlogloss:1.35632	validation-mlogloss:1.36756
[150]	train-mlogloss:1.35384	validation-mlogloss:1.36492
[151]	train-mlogloss:1.35135	validation-mlogloss:1.36224
[152]	train-mlogloss:1.34889	validation-mlogloss:1.35985
[153]	train-mlogloss:1.34645	validation-mlogloss:1.35737
[154]	train-mlogloss:1.34409	validation-mlogloss:1.35512
[155]	train-mlogloss:1.34165	validation-mlogloss:1.35263
[156]	train-mlogloss:1.33943	validation-mlogloss:1.35051
[157]	train-mlogloss:1.33726	validation-mlogloss:1.34830
[158]	train-mlogloss:1.33501	validation-mlogloss:1.34609
[159]	train-mlogloss:1.33271	validation-mlogloss:1.34384
[160]	train-mlogloss:1.33039	validation-mlogloss:1.34153
[161]	train-mlogloss:1.32823	validation-mlogloss:1.33943
[162]	train-mlogloss:1.32602	validation-mlogloss:1.33721
[163]	train-mlogloss:1.32399	validation-mlogloss:1.33522
[164]	train-mlogloss:1.32183	validation-mlogloss:1.33308
[165]	train-mlogloss:1.31969	validation-mlogloss:1.33108
[166]	train-mlogloss:1.31743	validation-mlogloss:1.32885
[167]	train-mlogloss:1.31537	validation-mlogloss:1.32673
[168]	train-mlogloss:1.31340	validation-mlogloss:1.32479
[169]	train-mlogloss:1.31137	validation-mlogloss:1.32276
[170]	train-mlogloss:1.30947	validation-mlogloss:1.32087
[171]	train-mlogloss:1.30734	validation-mlogloss:1.31881
[172]	train-mlogloss:1.30546	validation-mlogloss:1.31692
[173]	train-mlogloss:1.30377	validation-mlogloss:1.31520
[174]	train-mlogloss:1.30191	validation-mlogloss:1.31330
[175]	train-mlogloss:1.29994	validation-mlogloss:1.31128
[176]	train-mlogloss:1.29793	validation-mlogloss:1.30936
[177]	train-mlogloss:1.29614	validation-mlogloss:1.30759
[178]	train-mlogloss:1.29441	validation-mlogloss:1.30600
[179]	train-mlogloss:1.29253	validation-mlogloss:1.30414
[180]	train-mlogloss:1.29064	validation-mlogloss:1.30237
[181]	train-mlogloss:1.28882	validation-mlogloss:1.30044
[182]	train-mlogloss:1.28703	validation-mlogloss:1.29852
[183]	train-mlogloss:1.28522	validation-mlogloss:1.29685
[184]	train-mlogloss:1.28330	validation-mlogloss:1.29508
[185]	train-mlogloss:1.28155	validation-mlogloss:1.29340
[186]	train-mlogloss:1.27977	validation-mlogloss:1.29162
[187]	train-mlogloss:1.27811	validation-mlogloss:1.29007
[188]	train-mlogloss:1.27638	validation-mlogloss:1.28824
[189]	train-mlogloss:1.27478	validation-mlogloss:1.28682
[190]	train-mlogloss:1.27306	validation-mlogloss:1.28541
[191]	train-mlogloss:1.27145	validation-mlogloss:1.28375
[192]	train-mlogloss:1.26984	validation-mlogloss:1.28220
[193]	train-mlogloss:1.26815	validation-mlogloss:1.28063
[194]	train-mlogloss:1.26649	validation-mlogloss:1.27911
[195]	train-mlogloss:1.26477	validation-mlogloss:1.27740
[196]	train-mlogloss:1.26321	validation-mlogloss:1.27594
[197]	train-mlogloss:1.26168	validation-mlogloss:1.27451
[198]	train-mlogloss:1.26013	validation-mlogloss:1.27315
[199]	train-mlogloss:1.25850	validation-mlogloss:1.27148
[200]	train-mlogloss:1.25695	validation-mlogloss:1.27013
[201]	train-mlogloss:1.25541	validation-mlogloss:1.26872
[202]	train-mlogloss:1.25403	validation-mlogloss:1.26748
[203]	train-mlogloss:1.25253	validation-mlogloss:1.26608
[204]	train-mlogloss:1.25112	validation-mlogloss:1.26474
[205]	train-mlogloss:1.24964	validation-mlogloss:1.26347
[206]	train-mlogloss:1.24815	validation-mlogloss:1.26215
[207]	train-mlogloss:1.24675	validation-mlogloss:1.26080
[208]	train-mlogloss:1.24531	validation-mlogloss:1.25949
[209]	train-mlogloss:1.24385	validation-mlogloss:1.25826
[210]	train-mlogloss:1.24254	validation-mlogloss:1.25709
[211]	train-mlogloss:1.24120	validation-mlogloss:1.25582
[212]	train-mlogloss:1.23974	validation-mlogloss:1.25456
[213]	train-mlogloss:1.23843	validation-mlogloss:1.25336
[214]	train-mlogloss:1.23707	validation-mlogloss:1.25213
[215]	train-mlogloss:1.23570	validation-mlogloss:1.25080
[216]	train-mlogloss:1.23447	validation-mlogloss:1.24961
[217]	train-mlogloss:1.23324	validation-mlogloss:1.24843
[218]	train-mlogloss:1.23186	validation-mlogloss:1.24719
[219]	train-mlogloss:1.23050	validation-mlogloss:1.24594
[220]	train-mlogloss:1.22917	validation-mlogloss:1.24469
[221]	train-mlogloss:1.22784	validation-mlogloss:1.24342
[222]	train-mlogloss:1.22669	validation-mlogloss:1.24236
[223]	train-mlogloss:1.22547	validation-mlogloss:1.24133
[224]	train-mlogloss:1.22417	validation-mlogloss:1.24016
[225]	train-mlogloss:1.22311	validation-mlogloss:1.23924
[226]	train-mlogloss:1.22188	validation-mlogloss:1.23815
[227]	train-mlogloss:1.22061	validation-mlogloss:1.23696
[228]	train-mlogloss:1.21932	validation-mlogloss:1.23575
[229]	train-mlogloss:1.21815	validation-mlogloss:1.23465
[230]	train-mlogloss:1.21688	validation-mlogloss:1.23343
[231]	train-mlogloss:1.21568	validation-mlogloss:1.23230
[232]	train-mlogloss:1.21444	validation-mlogloss:1.23110
[233]	train-mlogloss:1.21330	validation-mlogloss:1.23001
[234]	train-mlogloss:1.21215	validation-mlogloss:1.22911
[235]	train-mlogloss:1.21088	validation-mlogloss:1.22795
[236]	train-mlogloss:1.20963	validation-mlogloss:1.22683
[237]	train-mlogloss:1.20848	validation-mlogloss:1.22579
[238]	train-mlogloss:1.20726	validation-mlogloss:1.22472
[239]	train-mlogloss:1.20605	validation-mlogloss:1.22347
[240]	train-mlogloss:1.20494	validation-mlogloss:1.22244
[241]	train-mlogloss:1.20388	validation-mlogloss:1.22163
[242]	train-mlogloss:1.20277	validation-mlogloss:1.22062
[243]	train-mlogloss:1.20174	validation-mlogloss:1.21969
[244]	train-mlogloss:1.20076	validation-mlogloss:1.21891
[245]	train-mlogloss:1.19969	validation-mlogloss:1.21807
[246]	train-mlogloss:1.19852	validation-mlogloss:1.21708
[247]	train-mlogloss:1.19754	validation-mlogloss:1.21617
[248]	train-mlogloss:1.19632	validation-mlogloss:1.21510
[249]	train-mlogloss:1.19520	validation-mlogloss:1.21409
[250]	train-mlogloss:1.19410	validation-mlogloss:1.21311
[251]	train-mlogloss:1.19310	validation-mlogloss:1.21233
[252]	train-mlogloss:1.19210	validation-mlogloss:1.21139
[253]	train-mlogloss:1.19113	validation-mlogloss:1.21060
[254]	train-mlogloss:1.19011	validation-mlogloss:1.20982
[255]	train-mlogloss:1.18912	validation-mlogloss:1.20900
[256]	train-mlogloss:1.18820	validation-mlogloss:1.20829
[257]	train-mlogloss:1.18726	validation-mlogloss:1.20747
[258]	train-mlogloss:1.18645	validation-mlogloss:1.20678
[259]	train-mlogloss:1.18536	validation-mlogloss:1.20568
[260]	train-mlogloss:1.18438	validation-mlogloss:1.20483
[261]	train-mlogloss:1.18342	validation-mlogloss:1.20402
[262]	train-mlogloss:1.18253	validation-mlogloss:1.20333
[263]	train-mlogloss:1.18172	validation-mlogloss:1.20260
[264]	train-mlogloss:1.18098	validation-mlogloss:1.20188
[265]	train-mlogloss:1.18008	validation-mlogloss:1.20114
[266]	train-mlogloss:1.17925	validation-mlogloss:1.20033
[267]	train-mlogloss:1.17845	validation-mlogloss:1.19967
[268]	train-mlogloss:1.17763	validation-mlogloss:1.19907
[269]	train-mlogloss:1.17672	validation-mlogloss:1.19821
[270]	train-mlogloss:1.17599	validation-mlogloss:1.19770
[271]	train-mlogloss:1.17511	validation-mlogloss:1.19687
[272]	train-mlogloss:1.17413	validation-mlogloss:1.19605
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[442]	train-mlogloss:1.10532	validation-mlogloss:1.14136
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[449]	train-mlogloss:1.10449	validation-mlogloss:1.14074
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[455]	train-mlogloss:1.10365	validation-mlogloss:1.14020
[456]	train-mlogloss:1.10356	validation-mlogloss:1.14015
[457]	train-mlogloss:1.10341	validation-mlogloss:1.14001
[458]	train-mlogloss:1.10322	validation-mlogloss:1.13995
[459]	train-mlogloss:1.10309	validation-mlogloss:1.13992
[460]	train-mlogloss:1.10298	validation-mlogloss:1.13991
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[462]	train-mlogloss:1.10270	validation-mlogloss:1.13965
[463]	train-mlogloss:1.10254	validation-mlogloss:1.13954
[464]	train-mlogloss:1.10237	validation-mlogloss:1.13942
[465]	train-mlogloss:1.10210	validation-mlogloss:1.13931
[466]	train-mlogloss:1.10196	validation-mlogloss:1.13923
[467]	train-mlogloss:1.10177	validation-mlogloss:1.13909
[468]	train-mlogloss:1.10158	validation-mlogloss:1.13894
[469]	train-mlogloss:1.10136	validation-mlogloss:1.13873
[470]	train-mlogloss:1.10129	validation-mlogloss:1.13868
[471]	train-mlogloss:1.10109	validation-mlogloss:1.13854
[472]	train-mlogloss:1.10100	validation-mlogloss:1.13844
[473]	train-mlogloss:1.10084	validation-mlogloss:1.13832
[474]	train-mlogloss:1.10079	validation-mlogloss:1.13828
[475]	train-mlogloss:1.10063	validation-mlogloss:1.13820
[476]	train-mlogloss:1.10054	validation-mlogloss:1.13809
[477]	train-mlogloss:1.10035	validation-mlogloss:1.13791
[478]	train-mlogloss:1.10013	validation-mlogloss:1.13771
[479]	train-mlogloss:1.09987	validation-mlogloss:1.13758
[480]	train-mlogloss:1.09970	validation-mlogloss:1.13744
[481]	train-mlogloss:1.09957	validation-mlogloss:1.13735
[482]	train-mlogloss:1.09952	validation-mlogloss:1.13731
[483]	train-mlogloss:1.09941	validation-mlogloss:1.13723
[484]	train-mlogloss:1.09932	validation-mlogloss:1.13718
[485]	train-mlogloss:1.09926	validation-mlogloss:1.13714
[486]	train-mlogloss:1.09908	validation-mlogloss:1.13705
[487]	train-mlogloss:1.09898	validation-mlogloss:1.13700
[488]	train-mlogloss:1.09872	validation-mlogloss:1.13682
[489]	train-mlogloss:1.09853	validation-mlogloss:1.13675
Saved model to disk.
2025-10-25 21:42:45.226795: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-25 21:42:45.242697: 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 00:00:1761421365.261112 1977019 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761421365.266872 1977019 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761421365.280783 1977019 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761421365.280897 1977019 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761421365.280966 1977019 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761421365.281027 1977019 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-25 21:42:45.285187: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67393	validation-mlogloss:2.67461
[1]	train-mlogloss:2.64257	validation-mlogloss:2.64335
[2]	train-mlogloss:2.61150	validation-mlogloss:2.61389
[3]	train-mlogloss:2.58339	validation-mlogloss:2.58628
[4]	train-mlogloss:2.55579	validation-mlogloss:2.55964
[5]	train-mlogloss:2.52882	validation-mlogloss:2.53390
[6]	train-mlogloss:2.50357	validation-mlogloss:2.50803
[7]	train-mlogloss:2.47827	validation-mlogloss:2.48360
[8]	train-mlogloss:2.45436	validation-mlogloss:2.45967
[9]	train-mlogloss:2.43092	validation-mlogloss:2.43678
[10]	train-mlogloss:2.40796	validation-mlogloss:2.41403
[11]	train-mlogloss:2.38548	validation-mlogloss:2.39172
[12]	train-mlogloss:2.36440	validation-mlogloss:2.37017
[13]	train-mlogloss:2.34351	validation-mlogloss:2.34896
[14]	train-mlogloss:2.32246	validation-mlogloss:2.32733
[15]	train-mlogloss:2.30393	validation-mlogloss:2.30977
[16]	train-mlogloss:2.28422	validation-mlogloss:2.29000
[17]	train-mlogloss:2.26612	validation-mlogloss:2.27261
[18]	train-mlogloss:2.24775	validation-mlogloss:2.25439
[19]	train-mlogloss:2.23041	validation-mlogloss:2.23693
[20]	train-mlogloss:2.21305	validation-mlogloss:2.22001
[21]	train-mlogloss:2.19620	validation-mlogloss:2.20327
[22]	train-mlogloss:2.17987	validation-mlogloss:2.18682
[23]	train-mlogloss:2.16401	validation-mlogloss:2.17104
[24]	train-mlogloss:2.14837	validation-mlogloss:2.15512
[25]	train-mlogloss:2.13281	validation-mlogloss:2.13985
[26]	train-mlogloss:2.11757	validation-mlogloss:2.12490
[27]	train-mlogloss:2.10302	validation-mlogloss:2.11063
[28]	train-mlogloss:2.08851	validation-mlogloss:2.09613
[29]	train-mlogloss:2.07452	validation-mlogloss:2.08211
[30]	train-mlogloss:2.06116	validation-mlogloss:2.06899
[31]	train-mlogloss:2.04730	validation-mlogloss:2.05451
[32]	train-mlogloss:2.03427	validation-mlogloss:2.04149
[33]	train-mlogloss:2.02155	validation-mlogloss:2.02883
[34]	train-mlogloss:2.00887	validation-mlogloss:2.01635
[35]	train-mlogloss:1.99737	validation-mlogloss:2.00510
[36]	train-mlogloss:1.98507	validation-mlogloss:1.99293
[37]	train-mlogloss:1.97358	validation-mlogloss:1.98149
[38]	train-mlogloss:1.96162	validation-mlogloss:1.96951
[39]	train-mlogloss:1.95070	validation-mlogloss:1.95905
[40]	train-mlogloss:1.93969	validation-mlogloss:1.94795
[41]	train-mlogloss:1.92869	validation-mlogloss:1.93695
[42]	train-mlogloss:1.91838	validation-mlogloss:1.92650
[43]	train-mlogloss:1.90724	validation-mlogloss:1.91480
[44]	train-mlogloss:1.89695	validation-mlogloss:1.90478
[45]	train-mlogloss:1.88647	validation-mlogloss:1.89461
[46]	train-mlogloss:1.87645	validation-mlogloss:1.88469
[47]	train-mlogloss:1.86695	validation-mlogloss:1.87497
[48]	train-mlogloss:1.85752	validation-mlogloss:1.86586
[49]	train-mlogloss:1.84818	validation-mlogloss:1.85653
[50]	train-mlogloss:1.83902	validation-mlogloss:1.84726
[51]	train-mlogloss:1.82970	validation-mlogloss:1.83801
[52]	train-mlogloss:1.82067	validation-mlogloss:1.82909
[53]	train-mlogloss:1.81137	validation-mlogloss:1.81940
[54]	train-mlogloss:1.80252	validation-mlogloss:1.81062
[55]	train-mlogloss:1.79403	validation-mlogloss:1.80200
[56]	train-mlogloss:1.78581	validation-mlogloss:1.79362
[57]	train-mlogloss:1.77744	validation-mlogloss:1.78529
[58]	train-mlogloss:1.76887	validation-mlogloss:1.77650
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[440]	train-mlogloss:1.10611	validation-mlogloss:1.13993
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[458]	train-mlogloss:1.10319	validation-mlogloss:1.13771
[459]	train-mlogloss:1.10300	validation-mlogloss:1.13757
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[462]	train-mlogloss:1.10240	validation-mlogloss:1.13727
[463]	train-mlogloss:1.10212	validation-mlogloss:1.13709
[464]	train-mlogloss:1.10194	validation-mlogloss:1.13692
[465]	train-mlogloss:1.10182	validation-mlogloss:1.13687
[466]	train-mlogloss:1.10182	validation-mlogloss:1.13687
[467]	train-mlogloss:1.10171	validation-mlogloss:1.13674
[468]	train-mlogloss:1.10159	validation-mlogloss:1.13669
[469]	train-mlogloss:1.10148	validation-mlogloss:1.13659
[470]	train-mlogloss:1.10131	validation-mlogloss:1.13654
[471]	train-mlogloss:1.10118	validation-mlogloss:1.13643
[472]	train-mlogloss:1.10104	validation-mlogloss:1.13627
[473]	train-mlogloss:1.10093	validation-mlogloss:1.13616
[474]	train-mlogloss:1.10079	validation-mlogloss:1.13607
[475]	train-mlogloss:1.10069	validation-mlogloss:1.13596
[476]	train-mlogloss:1.10051	validation-mlogloss:1.13588
[477]	train-mlogloss:1.10044	validation-mlogloss:1.13579
[478]	train-mlogloss:1.10042	validation-mlogloss:1.13577
[479]	train-mlogloss:1.10025	validation-mlogloss:1.13558
[480]	train-mlogloss:1.10012	validation-mlogloss:1.13546
[481]	train-mlogloss:1.09995	validation-mlogloss:1.13538
[482]	train-mlogloss:1.09990	validation-mlogloss:1.13531
[483]	train-mlogloss:1.09972	validation-mlogloss:1.13520
[484]	train-mlogloss:1.09963	validation-mlogloss:1.13514
[485]	train-mlogloss:1.09954	validation-mlogloss:1.13506
[486]	train-mlogloss:1.09935	validation-mlogloss:1.13491
[487]	train-mlogloss:1.09921	validation-mlogloss:1.13476
[488]	train-mlogloss:1.09917	validation-mlogloss:1.13474
[489]	train-mlogloss:1.09913	validation-mlogloss:1.13471
Saved model to disk.
2025-10-25 23:02:16.702635: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-25 23:02:16.747440: 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 00:00:1761426136.797351 2055649 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761426136.810475 2055649 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761426136.835807 2055649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761426136.835935 2055649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761426136.835990 2055649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761426136.836039 2055649 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-25 23:02:16.843977: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[1]	train-mlogloss:2.64288	validation-mlogloss:2.64388
[2]	train-mlogloss:2.61211	validation-mlogloss:2.61445
[3]	train-mlogloss:2.58237	validation-mlogloss:2.58562
[4]	train-mlogloss:2.55413	validation-mlogloss:2.55766
[5]	train-mlogloss:2.52706	validation-mlogloss:2.53156
[6]	train-mlogloss:2.50132	validation-mlogloss:2.50675
[7]	train-mlogloss:2.47619	validation-mlogloss:2.48142
[8]	train-mlogloss:2.45251	validation-mlogloss:2.45747
[9]	train-mlogloss:2.42915	validation-mlogloss:2.43451
[10]	train-mlogloss:2.40693	validation-mlogloss:2.41314
[11]	train-mlogloss:2.38431	validation-mlogloss:2.39111
[12]	train-mlogloss:2.36303	validation-mlogloss:2.36998
[13]	train-mlogloss:2.34234	validation-mlogloss:2.35004
[14]	train-mlogloss:2.32129	validation-mlogloss:2.32948
[15]	train-mlogloss:2.30213	validation-mlogloss:2.31095
[16]	train-mlogloss:2.28292	validation-mlogloss:2.29231
[17]	train-mlogloss:2.26484	validation-mlogloss:2.27409
[18]	train-mlogloss:2.24653	validation-mlogloss:2.25680
[19]	train-mlogloss:2.22804	validation-mlogloss:2.23827
[20]	train-mlogloss:2.21040	validation-mlogloss:2.22126
[21]	train-mlogloss:2.19409	validation-mlogloss:2.20542
[22]	train-mlogloss:2.17739	validation-mlogloss:2.18879
[23]	train-mlogloss:2.16108	validation-mlogloss:2.17243
[24]	train-mlogloss:2.14591	validation-mlogloss:2.15711
[25]	train-mlogloss:2.13118	validation-mlogloss:2.14302
[26]	train-mlogloss:2.11652	validation-mlogloss:2.12907
[27]	train-mlogloss:2.10267	validation-mlogloss:2.11556
[28]	train-mlogloss:2.08869	validation-mlogloss:2.10164
[29]	train-mlogloss:2.07446	validation-mlogloss:2.08735
[30]	train-mlogloss:2.06089	validation-mlogloss:2.07336
[31]	train-mlogloss:2.04680	validation-mlogloss:2.05925
[32]	train-mlogloss:2.03372	validation-mlogloss:2.04626
[33]	train-mlogloss:2.02045	validation-mlogloss:2.03321
[34]	train-mlogloss:2.00817	validation-mlogloss:2.02119
[35]	train-mlogloss:1.99617	validation-mlogloss:2.00917
[36]	train-mlogloss:1.98375	validation-mlogloss:1.99653
[37]	train-mlogloss:1.97156	validation-mlogloss:1.98396
[38]	train-mlogloss:1.96005	validation-mlogloss:1.97247
[39]	train-mlogloss:1.94884	validation-mlogloss:1.96132
[40]	train-mlogloss:1.93825	validation-mlogloss:1.95062
[41]	train-mlogloss:1.92741	validation-mlogloss:1.93977
[42]	train-mlogloss:1.91691	validation-mlogloss:1.92976
[43]	train-mlogloss:1.90650	validation-mlogloss:1.91923
[44]	train-mlogloss:1.89642	validation-mlogloss:1.90932
[45]	train-mlogloss:1.88628	validation-mlogloss:1.89894
[46]	train-mlogloss:1.87619	validation-mlogloss:1.88896
[47]	train-mlogloss:1.86679	validation-mlogloss:1.87973
[48]	train-mlogloss:1.85733	validation-mlogloss:1.87044
[49]	train-mlogloss:1.84788	validation-mlogloss:1.86105
[50]	train-mlogloss:1.83839	validation-mlogloss:1.85155
[51]	train-mlogloss:1.82956	validation-mlogloss:1.84286
[52]	train-mlogloss:1.82056	validation-mlogloss:1.83365
[53]	train-mlogloss:1.81139	validation-mlogloss:1.82441
[54]	train-mlogloss:1.80273	validation-mlogloss:1.81590
[55]	train-mlogloss:1.79425	validation-mlogloss:1.80726
[56]	train-mlogloss:1.78611	validation-mlogloss:1.79919
[57]	train-mlogloss:1.77781	validation-mlogloss:1.79102
[58]	train-mlogloss:1.76989	validation-mlogloss:1.78307
[59]	train-mlogloss:1.76193	validation-mlogloss:1.77507
[60]	train-mlogloss:1.75375	validation-mlogloss:1.76669
[61]	train-mlogloss:1.74593	validation-mlogloss:1.75878
[62]	train-mlogloss:1.73809	validation-mlogloss:1.75090
[63]	train-mlogloss:1.73072	validation-mlogloss:1.74370
[64]	train-mlogloss:1.72334	validation-mlogloss:1.73634
[65]	train-mlogloss:1.71600	validation-mlogloss:1.72880
[66]	train-mlogloss:1.70903	validation-mlogloss:1.72189
[67]	train-mlogloss:1.70198	validation-mlogloss:1.71481
[68]	train-mlogloss:1.69468	validation-mlogloss:1.70715
[69]	train-mlogloss:1.68803	validation-mlogloss:1.70055
[70]	train-mlogloss:1.68090	validation-mlogloss:1.69298
[71]	train-mlogloss:1.67401	validation-mlogloss:1.68587
[72]	train-mlogloss:1.66754	validation-mlogloss:1.67944
[73]	train-mlogloss:1.66104	validation-mlogloss:1.67310
[74]	train-mlogloss:1.65489	validation-mlogloss:1.66687
[75]	train-mlogloss:1.64840	validation-mlogloss:1.66055
[76]	train-mlogloss:1.64222	validation-mlogloss:1.65454
[77]	train-mlogloss:1.63632	validation-mlogloss:1.64854
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[85]	train-mlogloss:1.59114	validation-mlogloss:1.60294
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[90]	train-mlogloss:1.56557	validation-mlogloss:1.57688
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[94]	train-mlogloss:1.54599	validation-mlogloss:1.55661
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[103]	train-mlogloss:1.50650	validation-mlogloss:1.51747
[104]	train-mlogloss:1.50239	validation-mlogloss:1.51338
[105]	train-mlogloss:1.49834	validation-mlogloss:1.50919
[106]	train-mlogloss:1.49435	validation-mlogloss:1.50523
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[110]	train-mlogloss:1.47833	validation-mlogloss:1.48907
[111]	train-mlogloss:1.47448	validation-mlogloss:1.48498
[112]	train-mlogloss:1.47083	validation-mlogloss:1.48135
[113]	train-mlogloss:1.46720	validation-mlogloss:1.47778
[114]	train-mlogloss:1.46352	validation-mlogloss:1.47410
[115]	train-mlogloss:1.45991	validation-mlogloss:1.47047
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[117]	train-mlogloss:1.45274	validation-mlogloss:1.46325
[118]	train-mlogloss:1.44918	validation-mlogloss:1.45967
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[121]	train-mlogloss:1.43901	validation-mlogloss:1.44952
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[123]	train-mlogloss:1.43242	validation-mlogloss:1.44284
[124]	train-mlogloss:1.42906	validation-mlogloss:1.43956
[125]	train-mlogloss:1.42593	validation-mlogloss:1.43645
[126]	train-mlogloss:1.42274	validation-mlogloss:1.43312
[127]	train-mlogloss:1.41952	validation-mlogloss:1.42980
[128]	train-mlogloss:1.41655	validation-mlogloss:1.42679
[129]	train-mlogloss:1.41330	validation-mlogloss:1.42359
[130]	train-mlogloss:1.41030	validation-mlogloss:1.42041
[131]	train-mlogloss:1.40709	validation-mlogloss:1.41717
[132]	train-mlogloss:1.40428	validation-mlogloss:1.41433
[133]	train-mlogloss:1.40139	validation-mlogloss:1.41140
[134]	train-mlogloss:1.39845	validation-mlogloss:1.40850
[135]	train-mlogloss:1.39547	validation-mlogloss:1.40548
[136]	train-mlogloss:1.39255	validation-mlogloss:1.40246
[137]	train-mlogloss:1.38977	validation-mlogloss:1.39959
[138]	train-mlogloss:1.38686	validation-mlogloss:1.39678
[139]	train-mlogloss:1.38402	validation-mlogloss:1.39389
[140]	train-mlogloss:1.38133	validation-mlogloss:1.39113
[141]	train-mlogloss:1.37853	validation-mlogloss:1.38821
[142]	train-mlogloss:1.37582	validation-mlogloss:1.38560
[143]	train-mlogloss:1.37314	validation-mlogloss:1.38281
[144]	train-mlogloss:1.37056	validation-mlogloss:1.38011
[145]	train-mlogloss:1.36782	validation-mlogloss:1.37736
[146]	train-mlogloss:1.36529	validation-mlogloss:1.37467
[147]	train-mlogloss:1.36287	validation-mlogloss:1.37227
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[451]	train-mlogloss:1.10492	validation-mlogloss:1.13973
[452]	train-mlogloss:1.10475	validation-mlogloss:1.13965
[453]	train-mlogloss:1.10464	validation-mlogloss:1.13955
[454]	train-mlogloss:1.10448	validation-mlogloss:1.13943
[455]	train-mlogloss:1.10432	validation-mlogloss:1.13929
[456]	train-mlogloss:1.10418	validation-mlogloss:1.13917
[457]	train-mlogloss:1.10399	validation-mlogloss:1.13899
[458]	train-mlogloss:1.10364	validation-mlogloss:1.13868
[459]	train-mlogloss:1.10357	validation-mlogloss:1.13865
[460]	train-mlogloss:1.10345	validation-mlogloss:1.13852
[461]	train-mlogloss:1.10307	validation-mlogloss:1.13826
[462]	train-mlogloss:1.10283	validation-mlogloss:1.13810
[463]	train-mlogloss:1.10272	validation-mlogloss:1.13802
[464]	train-mlogloss:1.10255	validation-mlogloss:1.13793
[465]	train-mlogloss:1.10247	validation-mlogloss:1.13788
[466]	train-mlogloss:1.10238	validation-mlogloss:1.13780
[467]	train-mlogloss:1.10226	validation-mlogloss:1.13771
[468]	train-mlogloss:1.10213	validation-mlogloss:1.13760
[469]	train-mlogloss:1.10196	validation-mlogloss:1.13741
[470]	train-mlogloss:1.10194	validation-mlogloss:1.13742
[471]	train-mlogloss:1.10185	validation-mlogloss:1.13734
[472]	train-mlogloss:1.10171	validation-mlogloss:1.13719
[473]	train-mlogloss:1.10140	validation-mlogloss:1.13694
[474]	train-mlogloss:1.10128	validation-mlogloss:1.13683
[475]	train-mlogloss:1.10107	validation-mlogloss:1.13665
[476]	train-mlogloss:1.10088	validation-mlogloss:1.13654
[477]	train-mlogloss:1.10077	validation-mlogloss:1.13647
[478]	train-mlogloss:1.10056	validation-mlogloss:1.13630
[479]	train-mlogloss:1.10042	validation-mlogloss:1.13619
[480]	train-mlogloss:1.10035	validation-mlogloss:1.13614
[481]	train-mlogloss:1.10019	validation-mlogloss:1.13604
[482]	train-mlogloss:1.10008	validation-mlogloss:1.13595
[483]	train-mlogloss:1.10005	validation-mlogloss:1.13593
[484]	train-mlogloss:1.09982	validation-mlogloss:1.13580
[485]	train-mlogloss:1.09963	validation-mlogloss:1.13567
[486]	train-mlogloss:1.09952	validation-mlogloss:1.13555
[487]	train-mlogloss:1.09945	validation-mlogloss:1.13554
[488]	train-mlogloss:1.09945	validation-mlogloss:1.13554
[489]	train-mlogloss:1.09930	validation-mlogloss:1.13546
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.66 [%]
Global accuracy score (test) = 62.82 [%]
Global F1 score (train) = 66.47 [%]
Global F1 score (test) = 62.07 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.50      0.42      0.46       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.57      0.46       161
       CAMINAR USUAL SPEED       0.45      0.29      0.35       161
            CAMINAR ZIGZAG       0.72      0.58      0.64       161
          DE PIE BARRIENDO       0.72      0.61      0.66       161
   DE PIE DOBLANDO TOALLAS       0.43      0.53      0.48       161
    DE PIE MOVIENDO LIBROS       0.50      0.45      0.48       161
          DE PIE USANDO PC       0.84      0.94      0.89       161
        FASE REPOSO CON K5       0.77      0.92      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.62      0.37      0.47       161
         SENTADO USANDO PC       0.47      0.62      0.53       161
      SENTADO VIENDO LA TV       0.50      0.34      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 4: 62.82 [%]
F1-score capturado en la ejecución 4: 62.07 [%]

=== EJECUCIÓN 5 ===

--- TRAIN (ejecución 5) ---

--- TEST (ejecución 5) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  6  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.78 [%]
Global accuracy score (test) = 63.69 [%]
Global F1 score (train) = 66.59 [%]
Global F1 score (test) = 62.94 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.50      0.38      0.43       161
 CAMINAR CON MÓVIL O LIBRO       0.43      0.59      0.50       161
       CAMINAR USUAL SPEED       0.43      0.34      0.38       161
            CAMINAR ZIGZAG       0.72      0.58      0.64       161
          DE PIE BARRIENDO       0.75      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.46      0.58      0.52       161
    DE PIE MOVIENDO LIBROS       0.53      0.48      0.51       161
          DE PIE USANDO PC       0.83      0.93      0.88       161
        FASE REPOSO CON K5       0.77      0.93      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.61      0.39      0.48       161
         SENTADO USANDO PC       0.47      0.62      0.53       161
      SENTADO VIENDO LA TV       0.53      0.33      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.64      2415
                 macro avg       0.64      0.64      0.63      2415
              weighted avg       0.64      0.64      0.63      2415


Accuracy capturado en la ejecución 5: 63.69 [%]
F1-score capturado en la ejecución 5: 62.94 [%]

=== EJECUCIÓN 6 ===

--- TRAIN (ejecución 6) ---

--- TEST (ejecución 6) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.45 [%]
Global accuracy score (test) = 63.6 [%]
Global F1 score (train) = 66.27 [%]
Global F1 score (test) = 62.82 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.43      0.48       161
 CAMINAR CON MÓVIL O LIBRO       0.43      0.61      0.50       161
       CAMINAR USUAL SPEED       0.42      0.29      0.34       161
            CAMINAR ZIGZAG       0.73      0.58      0.65       161
          DE PIE BARRIENDO       0.73      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.43      0.54      0.48       161
    DE PIE MOVIENDO LIBROS       0.53      0.45      0.49       161
          DE PIE USANDO PC       0.82      0.94      0.87       161
        FASE REPOSO CON K5       0.78      0.93      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.59      0.40      0.47       161
         SENTADO USANDO PC       0.48      0.62      0.54       161
      SENTADO VIENDO LA TV       0.52      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.65      0.94      0.77       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.64      2415
                 macro avg       0.64      0.64      0.63      2415
              weighted avg       0.64      0.64      0.63      2415


Accuracy capturado en la ejecución 6: 63.6 [%]
F1-score capturado en la ejecución 6: 62.82 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-10-26 00:22:18.024098: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 00:22:18.040633: 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 00:00:1761430938.060209 2134714 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761430938.066344 2134714 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761430938.080681 2134714 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761430938.080804 2134714 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761430938.080850 2134714 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761430938.080892 2134714 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 00:22:18.085325: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67650	validation-mlogloss:2.67675
[1]	train-mlogloss:2.64399	validation-mlogloss:2.64352
[2]	train-mlogloss:2.61403	validation-mlogloss:2.61430
[3]	train-mlogloss:2.58404	validation-mlogloss:2.58447
[4]	train-mlogloss:2.55524	validation-mlogloss:2.55634
[5]	train-mlogloss:2.52749	validation-mlogloss:2.52872
[6]	train-mlogloss:2.50332	validation-mlogloss:2.50581
[7]	train-mlogloss:2.47854	validation-mlogloss:2.48119
[8]	train-mlogloss:2.45498	validation-mlogloss:2.45843
[9]	train-mlogloss:2.43163	validation-mlogloss:2.43635
[10]	train-mlogloss:2.40748	validation-mlogloss:2.41221
[11]	train-mlogloss:2.38588	validation-mlogloss:2.39156
[12]	train-mlogloss:2.36411	validation-mlogloss:2.37006
[13]	train-mlogloss:2.34276	validation-mlogloss:2.34921
[14]	train-mlogloss:2.32236	validation-mlogloss:2.32898
[15]	train-mlogloss:2.30371	validation-mlogloss:2.31028
[16]	train-mlogloss:2.28419	validation-mlogloss:2.29095
[17]	train-mlogloss:2.26516	validation-mlogloss:2.27246
[18]	train-mlogloss:2.24754	validation-mlogloss:2.25578
[19]	train-mlogloss:2.23003	validation-mlogloss:2.23868
[20]	train-mlogloss:2.21291	validation-mlogloss:2.22183
[21]	train-mlogloss:2.19622	validation-mlogloss:2.20621
[22]	train-mlogloss:2.17983	validation-mlogloss:2.18985
[23]	train-mlogloss:2.16376	validation-mlogloss:2.17392
[24]	train-mlogloss:2.14884	validation-mlogloss:2.15952
[25]	train-mlogloss:2.13396	validation-mlogloss:2.14531
[26]	train-mlogloss:2.11912	validation-mlogloss:2.13009
[27]	train-mlogloss:2.10359	validation-mlogloss:2.11466
[28]	train-mlogloss:2.08916	validation-mlogloss:2.10008
[29]	train-mlogloss:2.07511	validation-mlogloss:2.08591
[30]	train-mlogloss:2.06113	validation-mlogloss:2.07190
[31]	train-mlogloss:2.04748	validation-mlogloss:2.05829
[32]	train-mlogloss:2.03455	validation-mlogloss:2.04565
[33]	train-mlogloss:2.02197	validation-mlogloss:2.03339
[34]	train-mlogloss:2.00974	validation-mlogloss:2.02139
[35]	train-mlogloss:1.99736	validation-mlogloss:2.00973
[36]	train-mlogloss:1.98512	validation-mlogloss:1.99722
[37]	train-mlogloss:1.97346	validation-mlogloss:1.98517
[38]	train-mlogloss:1.96207	validation-mlogloss:1.97342
[39]	train-mlogloss:1.95057	validation-mlogloss:1.96221
[40]	train-mlogloss:1.93910	validation-mlogloss:1.95052
[41]	train-mlogloss:1.92845	validation-mlogloss:1.93985
[42]	train-mlogloss:1.91765	validation-mlogloss:1.92882
[43]	train-mlogloss:1.90694	validation-mlogloss:1.91792
[44]	train-mlogloss:1.89631	validation-mlogloss:1.90683
[45]	train-mlogloss:1.88644	validation-mlogloss:1.89708
[46]	train-mlogloss:1.87665	validation-mlogloss:1.88677
[47]	train-mlogloss:1.86690	validation-mlogloss:1.87703
[48]	train-mlogloss:1.85685	validation-mlogloss:1.86709
[49]	train-mlogloss:1.84748	validation-mlogloss:1.85789
[50]	train-mlogloss:1.83834	validation-mlogloss:1.84884
[51]	train-mlogloss:1.82912	validation-mlogloss:1.83953
[52]	train-mlogloss:1.82040	validation-mlogloss:1.83073
[53]	train-mlogloss:1.81142	validation-mlogloss:1.82187
[54]	train-mlogloss:1.80249	validation-mlogloss:1.81253
[55]	train-mlogloss:1.79396	validation-mlogloss:1.80397
[56]	train-mlogloss:1.78519	validation-mlogloss:1.79524
[57]	train-mlogloss:1.77658	validation-mlogloss:1.78617
[58]	train-mlogloss:1.76864	validation-mlogloss:1.77809
[59]	train-mlogloss:1.76036	validation-mlogloss:1.76992
[60]	train-mlogloss:1.75209	validation-mlogloss:1.76152
[61]	train-mlogloss:1.74447	validation-mlogloss:1.75380
[62]	train-mlogloss:1.73702	validation-mlogloss:1.74655
[63]	train-mlogloss:1.72967	validation-mlogloss:1.73918
[64]	train-mlogloss:1.72251	validation-mlogloss:1.73197
[65]	train-mlogloss:1.71539	validation-mlogloss:1.72473
[66]	train-mlogloss:1.70798	validation-mlogloss:1.71706
[67]	train-mlogloss:1.70058	validation-mlogloss:1.70959
[68]	train-mlogloss:1.69356	validation-mlogloss:1.70244
[69]	train-mlogloss:1.68695	validation-mlogloss:1.69583
[70]	train-mlogloss:1.68033	validation-mlogloss:1.68912
[71]	train-mlogloss:1.67377	validation-mlogloss:1.68255
[72]	train-mlogloss:1.66746	validation-mlogloss:1.67599
[73]	train-mlogloss:1.66118	validation-mlogloss:1.66985
[74]	train-mlogloss:1.65456	validation-mlogloss:1.66298
[75]	train-mlogloss:1.64828	validation-mlogloss:1.65651
[76]	train-mlogloss:1.64210	validation-mlogloss:1.65026
[77]	train-mlogloss:1.63628	validation-mlogloss:1.64434
[78]	train-mlogloss:1.63035	validation-mlogloss:1.63854
[79]	train-mlogloss:1.62456	validation-mlogloss:1.63244
[80]	train-mlogloss:1.61885	validation-mlogloss:1.62689
[81]	train-mlogloss:1.61326	validation-mlogloss:1.62127
[82]	train-mlogloss:1.60771	validation-mlogloss:1.61559
[83]	train-mlogloss:1.60245	validation-mlogloss:1.61020
[84]	train-mlogloss:1.59687	validation-mlogloss:1.60448
[85]	train-mlogloss:1.59150	validation-mlogloss:1.59888
[86]	train-mlogloss:1.58607	validation-mlogloss:1.59329
[87]	train-mlogloss:1.58085	validation-mlogloss:1.58781
[88]	train-mlogloss:1.57540	validation-mlogloss:1.58235
[89]	train-mlogloss:1.57043	validation-mlogloss:1.57732
[90]	train-mlogloss:1.56519	validation-mlogloss:1.57155
[91]	train-mlogloss:1.56011	validation-mlogloss:1.56626
[92]	train-mlogloss:1.55525	validation-mlogloss:1.56118
[93]	train-mlogloss:1.55053	validation-mlogloss:1.55675
[94]	train-mlogloss:1.54569	validation-mlogloss:1.55177
[95]	train-mlogloss:1.54111	validation-mlogloss:1.54713
[96]	train-mlogloss:1.53640	validation-mlogloss:1.54244
[97]	train-mlogloss:1.53198	validation-mlogloss:1.53799
[98]	train-mlogloss:1.52743	validation-mlogloss:1.53348
[99]	train-mlogloss:1.52289	validation-mlogloss:1.52899
[100]	train-mlogloss:1.51849	validation-mlogloss:1.52474
[101]	train-mlogloss:1.51435	validation-mlogloss:1.52071
[102]	train-mlogloss:1.51028	validation-mlogloss:1.51668
[103]	train-mlogloss:1.50612	validation-mlogloss:1.51252
[104]	train-mlogloss:1.50202	validation-mlogloss:1.50849
[105]	train-mlogloss:1.49795	validation-mlogloss:1.50438
[106]	train-mlogloss:1.49408	validation-mlogloss:1.50042
[107]	train-mlogloss:1.48988	validation-mlogloss:1.49635
[108]	train-mlogloss:1.48584	validation-mlogloss:1.49220
[109]	train-mlogloss:1.48228	validation-mlogloss:1.48894
[110]	train-mlogloss:1.47836	validation-mlogloss:1.48498
[111]	train-mlogloss:1.47472	validation-mlogloss:1.48134
[112]	train-mlogloss:1.47122	validation-mlogloss:1.47781
[113]	train-mlogloss:1.46758	validation-mlogloss:1.47422
[114]	train-mlogloss:1.46391	validation-mlogloss:1.47040
[115]	train-mlogloss:1.46042	validation-mlogloss:1.46686
[116]	train-mlogloss:1.45690	validation-mlogloss:1.46349
[117]	train-mlogloss:1.45322	validation-mlogloss:1.45986
[118]	train-mlogloss:1.44971	validation-mlogloss:1.45632
[119]	train-mlogloss:1.44636	validation-mlogloss:1.45303
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[423]	train-mlogloss:1.10966	validation-mlogloss:1.14300
[424]	train-mlogloss:1.10935	validation-mlogloss:1.14284
[425]	train-mlogloss:1.10909	validation-mlogloss:1.14275
[426]	train-mlogloss:1.10889	validation-mlogloss:1.14269
[427]	train-mlogloss:1.10875	validation-mlogloss:1.14259
[428]	train-mlogloss:1.10866	validation-mlogloss:1.14247
[429]	train-mlogloss:1.10843	validation-mlogloss:1.14233
[430]	train-mlogloss:1.10827	validation-mlogloss:1.14219
[431]	train-mlogloss:1.10808	validation-mlogloss:1.14210
[432]	train-mlogloss:1.10788	validation-mlogloss:1.14198
[433]	train-mlogloss:1.10768	validation-mlogloss:1.14175
[434]	train-mlogloss:1.10748	validation-mlogloss:1.14163
[435]	train-mlogloss:1.10736	validation-mlogloss:1.14154
[436]	train-mlogloss:1.10722	validation-mlogloss:1.14144
[437]	train-mlogloss:1.10706	validation-mlogloss:1.14130
[438]	train-mlogloss:1.10698	validation-mlogloss:1.14123
[439]	train-mlogloss:1.10684	validation-mlogloss:1.14116
[440]	train-mlogloss:1.10663	validation-mlogloss:1.14101
[441]	train-mlogloss:1.10655	validation-mlogloss:1.14093
[442]	train-mlogloss:1.10637	validation-mlogloss:1.14075
[443]	train-mlogloss:1.10618	validation-mlogloss:1.14063
[444]	train-mlogloss:1.10593	validation-mlogloss:1.14047
[445]	train-mlogloss:1.10576	validation-mlogloss:1.14040
[446]	train-mlogloss:1.10545	validation-mlogloss:1.14022
[447]	train-mlogloss:1.10528	validation-mlogloss:1.14009
[448]	train-mlogloss:1.10518	validation-mlogloss:1.14000
[449]	train-mlogloss:1.10493	validation-mlogloss:1.13979
[450]	train-mlogloss:1.10485	validation-mlogloss:1.13971
[451]	train-mlogloss:1.10456	validation-mlogloss:1.13954
[452]	train-mlogloss:1.10451	validation-mlogloss:1.13950
[453]	train-mlogloss:1.10437	validation-mlogloss:1.13944
[454]	train-mlogloss:1.10419	validation-mlogloss:1.13941
[455]	train-mlogloss:1.10400	validation-mlogloss:1.13933
[456]	train-mlogloss:1.10387	validation-mlogloss:1.13924
[457]	train-mlogloss:1.10373	validation-mlogloss:1.13918
[458]	train-mlogloss:1.10357	validation-mlogloss:1.13911
[459]	train-mlogloss:1.10350	validation-mlogloss:1.13909
[460]	train-mlogloss:1.10340	validation-mlogloss:1.13900
[461]	train-mlogloss:1.10314	validation-mlogloss:1.13881
[462]	train-mlogloss:1.10295	validation-mlogloss:1.13867
[463]	train-mlogloss:1.10282	validation-mlogloss:1.13854
[464]	train-mlogloss:1.10262	validation-mlogloss:1.13838
[465]	train-mlogloss:1.10243	validation-mlogloss:1.13822
[466]	train-mlogloss:1.10214	validation-mlogloss:1.13799
[467]	train-mlogloss:1.10196	validation-mlogloss:1.13778
[468]	train-mlogloss:1.10174	validation-mlogloss:1.13759
[469]	train-mlogloss:1.10171	validation-mlogloss:1.13757
[470]	train-mlogloss:1.10162	validation-mlogloss:1.13749
[471]	train-mlogloss:1.10152	validation-mlogloss:1.13743
[472]	train-mlogloss:1.10136	validation-mlogloss:1.13732
[473]	train-mlogloss:1.10118	validation-mlogloss:1.13726
[474]	train-mlogloss:1.10102	validation-mlogloss:1.13715
[475]	train-mlogloss:1.10094	validation-mlogloss:1.13713
[476]	train-mlogloss:1.10084	validation-mlogloss:1.13705
[477]	train-mlogloss:1.10075	validation-mlogloss:1.13699
[478]	train-mlogloss:1.10067	validation-mlogloss:1.13686
[479]	train-mlogloss:1.10055	validation-mlogloss:1.13680
[480]	train-mlogloss:1.10046	validation-mlogloss:1.13674
[481]	train-mlogloss:1.10032	validation-mlogloss:1.13666
[482]	train-mlogloss:1.10018	validation-mlogloss:1.13656
[483]	train-mlogloss:1.10012	validation-mlogloss:1.13652
[484]	train-mlogloss:1.10002	validation-mlogloss:1.13646
[485]	train-mlogloss:1.09978	validation-mlogloss:1.13629
[486]	train-mlogloss:1.09969	validation-mlogloss:1.13631
[487]	train-mlogloss:1.09947	validation-mlogloss:1.13627
[488]	train-mlogloss:1.09936	validation-mlogloss:1.13621
[489]	train-mlogloss:1.09933	validation-mlogloss:1.13617
Saved model to disk.
2025-10-26 01:42:22.495284: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 01:42:22.536585: 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 00:00:1761435742.566881 2213861 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761435742.573602 2213861 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761435742.594914 2213861 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761435742.595037 2213861 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761435742.595099 2213861 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761435742.595156 2213861 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 01:42:22.599242: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67470	validation-mlogloss:2.67573
[1]	train-mlogloss:2.64288	validation-mlogloss:2.64371
[2]	train-mlogloss:2.61174	validation-mlogloss:2.61426
[3]	train-mlogloss:2.58055	validation-mlogloss:2.58357
[4]	train-mlogloss:2.55102	validation-mlogloss:2.55473
[5]	train-mlogloss:2.52429	validation-mlogloss:2.52845
[6]	train-mlogloss:2.49820	validation-mlogloss:2.50300
[7]	train-mlogloss:2.47275	validation-mlogloss:2.47751
[8]	train-mlogloss:2.44722	validation-mlogloss:2.45238
[9]	train-mlogloss:2.42412	validation-mlogloss:2.42973
[10]	train-mlogloss:2.40228	validation-mlogloss:2.40880
[11]	train-mlogloss:2.38032	validation-mlogloss:2.38714
[12]	train-mlogloss:2.35915	validation-mlogloss:2.36615
[13]	train-mlogloss:2.33833	validation-mlogloss:2.34583
[14]	train-mlogloss:2.31708	validation-mlogloss:2.32457
[15]	train-mlogloss:2.29734	validation-mlogloss:2.30517
[16]	train-mlogloss:2.27859	validation-mlogloss:2.28745
[17]	train-mlogloss:2.26025	validation-mlogloss:2.26974
[18]	train-mlogloss:2.24181	validation-mlogloss:2.25182
[19]	train-mlogloss:2.22415	validation-mlogloss:2.23463
[20]	train-mlogloss:2.20632	validation-mlogloss:2.21675
[21]	train-mlogloss:2.18947	validation-mlogloss:2.19974
[22]	train-mlogloss:2.17268	validation-mlogloss:2.18223
[23]	train-mlogloss:2.15700	validation-mlogloss:2.16731
[24]	train-mlogloss:2.14103	validation-mlogloss:2.15128
[25]	train-mlogloss:2.12718	validation-mlogloss:2.13821
[26]	train-mlogloss:2.11221	validation-mlogloss:2.12344
[27]	train-mlogloss:2.09725	validation-mlogloss:2.10820
[28]	train-mlogloss:2.08371	validation-mlogloss:2.09492
[29]	train-mlogloss:2.07027	validation-mlogloss:2.08186
[30]	train-mlogloss:2.05677	validation-mlogloss:2.06869
[31]	train-mlogloss:2.04424	validation-mlogloss:2.05658
[32]	train-mlogloss:2.03133	validation-mlogloss:2.04361
[33]	train-mlogloss:2.01860	validation-mlogloss:2.03072
[34]	train-mlogloss:2.00636	validation-mlogloss:2.01879
[35]	train-mlogloss:1.99451	validation-mlogloss:2.00703
[36]	train-mlogloss:1.98277	validation-mlogloss:1.99497
[37]	train-mlogloss:1.97075	validation-mlogloss:1.98278
[38]	train-mlogloss:1.95885	validation-mlogloss:1.97051
[39]	train-mlogloss:1.94761	validation-mlogloss:1.95920
[40]	train-mlogloss:1.93699	validation-mlogloss:1.94895
[41]	train-mlogloss:1.92604	validation-mlogloss:1.93786
[42]	train-mlogloss:1.91492	validation-mlogloss:1.92678
[43]	train-mlogloss:1.90408	validation-mlogloss:1.91619
[44]	train-mlogloss:1.89378	validation-mlogloss:1.90564
[45]	train-mlogloss:1.88370	validation-mlogloss:1.89601
[46]	train-mlogloss:1.87352	validation-mlogloss:1.88587
[47]	train-mlogloss:1.86352	validation-mlogloss:1.87587
[48]	train-mlogloss:1.85388	validation-mlogloss:1.86587
[49]	train-mlogloss:1.84415	validation-mlogloss:1.85620
[50]	train-mlogloss:1.83486	validation-mlogloss:1.84688
[51]	train-mlogloss:1.82534	validation-mlogloss:1.83749
[52]	train-mlogloss:1.81631	validation-mlogloss:1.82845
[53]	train-mlogloss:1.80745	validation-mlogloss:1.81985
[54]	train-mlogloss:1.79886	validation-mlogloss:1.81111
[55]	train-mlogloss:1.79046	validation-mlogloss:1.80289
[56]	train-mlogloss:1.78212	validation-mlogloss:1.79453
[57]	train-mlogloss:1.77390	validation-mlogloss:1.78635
[58]	train-mlogloss:1.76571	validation-mlogloss:1.77817
[59]	train-mlogloss:1.75801	validation-mlogloss:1.77056
[60]	train-mlogloss:1.75031	validation-mlogloss:1.76289
[61]	train-mlogloss:1.74266	validation-mlogloss:1.75539
[62]	train-mlogloss:1.73525	validation-mlogloss:1.74815
[63]	train-mlogloss:1.72803	validation-mlogloss:1.74083
[64]	train-mlogloss:1.72069	validation-mlogloss:1.73362
[65]	train-mlogloss:1.71368	validation-mlogloss:1.72606
[66]	train-mlogloss:1.70682	validation-mlogloss:1.71914
[67]	train-mlogloss:1.69963	validation-mlogloss:1.71184
[68]	train-mlogloss:1.69312	validation-mlogloss:1.70531
[69]	train-mlogloss:1.68623	validation-mlogloss:1.69841
[70]	train-mlogloss:1.67963	validation-mlogloss:1.69190
[71]	train-mlogloss:1.67307	validation-mlogloss:1.68508
[72]	train-mlogloss:1.66676	validation-mlogloss:1.67851
[73]	train-mlogloss:1.66034	validation-mlogloss:1.67181
[74]	train-mlogloss:1.65415	validation-mlogloss:1.66559
[75]	train-mlogloss:1.64811	validation-mlogloss:1.65931
[76]	train-mlogloss:1.64215	validation-mlogloss:1.65331
[77]	train-mlogloss:1.63603	validation-mlogloss:1.64713
[78]	train-mlogloss:1.62980	validation-mlogloss:1.64089
[79]	train-mlogloss:1.62400	validation-mlogloss:1.63504
[80]	train-mlogloss:1.61812	validation-mlogloss:1.62893
[81]	train-mlogloss:1.61220	validation-mlogloss:1.62312
[82]	train-mlogloss:1.60648	validation-mlogloss:1.61728
[83]	train-mlogloss:1.60089	validation-mlogloss:1.61169
[84]	train-mlogloss:1.59534	validation-mlogloss:1.60592
[85]	train-mlogloss:1.58993	validation-mlogloss:1.60029
[86]	train-mlogloss:1.58478	validation-mlogloss:1.59523
[87]	train-mlogloss:1.57958	validation-mlogloss:1.58991
[88]	train-mlogloss:1.57419	validation-mlogloss:1.58459
[89]	train-mlogloss:1.56903	validation-mlogloss:1.57918
[90]	train-mlogloss:1.56391	validation-mlogloss:1.57406
[91]	train-mlogloss:1.55885	validation-mlogloss:1.56882
[92]	train-mlogloss:1.55401	validation-mlogloss:1.56394
[93]	train-mlogloss:1.54937	validation-mlogloss:1.55914
[94]	train-mlogloss:1.54488	validation-mlogloss:1.55458
[95]	train-mlogloss:1.54022	validation-mlogloss:1.54998
[96]	train-mlogloss:1.53564	validation-mlogloss:1.54554
[97]	train-mlogloss:1.53099	validation-mlogloss:1.54095
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[99]	train-mlogloss:1.52240	validation-mlogloss:1.53226
[100]	train-mlogloss:1.51790	validation-mlogloss:1.52767
[101]	train-mlogloss:1.51367	validation-mlogloss:1.52344
[102]	train-mlogloss:1.50929	validation-mlogloss:1.51924
[103]	train-mlogloss:1.50511	validation-mlogloss:1.51474
[104]	train-mlogloss:1.50105	validation-mlogloss:1.51079
[105]	train-mlogloss:1.49692	validation-mlogloss:1.50677
[106]	train-mlogloss:1.49290	validation-mlogloss:1.50272
[107]	train-mlogloss:1.48908	validation-mlogloss:1.49895
[108]	train-mlogloss:1.48501	validation-mlogloss:1.49482
[109]	train-mlogloss:1.48121	validation-mlogloss:1.49107
[110]	train-mlogloss:1.47760	validation-mlogloss:1.48713
[111]	train-mlogloss:1.47377	validation-mlogloss:1.48328
[112]	train-mlogloss:1.47028	validation-mlogloss:1.47995
[113]	train-mlogloss:1.46651	validation-mlogloss:1.47608
[114]	train-mlogloss:1.46290	validation-mlogloss:1.47244
[115]	train-mlogloss:1.45909	validation-mlogloss:1.46852
[116]	train-mlogloss:1.45553	validation-mlogloss:1.46491
[117]	train-mlogloss:1.45205	validation-mlogloss:1.46154
[118]	train-mlogloss:1.44837	validation-mlogloss:1.45784
[119]	train-mlogloss:1.44506	validation-mlogloss:1.45455
[120]	train-mlogloss:1.44165	validation-mlogloss:1.45110
[121]	train-mlogloss:1.43797	validation-mlogloss:1.44738
[122]	train-mlogloss:1.43480	validation-mlogloss:1.44408
[123]	train-mlogloss:1.43130	validation-mlogloss:1.44065
[124]	train-mlogloss:1.42810	validation-mlogloss:1.43728
[125]	train-mlogloss:1.42494	validation-mlogloss:1.43412
[126]	train-mlogloss:1.42170	validation-mlogloss:1.43085
[127]	train-mlogloss:1.41862	validation-mlogloss:1.42786
[128]	train-mlogloss:1.41538	validation-mlogloss:1.42463
[129]	train-mlogloss:1.41235	validation-mlogloss:1.42167
[130]	train-mlogloss:1.40921	validation-mlogloss:1.41848
[131]	train-mlogloss:1.40628	validation-mlogloss:1.41550
[132]	train-mlogloss:1.40327	validation-mlogloss:1.41244
[133]	train-mlogloss:1.40037	validation-mlogloss:1.40962
[134]	train-mlogloss:1.39719	validation-mlogloss:1.40625
[135]	train-mlogloss:1.39427	validation-mlogloss:1.40318
[136]	train-mlogloss:1.39145	validation-mlogloss:1.40016
[137]	train-mlogloss:1.38863	validation-mlogloss:1.39742
[138]	train-mlogloss:1.38595	validation-mlogloss:1.39479
[139]	train-mlogloss:1.38310	validation-mlogloss:1.39187
[140]	train-mlogloss:1.38020	validation-mlogloss:1.38887
[141]	train-mlogloss:1.37724	validation-mlogloss:1.38589
[142]	train-mlogloss:1.37475	validation-mlogloss:1.38340
[143]	train-mlogloss:1.37216	validation-mlogloss:1.38085
[144]	train-mlogloss:1.36948	validation-mlogloss:1.37823
[145]	train-mlogloss:1.36674	validation-mlogloss:1.37532
[146]	train-mlogloss:1.36406	validation-mlogloss:1.37292
[147]	train-mlogloss:1.36147	validation-mlogloss:1.37039
[148]	train-mlogloss:1.35906	validation-mlogloss:1.36790
[149]	train-mlogloss:1.35650	validation-mlogloss:1.36530
[150]	train-mlogloss:1.35389	validation-mlogloss:1.36286
[151]	train-mlogloss:1.35135	validation-mlogloss:1.36037
[152]	train-mlogloss:1.34916	validation-mlogloss:1.35821
[153]	train-mlogloss:1.34681	validation-mlogloss:1.35578
[154]	train-mlogloss:1.34449	validation-mlogloss:1.35357
[155]	train-mlogloss:1.34195	validation-mlogloss:1.35089
[156]	train-mlogloss:1.33943	validation-mlogloss:1.34836
[157]	train-mlogloss:1.33712	validation-mlogloss:1.34605
[158]	train-mlogloss:1.33484	validation-mlogloss:1.34374
[159]	train-mlogloss:1.33265	validation-mlogloss:1.34153
[160]	train-mlogloss:1.33036	validation-mlogloss:1.33943
[161]	train-mlogloss:1.32811	validation-mlogloss:1.33727
[162]	train-mlogloss:1.32612	validation-mlogloss:1.33556
[163]	train-mlogloss:1.32396	validation-mlogloss:1.33341
[164]	train-mlogloss:1.32172	validation-mlogloss:1.33121
[165]	train-mlogloss:1.31966	validation-mlogloss:1.32908
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[170]	train-mlogloss:1.30930	validation-mlogloss:1.31891
[171]	train-mlogloss:1.30719	validation-mlogloss:1.31683
[172]	train-mlogloss:1.30516	validation-mlogloss:1.31501
[173]	train-mlogloss:1.30301	validation-mlogloss:1.31287
[174]	train-mlogloss:1.30114	validation-mlogloss:1.31105
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[186]	train-mlogloss:1.27892	validation-mlogloss:1.28914
[187]	train-mlogloss:1.27711	validation-mlogloss:1.28754
[188]	train-mlogloss:1.27546	validation-mlogloss:1.28611
[189]	train-mlogloss:1.27368	validation-mlogloss:1.28452
[190]	train-mlogloss:1.27194	validation-mlogloss:1.28282
[191]	train-mlogloss:1.27031	validation-mlogloss:1.28135
[192]	train-mlogloss:1.26869	validation-mlogloss:1.27979
[193]	train-mlogloss:1.26716	validation-mlogloss:1.27856
[194]	train-mlogloss:1.26562	validation-mlogloss:1.27709
[195]	train-mlogloss:1.26387	validation-mlogloss:1.27549
[196]	train-mlogloss:1.26221	validation-mlogloss:1.27398
[197]	train-mlogloss:1.26076	validation-mlogloss:1.27255
[198]	train-mlogloss:1.25917	validation-mlogloss:1.27109
[199]	train-mlogloss:1.25778	validation-mlogloss:1.26993
[200]	train-mlogloss:1.25615	validation-mlogloss:1.26826
[201]	train-mlogloss:1.25466	validation-mlogloss:1.26685
[202]	train-mlogloss:1.25306	validation-mlogloss:1.26526
[203]	train-mlogloss:1.25158	validation-mlogloss:1.26392
[204]	train-mlogloss:1.25013	validation-mlogloss:1.26264
[205]	train-mlogloss:1.24878	validation-mlogloss:1.26129
[206]	train-mlogloss:1.24728	validation-mlogloss:1.25981
[207]	train-mlogloss:1.24576	validation-mlogloss:1.25843
[208]	train-mlogloss:1.24435	validation-mlogloss:1.25707
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[478]	train-mlogloss:1.10110	validation-mlogloss:1.13597
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[485]	train-mlogloss:1.10016	validation-mlogloss:1.13535
[486]	train-mlogloss:1.10002	validation-mlogloss:1.13521
[487]	train-mlogloss:1.09988	validation-mlogloss:1.13508
[488]	train-mlogloss:1.09963	validation-mlogloss:1.13490
[489]	train-mlogloss:1.09955	validation-mlogloss:1.13484
Saved model to disk.
2025-10-26 02:00:51.214392: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 02:00:51.248513: 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 00:00:1761440451.300815 2291711 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761440451.306512 2291711 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761440451.324019 2291711 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761440451.324122 2291711 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761440451.324175 2291711 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761440451.324245 2291711 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 02:00:51.328217: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[300]	train-mlogloss:1.15201	validation-mlogloss:1.17698
[301]	train-mlogloss:1.15138	validation-mlogloss:1.17645
[302]	train-mlogloss:1.15080	validation-mlogloss:1.17592
[303]	train-mlogloss:1.15012	validation-mlogloss:1.17533
[304]	train-mlogloss:1.14957	validation-mlogloss:1.17485
[305]	train-mlogloss:1.14882	validation-mlogloss:1.17419
[306]	train-mlogloss:1.14826	validation-mlogloss:1.17368
[307]	train-mlogloss:1.14758	validation-mlogloss:1.17312
[308]	train-mlogloss:1.14715	validation-mlogloss:1.17273
[309]	train-mlogloss:1.14669	validation-mlogloss:1.17233
[310]	train-mlogloss:1.14614	validation-mlogloss:1.17187
[311]	train-mlogloss:1.14573	validation-mlogloss:1.17157
[312]	train-mlogloss:1.14520	validation-mlogloss:1.17104
[313]	train-mlogloss:1.14471	validation-mlogloss:1.17069
[314]	train-mlogloss:1.14417	validation-mlogloss:1.17032
[315]	train-mlogloss:1.14362	validation-mlogloss:1.16977
[316]	train-mlogloss:1.14301	validation-mlogloss:1.16926
[317]	train-mlogloss:1.14249	validation-mlogloss:1.16891
[318]	train-mlogloss:1.14198	validation-mlogloss:1.16849
[319]	train-mlogloss:1.14144	validation-mlogloss:1.16810
[320]	train-mlogloss:1.14088	validation-mlogloss:1.16763
[321]	train-mlogloss:1.14036	validation-mlogloss:1.16726
[322]	train-mlogloss:1.13969	validation-mlogloss:1.16681
[323]	train-mlogloss:1.13906	validation-mlogloss:1.16622
[324]	train-mlogloss:1.13868	validation-mlogloss:1.16581
[325]	train-mlogloss:1.13809	validation-mlogloss:1.16530
[326]	train-mlogloss:1.13755	validation-mlogloss:1.16487
[327]	train-mlogloss:1.13708	validation-mlogloss:1.16451
[328]	train-mlogloss:1.13666	validation-mlogloss:1.16421
[329]	train-mlogloss:1.13616	validation-mlogloss:1.16376
[330]	train-mlogloss:1.13575	validation-mlogloss:1.16338
[331]	train-mlogloss:1.13525	validation-mlogloss:1.16305
[332]	train-mlogloss:1.13477	validation-mlogloss:1.16270
[333]	train-mlogloss:1.13438	validation-mlogloss:1.16242
[334]	train-mlogloss:1.13388	validation-mlogloss:1.16202
[335]	train-mlogloss:1.13331	validation-mlogloss:1.16170
[336]	train-mlogloss:1.13289	validation-mlogloss:1.16139
[337]	train-mlogloss:1.13226	validation-mlogloss:1.16084
[338]	train-mlogloss:1.13201	validation-mlogloss:1.16067
[339]	train-mlogloss:1.13174	validation-mlogloss:1.16049
[340]	train-mlogloss:1.13142	validation-mlogloss:1.16023
[341]	train-mlogloss:1.13102	validation-mlogloss:1.15995
[342]	train-mlogloss:1.13067	validation-mlogloss:1.15970
[343]	train-mlogloss:1.13031	validation-mlogloss:1.15947
[344]	train-mlogloss:1.13000	validation-mlogloss:1.15927
[345]	train-mlogloss:1.12957	validation-mlogloss:1.15892
[346]	train-mlogloss:1.12913	validation-mlogloss:1.15870
[347]	train-mlogloss:1.12897	validation-mlogloss:1.15856
[348]	train-mlogloss:1.12859	validation-mlogloss:1.15826
[349]	train-mlogloss:1.12820	validation-mlogloss:1.15790
[350]	train-mlogloss:1.12774	validation-mlogloss:1.15754
[351]	train-mlogloss:1.12747	validation-mlogloss:1.15731
[352]	train-mlogloss:1.12719	validation-mlogloss:1.15708
[353]	train-mlogloss:1.12688	validation-mlogloss:1.15682
[354]	train-mlogloss:1.12638	validation-mlogloss:1.15641
[355]	train-mlogloss:1.12607	validation-mlogloss:1.15618
[356]	train-mlogloss:1.12569	validation-mlogloss:1.15591
[357]	train-mlogloss:1.12535	validation-mlogloss:1.15572
[358]	train-mlogloss:1.12494	validation-mlogloss:1.15549
[359]	train-mlogloss:1.12463	validation-mlogloss:1.15530
[360]	train-mlogloss:1.12427	validation-mlogloss:1.15502
[361]	train-mlogloss:1.12391	validation-mlogloss:1.15477
[362]	train-mlogloss:1.12353	validation-mlogloss:1.15440
[363]	train-mlogloss:1.12316	validation-mlogloss:1.15414
[364]	train-mlogloss:1.12290	validation-mlogloss:1.15394
[365]	train-mlogloss:1.12249	validation-mlogloss:1.15362
[366]	train-mlogloss:1.12210	validation-mlogloss:1.15327
[367]	train-mlogloss:1.12173	validation-mlogloss:1.15293
[368]	train-mlogloss:1.12142	validation-mlogloss:1.15278
[369]	train-mlogloss:1.12107	validation-mlogloss:1.15257
[370]	train-mlogloss:1.12077	validation-mlogloss:1.15236
[371]	train-mlogloss:1.12057	validation-mlogloss:1.15226
[372]	train-mlogloss:1.12028	validation-mlogloss:1.15207
[373]	train-mlogloss:1.11994	validation-mlogloss:1.15183
[374]	train-mlogloss:1.11959	validation-mlogloss:1.15147
[375]	train-mlogloss:1.11927	validation-mlogloss:1.15129
[376]	train-mlogloss:1.11892	validation-mlogloss:1.15103
[377]	train-mlogloss:1.11865	validation-mlogloss:1.15077
[378]	train-mlogloss:1.11833	validation-mlogloss:1.15054
[379]	train-mlogloss:1.11810	validation-mlogloss:1.15040
[380]	train-mlogloss:1.11781	validation-mlogloss:1.15012
[381]	train-mlogloss:1.11752	validation-mlogloss:1.14991
[382]	train-mlogloss:1.11724	validation-mlogloss:1.14968
[383]	train-mlogloss:1.11693	validation-mlogloss:1.14939
[384]	train-mlogloss:1.11669	validation-mlogloss:1.14920
[385]	train-mlogloss:1.11645	validation-mlogloss:1.14901
[386]	train-mlogloss:1.11624	validation-mlogloss:1.14888
[387]	train-mlogloss:1.11599	validation-mlogloss:1.14867
[388]	train-mlogloss:1.11580	validation-mlogloss:1.14855
[389]	train-mlogloss:1.11552	validation-mlogloss:1.14837
[390]	train-mlogloss:1.11519	validation-mlogloss:1.14814
[391]	train-mlogloss:1.11492	validation-mlogloss:1.14786
[392]	train-mlogloss:1.11469	validation-mlogloss:1.14773
[393]	train-mlogloss:1.11460	validation-mlogloss:1.14762
[394]	train-mlogloss:1.11435	validation-mlogloss:1.14749
[395]	train-mlogloss:1.11424	validation-mlogloss:1.14739
[396]	train-mlogloss:1.11400	validation-mlogloss:1.14727
[397]	train-mlogloss:1.11368	validation-mlogloss:1.14706
[398]	train-mlogloss:1.11351	validation-mlogloss:1.14687
[399]	train-mlogloss:1.11335	validation-mlogloss:1.14673
[400]	train-mlogloss:1.11305	validation-mlogloss:1.14648
[401]	train-mlogloss:1.11292	validation-mlogloss:1.14634
[402]	train-mlogloss:1.11269	validation-mlogloss:1.14623
[403]	train-mlogloss:1.11236	validation-mlogloss:1.14601
[404]	train-mlogloss:1.11204	validation-mlogloss:1.14582
[405]	train-mlogloss:1.11186	validation-mlogloss:1.14571
[406]	train-mlogloss:1.11151	validation-mlogloss:1.14538
[407]	train-mlogloss:1.11128	validation-mlogloss:1.14520
[408]	train-mlogloss:1.11108	validation-mlogloss:1.14501
[409]	train-mlogloss:1.11088	validation-mlogloss:1.14483
[410]	train-mlogloss:1.11069	validation-mlogloss:1.14479
[411]	train-mlogloss:1.11051	validation-mlogloss:1.14463
[412]	train-mlogloss:1.11034	validation-mlogloss:1.14453
[413]	train-mlogloss:1.11014	validation-mlogloss:1.14446
[414]	train-mlogloss:1.10985	validation-mlogloss:1.14425
[415]	train-mlogloss:1.10964	validation-mlogloss:1.14417
[416]	train-mlogloss:1.10935	validation-mlogloss:1.14403
[417]	train-mlogloss:1.10911	validation-mlogloss:1.14387
[418]	train-mlogloss:1.10878	validation-mlogloss:1.14359
[419]	train-mlogloss:1.10849	validation-mlogloss:1.14332
[420]	train-mlogloss:1.10837	validation-mlogloss:1.14326
[421]	train-mlogloss:1.10819	validation-mlogloss:1.14314
[422]	train-mlogloss:1.10793	validation-mlogloss:1.14291
[423]	train-mlogloss:1.10768	validation-mlogloss:1.14274
[424]	train-mlogloss:1.10730	validation-mlogloss:1.14244
[425]	train-mlogloss:1.10714	validation-mlogloss:1.14239
[426]	train-mlogloss:1.10703	validation-mlogloss:1.14231
[427]	train-mlogloss:1.10678	validation-mlogloss:1.14214
[428]	train-mlogloss:1.10660	validation-mlogloss:1.14207
[429]	train-mlogloss:1.10634	validation-mlogloss:1.14185
[430]	train-mlogloss:1.10619	validation-mlogloss:1.14170
[431]	train-mlogloss:1.10610	validation-mlogloss:1.14166
[432]	train-mlogloss:1.10597	validation-mlogloss:1.14162
[433]	train-mlogloss:1.10588	validation-mlogloss:1.14158
[434]	train-mlogloss:1.10578	validation-mlogloss:1.14148
[435]	train-mlogloss:1.10568	validation-mlogloss:1.14140
[436]	train-mlogloss:1.10555	validation-mlogloss:1.14127
[437]	train-mlogloss:1.10531	validation-mlogloss:1.14105
[438]	train-mlogloss:1.10515	validation-mlogloss:1.14097
[439]	train-mlogloss:1.10490	validation-mlogloss:1.14081
[440]	train-mlogloss:1.10473	validation-mlogloss:1.14063
[441]	train-mlogloss:1.10442	validation-mlogloss:1.14034
[442]	train-mlogloss:1.10440	validation-mlogloss:1.14031
[443]	train-mlogloss:1.10432	validation-mlogloss:1.14028
[444]	train-mlogloss:1.10417	validation-mlogloss:1.14015
[445]	train-mlogloss:1.10402	validation-mlogloss:1.14009
[446]	train-mlogloss:1.10395	validation-mlogloss:1.14005
[447]	train-mlogloss:1.10378	validation-mlogloss:1.13992
[448]	train-mlogloss:1.10375	validation-mlogloss:1.13987
[449]	train-mlogloss:1.10356	validation-mlogloss:1.13978
[450]	train-mlogloss:1.10328	validation-mlogloss:1.13963
[451]	train-mlogloss:1.10319	validation-mlogloss:1.13960
[452]	train-mlogloss:1.10305	validation-mlogloss:1.13953
[453]	train-mlogloss:1.10275	validation-mlogloss:1.13928
[454]	train-mlogloss:1.10259	validation-mlogloss:1.13917
[455]	train-mlogloss:1.10247	validation-mlogloss:1.13906
[456]	train-mlogloss:1.10242	validation-mlogloss:1.13905
[457]	train-mlogloss:1.10223	validation-mlogloss:1.13896
[458]	train-mlogloss:1.10209	validation-mlogloss:1.13879
[459]	train-mlogloss:1.10192	validation-mlogloss:1.13867
[460]	train-mlogloss:1.10180	validation-mlogloss:1.13854
[461]	train-mlogloss:1.10159	validation-mlogloss:1.13833
[462]	train-mlogloss:1.10146	validation-mlogloss:1.13823
[463]	train-mlogloss:1.10134	validation-mlogloss:1.13814
[464]	train-mlogloss:1.10130	validation-mlogloss:1.13811
[465]	train-mlogloss:1.10118	validation-mlogloss:1.13803
[466]	train-mlogloss:1.10100	validation-mlogloss:1.13787
[467]	train-mlogloss:1.10081	validation-mlogloss:1.13781
[468]	train-mlogloss:1.10066	validation-mlogloss:1.13772
[469]	train-mlogloss:1.10053	validation-mlogloss:1.13767
[470]	train-mlogloss:1.10036	validation-mlogloss:1.13757
[471]	train-mlogloss:1.10016	validation-mlogloss:1.13752
[472]	train-mlogloss:1.10010	validation-mlogloss:1.13751
[473]	train-mlogloss:1.10004	validation-mlogloss:1.13747
[474]	train-mlogloss:1.09995	validation-mlogloss:1.13737
[475]	train-mlogloss:1.09981	validation-mlogloss:1.13734
[476]	train-mlogloss:1.09973	validation-mlogloss:1.13729
[477]	train-mlogloss:1.09957	validation-mlogloss:1.13723
[478]	train-mlogloss:1.09945	validation-mlogloss:1.13714
[479]	train-mlogloss:1.09928	validation-mlogloss:1.13700
[480]	train-mlogloss:1.09921	validation-mlogloss:1.13696
[481]	train-mlogloss:1.09909	validation-mlogloss:1.13688
[482]	train-mlogloss:1.09896	validation-mlogloss:1.13681
[483]	train-mlogloss:1.09890	validation-mlogloss:1.13671
[484]	train-mlogloss:1.09880	validation-mlogloss:1.13664
[485]	train-mlogloss:1.09867	validation-mlogloss:1.13652
[486]	train-mlogloss:1.09854	validation-mlogloss:1.13646
[487]	train-mlogloss:1.09848	validation-mlogloss:1.13641
[488]	train-mlogloss:1.09835	validation-mlogloss:1.13625
[489]	train-mlogloss:1.09824	validation-mlogloss:1.13613
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.69 [%]
Global accuracy score (test) = 63.23 [%]
Global F1 score (train) = 66.52 [%]
Global F1 score (test) = 62.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.52      0.42      0.47       161
 CAMINAR CON MÓVIL O LIBRO       0.40      0.58      0.48       161
       CAMINAR USUAL SPEED       0.44      0.30      0.35       161
            CAMINAR ZIGZAG       0.75      0.58      0.65       161
          DE PIE BARRIENDO       0.74      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.44      0.57      0.49       161
    DE PIE MOVIENDO LIBROS       0.54      0.48      0.51       161
          DE PIE USANDO PC       0.82      0.91      0.86       161
        FASE REPOSO CON K5       0.78      0.92      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.58      0.37      0.45       161
         SENTADO USANDO PC       0.48      0.60      0.54       161
      SENTADO VIENDO LA TV       0.51      0.36      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.63      2415
              weighted avg       0.64      0.63      0.63      2415


Accuracy capturado en la ejecución 7: 63.23 [%]
F1-score capturado en la ejecución 7: 62.56 [%]

=== EJECUCIÓN 8 ===

--- TRAIN (ejecución 8) ---

--- TEST (ejecución 8) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.7 [%]
Global accuracy score (test) = 62.61 [%]
Global F1 score (train) = 66.52 [%]
Global F1 score (test) = 61.79 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.49      0.42      0.45       161
 CAMINAR CON MÓVIL O LIBRO       0.42      0.57      0.48       161
       CAMINAR USUAL SPEED       0.39      0.26      0.31       161
            CAMINAR ZIGZAG       0.70      0.58      0.64       161
          DE PIE BARRIENDO       0.74      0.61      0.67       161
   DE PIE DOBLANDO TOALLAS       0.44      0.56      0.49       161
    DE PIE MOVIENDO LIBROS       0.53      0.50      0.51       161
          DE PIE USANDO PC       0.81      0.89      0.85       161
        FASE REPOSO CON K5       0.77      0.92      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.56      0.34      0.42       161
         SENTADO USANDO PC       0.47      0.61      0.53       161
      SENTADO VIENDO LA TV       0.51      0.35      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 8: 62.61 [%]
F1-score capturado en la ejecución 8: 61.79 [%]

=== EJECUCIÓN 9 ===

--- TRAIN (ejecución 9) ---

--- TEST (ejecución 9) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.65 [%]
Global accuracy score (test) = 62.77 [%]
Global F1 score (train) = 66.47 [%]
Global F1 score (test) = 61.86 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.49      0.41      0.45       161
 CAMINAR CON MÓVIL O LIBRO       0.42      0.59      0.49       161
       CAMINAR USUAL SPEED       0.42      0.27      0.33       161
            CAMINAR ZIGZAG       0.71      0.58      0.64       161
          DE PIE BARRIENDO       0.73      0.64      0.68       161
   DE PIE DOBLANDO TOALLAS       0.44      0.56      0.49       161
    DE PIE MOVIENDO LIBROS       0.54      0.47      0.50       161
          DE PIE USANDO PC       0.83      0.94      0.88       161
        FASE REPOSO CON K5       0.75      0.91      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.57      0.39      0.46       161
         SENTADO USANDO PC       0.47      0.60      0.53       161
      SENTADO VIENDO LA TV       0.47      0.29      0.36       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 9: 62.77 [%]
F1-score capturado en la ejecución 9: 61.86 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-10-26 03:21:02.807568: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 03:21:02.823495: 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 00:00:1761445262.842349 2371162 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761445262.848090 2371162 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761445262.861990 2371162 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761445262.862104 2371162 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761445262.862150 2371162 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761445262.862193 2371162 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 03:21:02.866223: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67471	validation-mlogloss:2.67532
[1]	train-mlogloss:2.64244	validation-mlogloss:2.64380
[2]	train-mlogloss:2.61177	validation-mlogloss:2.61373
[3]	train-mlogloss:2.58315	validation-mlogloss:2.58474
[4]	train-mlogloss:2.55443	validation-mlogloss:2.55707
[5]	train-mlogloss:2.52633	validation-mlogloss:2.52966
[6]	train-mlogloss:2.49898	validation-mlogloss:2.50227
[7]	train-mlogloss:2.47398	validation-mlogloss:2.47687
[8]	train-mlogloss:2.44812	validation-mlogloss:2.45233
[9]	train-mlogloss:2.42477	validation-mlogloss:2.42960
[10]	train-mlogloss:2.40206	validation-mlogloss:2.40756
[11]	train-mlogloss:2.37915	validation-mlogloss:2.38516
[12]	train-mlogloss:2.35777	validation-mlogloss:2.36415
[13]	train-mlogloss:2.33766	validation-mlogloss:2.34434
[14]	train-mlogloss:2.31771	validation-mlogloss:2.32516
[15]	train-mlogloss:2.29918	validation-mlogloss:2.30720
[16]	train-mlogloss:2.28071	validation-mlogloss:2.28926
[17]	train-mlogloss:2.26071	validation-mlogloss:2.26856
[18]	train-mlogloss:2.24355	validation-mlogloss:2.25114
[19]	train-mlogloss:2.22633	validation-mlogloss:2.23436
[20]	train-mlogloss:2.20917	validation-mlogloss:2.21724
[21]	train-mlogloss:2.19221	validation-mlogloss:2.19999
[22]	train-mlogloss:2.17643	validation-mlogloss:2.18399
[23]	train-mlogloss:2.16039	validation-mlogloss:2.16870
[24]	train-mlogloss:2.14428	validation-mlogloss:2.15218
[25]	train-mlogloss:2.12930	validation-mlogloss:2.13821
[26]	train-mlogloss:2.11441	validation-mlogloss:2.12352
[27]	train-mlogloss:2.09982	validation-mlogloss:2.10968
[28]	train-mlogloss:2.08520	validation-mlogloss:2.09502
[29]	train-mlogloss:2.07079	validation-mlogloss:2.08091
[30]	train-mlogloss:2.05760	validation-mlogloss:2.06784
[31]	train-mlogloss:2.04473	validation-mlogloss:2.05486
[32]	train-mlogloss:2.03139	validation-mlogloss:2.04176
[33]	train-mlogloss:2.01877	validation-mlogloss:2.02950
[34]	train-mlogloss:2.00621	validation-mlogloss:2.01737
[35]	train-mlogloss:1.99393	validation-mlogloss:2.00521
[36]	train-mlogloss:1.98220	validation-mlogloss:1.99440
[37]	train-mlogloss:1.96998	validation-mlogloss:1.98220
[38]	train-mlogloss:1.95866	validation-mlogloss:1.97123
[39]	train-mlogloss:1.94723	validation-mlogloss:1.95953
[40]	train-mlogloss:1.93646	validation-mlogloss:1.94874
[41]	train-mlogloss:1.92566	validation-mlogloss:1.93812
[42]	train-mlogloss:1.91505	validation-mlogloss:1.92743
[43]	train-mlogloss:1.90436	validation-mlogloss:1.91650
[44]	train-mlogloss:1.89416	validation-mlogloss:1.90629
[45]	train-mlogloss:1.88429	validation-mlogloss:1.89663
[46]	train-mlogloss:1.87465	validation-mlogloss:1.88755
[47]	train-mlogloss:1.86469	validation-mlogloss:1.87737
[48]	train-mlogloss:1.85503	validation-mlogloss:1.86778
[49]	train-mlogloss:1.84581	validation-mlogloss:1.85850
[50]	train-mlogloss:1.83654	validation-mlogloss:1.84924
[51]	train-mlogloss:1.82738	validation-mlogloss:1.84024
[52]	train-mlogloss:1.81864	validation-mlogloss:1.83180
[53]	train-mlogloss:1.81006	validation-mlogloss:1.82310
[54]	train-mlogloss:1.80165	validation-mlogloss:1.81476
[55]	train-mlogloss:1.79313	validation-mlogloss:1.80608
[56]	train-mlogloss:1.78466	validation-mlogloss:1.79776
[57]	train-mlogloss:1.77617	validation-mlogloss:1.78921
[58]	train-mlogloss:1.76812	validation-mlogloss:1.78116
[59]	train-mlogloss:1.76020	validation-mlogloss:1.77327
[60]	train-mlogloss:1.75246	validation-mlogloss:1.76565
[61]	train-mlogloss:1.74484	validation-mlogloss:1.75817
[62]	train-mlogloss:1.73707	validation-mlogloss:1.75027
[63]	train-mlogloss:1.72935	validation-mlogloss:1.74276
[64]	train-mlogloss:1.72217	validation-mlogloss:1.73545
[65]	train-mlogloss:1.71490	validation-mlogloss:1.72822
[66]	train-mlogloss:1.70739	validation-mlogloss:1.72044
[67]	train-mlogloss:1.70069	validation-mlogloss:1.71389
[68]	train-mlogloss:1.69358	validation-mlogloss:1.70656
[69]	train-mlogloss:1.68678	validation-mlogloss:1.69967
[70]	train-mlogloss:1.68009	validation-mlogloss:1.69291
[71]	train-mlogloss:1.67347	validation-mlogloss:1.68608
[72]	train-mlogloss:1.66721	validation-mlogloss:1.67975
[73]	train-mlogloss:1.66093	validation-mlogloss:1.67330
[74]	train-mlogloss:1.65485	validation-mlogloss:1.66725
[75]	train-mlogloss:1.64896	validation-mlogloss:1.66164
[76]	train-mlogloss:1.64284	validation-mlogloss:1.65560
[77]	train-mlogloss:1.63690	validation-mlogloss:1.64982
[78]	train-mlogloss:1.63095	validation-mlogloss:1.64390
[79]	train-mlogloss:1.62524	validation-mlogloss:1.63820
[80]	train-mlogloss:1.61940	validation-mlogloss:1.63225
[81]	train-mlogloss:1.61381	validation-mlogloss:1.62667
[82]	train-mlogloss:1.60804	validation-mlogloss:1.62063
[83]	train-mlogloss:1.60256	validation-mlogloss:1.61521
[84]	train-mlogloss:1.59704	validation-mlogloss:1.60966
[85]	train-mlogloss:1.59184	validation-mlogloss:1.60468
[86]	train-mlogloss:1.58631	validation-mlogloss:1.59909
[87]	train-mlogloss:1.58097	validation-mlogloss:1.59362
[88]	train-mlogloss:1.57594	validation-mlogloss:1.58866
[89]	train-mlogloss:1.57090	validation-mlogloss:1.58360
[90]	train-mlogloss:1.56576	validation-mlogloss:1.57822
[91]	train-mlogloss:1.56072	validation-mlogloss:1.57309
[92]	train-mlogloss:1.55605	validation-mlogloss:1.56837
[93]	train-mlogloss:1.55119	validation-mlogloss:1.56345
[94]	train-mlogloss:1.54640	validation-mlogloss:1.55873
[95]	train-mlogloss:1.54182	validation-mlogloss:1.55427
[96]	train-mlogloss:1.53742	validation-mlogloss:1.54992
[97]	train-mlogloss:1.53271	validation-mlogloss:1.54518
[98]	train-mlogloss:1.52802	validation-mlogloss:1.54043
[99]	train-mlogloss:1.52383	validation-mlogloss:1.53628
[100]	train-mlogloss:1.51927	validation-mlogloss:1.53181
[101]	train-mlogloss:1.51476	validation-mlogloss:1.52733
[102]	train-mlogloss:1.51044	validation-mlogloss:1.52303
[103]	train-mlogloss:1.50629	validation-mlogloss:1.51873
[104]	train-mlogloss:1.50204	validation-mlogloss:1.51425
[105]	train-mlogloss:1.49814	validation-mlogloss:1.51025
[106]	train-mlogloss:1.49412	validation-mlogloss:1.50634
[107]	train-mlogloss:1.49015	validation-mlogloss:1.50221
[108]	train-mlogloss:1.48599	validation-mlogloss:1.49796
[109]	train-mlogloss:1.48214	validation-mlogloss:1.49408
[110]	train-mlogloss:1.47809	validation-mlogloss:1.49004
[111]	train-mlogloss:1.47431	validation-mlogloss:1.48633
[112]	train-mlogloss:1.47057	validation-mlogloss:1.48257
[113]	train-mlogloss:1.46687	validation-mlogloss:1.47894
[114]	train-mlogloss:1.46290	validation-mlogloss:1.47483
[115]	train-mlogloss:1.45924	validation-mlogloss:1.47119
[116]	train-mlogloss:1.45565	validation-mlogloss:1.46747
[117]	train-mlogloss:1.45202	validation-mlogloss:1.46369
[118]	train-mlogloss:1.44846	validation-mlogloss:1.45988
[119]	train-mlogloss:1.44502	validation-mlogloss:1.45668
[120]	train-mlogloss:1.44147	validation-mlogloss:1.45320
[121]	train-mlogloss:1.43811	validation-mlogloss:1.44969
[122]	train-mlogloss:1.43467	validation-mlogloss:1.44622
[123]	train-mlogloss:1.43124	validation-mlogloss:1.44261
[124]	train-mlogloss:1.42793	validation-mlogloss:1.43919
[125]	train-mlogloss:1.42468	validation-mlogloss:1.43593
[126]	train-mlogloss:1.42132	validation-mlogloss:1.43258
[127]	train-mlogloss:1.41832	validation-mlogloss:1.42966
[128]	train-mlogloss:1.41520	validation-mlogloss:1.42666
[129]	train-mlogloss:1.41207	validation-mlogloss:1.42346
[130]	train-mlogloss:1.40901	validation-mlogloss:1.42044
[131]	train-mlogloss:1.40576	validation-mlogloss:1.41715
[132]	train-mlogloss:1.40271	validation-mlogloss:1.41429
[133]	train-mlogloss:1.39973	validation-mlogloss:1.41135
[134]	train-mlogloss:1.39681	validation-mlogloss:1.40846
[135]	train-mlogloss:1.39387	validation-mlogloss:1.40547
[136]	train-mlogloss:1.39105	validation-mlogloss:1.40265
[137]	train-mlogloss:1.38843	validation-mlogloss:1.40000
[138]	train-mlogloss:1.38591	validation-mlogloss:1.39736
[139]	train-mlogloss:1.38319	validation-mlogloss:1.39467
[140]	train-mlogloss:1.38058	validation-mlogloss:1.39189
[141]	train-mlogloss:1.37798	validation-mlogloss:1.38927
[142]	train-mlogloss:1.37531	validation-mlogloss:1.38652
[143]	train-mlogloss:1.37255	validation-mlogloss:1.38362
[144]	train-mlogloss:1.36993	validation-mlogloss:1.38089
[145]	train-mlogloss:1.36736	validation-mlogloss:1.37837
[146]	train-mlogloss:1.36463	validation-mlogloss:1.37558
[147]	train-mlogloss:1.36221	validation-mlogloss:1.37301
[148]	train-mlogloss:1.35950	validation-mlogloss:1.37019
[149]	train-mlogloss:1.35695	validation-mlogloss:1.36744
[150]	train-mlogloss:1.35455	validation-mlogloss:1.36497
[151]	train-mlogloss:1.35197	validation-mlogloss:1.36243
[152]	train-mlogloss:1.34954	validation-mlogloss:1.35998
[153]	train-mlogloss:1.34712	validation-mlogloss:1.35769
[154]	train-mlogloss:1.34472	validation-mlogloss:1.35522
[155]	train-mlogloss:1.34231	validation-mlogloss:1.35287
[156]	train-mlogloss:1.33995	validation-mlogloss:1.35043
[157]	train-mlogloss:1.33748	validation-mlogloss:1.34804
[158]	train-mlogloss:1.33527	validation-mlogloss:1.34579
[159]	train-mlogloss:1.33303	validation-mlogloss:1.34355
[160]	train-mlogloss:1.33071	validation-mlogloss:1.34143
[161]	train-mlogloss:1.32864	validation-mlogloss:1.33942
[162]	train-mlogloss:1.32636	validation-mlogloss:1.33722
[163]	train-mlogloss:1.32432	validation-mlogloss:1.33520
[164]	train-mlogloss:1.32222	validation-mlogloss:1.33326
[165]	train-mlogloss:1.32022	validation-mlogloss:1.33130
[166]	train-mlogloss:1.31810	validation-mlogloss:1.32919
[167]	train-mlogloss:1.31611	validation-mlogloss:1.32729
[168]	train-mlogloss:1.31403	validation-mlogloss:1.32513
[169]	train-mlogloss:1.31194	validation-mlogloss:1.32303
[170]	train-mlogloss:1.30986	validation-mlogloss:1.32096
[171]	train-mlogloss:1.30781	validation-mlogloss:1.31877
[172]	train-mlogloss:1.30600	validation-mlogloss:1.31704
[173]	train-mlogloss:1.30401	validation-mlogloss:1.31508
[174]	train-mlogloss:1.30206	validation-mlogloss:1.31324
[175]	train-mlogloss:1.30013	validation-mlogloss:1.31138
[176]	train-mlogloss:1.29824	validation-mlogloss:1.30960
[177]	train-mlogloss:1.29628	validation-mlogloss:1.30761
[178]	train-mlogloss:1.29436	validation-mlogloss:1.30553
[179]	train-mlogloss:1.29252	validation-mlogloss:1.30373
[180]	train-mlogloss:1.29068	validation-mlogloss:1.30199
[181]	train-mlogloss:1.28890	validation-mlogloss:1.30026
[182]	train-mlogloss:1.28708	validation-mlogloss:1.29848
[183]	train-mlogloss:1.28541	validation-mlogloss:1.29681
[184]	train-mlogloss:1.28363	validation-mlogloss:1.29520
[185]	train-mlogloss:1.28184	validation-mlogloss:1.29357
[186]	train-mlogloss:1.28001	validation-mlogloss:1.29159
[187]	train-mlogloss:1.27818	validation-mlogloss:1.28987
[188]	train-mlogloss:1.27644	validation-mlogloss:1.28817
[189]	train-mlogloss:1.27466	validation-mlogloss:1.28647
[190]	train-mlogloss:1.27298	validation-mlogloss:1.28487
[191]	train-mlogloss:1.27132	validation-mlogloss:1.28349
[192]	train-mlogloss:1.26968	validation-mlogloss:1.28187
[193]	train-mlogloss:1.26810	validation-mlogloss:1.28041
[194]	train-mlogloss:1.26653	validation-mlogloss:1.27889
[195]	train-mlogloss:1.26487	validation-mlogloss:1.27727
[196]	train-mlogloss:1.26326	validation-mlogloss:1.27579
[197]	train-mlogloss:1.26178	validation-mlogloss:1.27427
[198]	train-mlogloss:1.26026	validation-mlogloss:1.27274
[199]	train-mlogloss:1.25869	validation-mlogloss:1.27128
[200]	train-mlogloss:1.25710	validation-mlogloss:1.26984
[201]	train-mlogloss:1.25557	validation-mlogloss:1.26855
[202]	train-mlogloss:1.25408	validation-mlogloss:1.26721
[203]	train-mlogloss:1.25263	validation-mlogloss:1.26568
[204]	train-mlogloss:1.25104	validation-mlogloss:1.26423
[205]	train-mlogloss:1.24958	validation-mlogloss:1.26268
[206]	train-mlogloss:1.24812	validation-mlogloss:1.26137
[207]	train-mlogloss:1.24658	validation-mlogloss:1.25993
[208]	train-mlogloss:1.24518	validation-mlogloss:1.25865
[209]	train-mlogloss:1.24372	validation-mlogloss:1.25728
[210]	train-mlogloss:1.24233	validation-mlogloss:1.25588
[211]	train-mlogloss:1.24097	validation-mlogloss:1.25446
[212]	train-mlogloss:1.23972	validation-mlogloss:1.25327
[213]	train-mlogloss:1.23831	validation-mlogloss:1.25181
[214]	train-mlogloss:1.23693	validation-mlogloss:1.25048
[215]	train-mlogloss:1.23563	validation-mlogloss:1.24933
[216]	train-mlogloss:1.23441	validation-mlogloss:1.24817
[217]	train-mlogloss:1.23313	validation-mlogloss:1.24698
[218]	train-mlogloss:1.23178	validation-mlogloss:1.24567
[219]	train-mlogloss:1.23038	validation-mlogloss:1.24442
[220]	train-mlogloss:1.22908	validation-mlogloss:1.24329
[221]	train-mlogloss:1.22771	validation-mlogloss:1.24213
[222]	train-mlogloss:1.22645	validation-mlogloss:1.24118
[223]	train-mlogloss:1.22522	validation-mlogloss:1.24004
[224]	train-mlogloss:1.22398	validation-mlogloss:1.23886
[225]	train-mlogloss:1.22279	validation-mlogloss:1.23773
[226]	train-mlogloss:1.22154	validation-mlogloss:1.23672
[227]	train-mlogloss:1.22036	validation-mlogloss:1.23566
[228]	train-mlogloss:1.21920	validation-mlogloss:1.23467
[229]	train-mlogloss:1.21801	validation-mlogloss:1.23346
[230]	train-mlogloss:1.21689	validation-mlogloss:1.23239
[231]	train-mlogloss:1.21570	validation-mlogloss:1.23131
[232]	train-mlogloss:1.21451	validation-mlogloss:1.23025
[233]	train-mlogloss:1.21330	validation-mlogloss:1.22910
[234]	train-mlogloss:1.21218	validation-mlogloss:1.22797
[235]	train-mlogloss:1.21099	validation-mlogloss:1.22691
[236]	train-mlogloss:1.20984	validation-mlogloss:1.22589
[237]	train-mlogloss:1.20874	validation-mlogloss:1.22483
[238]	train-mlogloss:1.20764	validation-mlogloss:1.22391
[239]	train-mlogloss:1.20656	validation-mlogloss:1.22280
[240]	train-mlogloss:1.20545	validation-mlogloss:1.22171
[241]	train-mlogloss:1.20439	validation-mlogloss:1.22075
[242]	train-mlogloss:1.20335	validation-mlogloss:1.21983
[243]	train-mlogloss:1.20226	validation-mlogloss:1.21885
[244]	train-mlogloss:1.20105	validation-mlogloss:1.21786
[245]	train-mlogloss:1.20003	validation-mlogloss:1.21687
[246]	train-mlogloss:1.19882	validation-mlogloss:1.21582
[247]	train-mlogloss:1.19779	validation-mlogloss:1.21494
[248]	train-mlogloss:1.19676	validation-mlogloss:1.21409
[249]	train-mlogloss:1.19575	validation-mlogloss:1.21321
[250]	train-mlogloss:1.19470	validation-mlogloss:1.21228
[251]	train-mlogloss:1.19370	validation-mlogloss:1.21137
[252]	train-mlogloss:1.19268	validation-mlogloss:1.21061
[253]	train-mlogloss:1.19159	validation-mlogloss:1.20955
[254]	train-mlogloss:1.19066	validation-mlogloss:1.20867
[255]	train-mlogloss:1.18986	validation-mlogloss:1.20793
[256]	train-mlogloss:1.18884	validation-mlogloss:1.20693
[257]	train-mlogloss:1.18800	validation-mlogloss:1.20615
[258]	train-mlogloss:1.18722	validation-mlogloss:1.20546
[259]	train-mlogloss:1.18627	validation-mlogloss:1.20462
[260]	train-mlogloss:1.18525	validation-mlogloss:1.20380
[261]	train-mlogloss:1.18432	validation-mlogloss:1.20307
[262]	train-mlogloss:1.18331	validation-mlogloss:1.20229
[263]	train-mlogloss:1.18236	validation-mlogloss:1.20144
[264]	train-mlogloss:1.18147	validation-mlogloss:1.20058
[265]	train-mlogloss:1.18044	validation-mlogloss:1.19983
[266]	train-mlogloss:1.17962	validation-mlogloss:1.19913
[267]	train-mlogloss:1.17885	validation-mlogloss:1.19842
[268]	train-mlogloss:1.17794	validation-mlogloss:1.19759
[269]	train-mlogloss:1.17714	validation-mlogloss:1.19688
[270]	train-mlogloss:1.17629	validation-mlogloss:1.19611
[271]	train-mlogloss:1.17543	validation-mlogloss:1.19544
[272]	train-mlogloss:1.17466	validation-mlogloss:1.19475
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[329]	train-mlogloss:1.13844	validation-mlogloss:1.16421
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[331]	train-mlogloss:1.13743	validation-mlogloss:1.16346
[332]	train-mlogloss:1.13699	validation-mlogloss:1.16319
[333]	train-mlogloss:1.13661	validation-mlogloss:1.16280
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[338]	train-mlogloss:1.13476	validation-mlogloss:1.16135
[339]	train-mlogloss:1.13431	validation-mlogloss:1.16103
[340]	train-mlogloss:1.13398	validation-mlogloss:1.16073
[341]	train-mlogloss:1.13357	validation-mlogloss:1.16035
[342]	train-mlogloss:1.13313	validation-mlogloss:1.16005
[343]	train-mlogloss:1.13276	validation-mlogloss:1.15965
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[346]	train-mlogloss:1.13152	validation-mlogloss:1.15864
[347]	train-mlogloss:1.13114	validation-mlogloss:1.15837
[348]	train-mlogloss:1.13073	validation-mlogloss:1.15796
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[352]	train-mlogloss:1.12935	validation-mlogloss:1.15689
[353]	train-mlogloss:1.12891	validation-mlogloss:1.15656
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[355]	train-mlogloss:1.12817	validation-mlogloss:1.15608
[356]	train-mlogloss:1.12772	validation-mlogloss:1.15572
[357]	train-mlogloss:1.12741	validation-mlogloss:1.15545
[358]	train-mlogloss:1.12711	validation-mlogloss:1.15531
[359]	train-mlogloss:1.12677	validation-mlogloss:1.15504
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[419]	train-mlogloss:1.11036	validation-mlogloss:1.14320
[420]	train-mlogloss:1.11012	validation-mlogloss:1.14305
[421]	train-mlogloss:1.11003	validation-mlogloss:1.14297
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[426]	train-mlogloss:1.10911	validation-mlogloss:1.14223
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[435]	train-mlogloss:1.10752	validation-mlogloss:1.14091
[436]	train-mlogloss:1.10736	validation-mlogloss:1.14084
[437]	train-mlogloss:1.10721	validation-mlogloss:1.14074
[438]	train-mlogloss:1.10701	validation-mlogloss:1.14054
[439]	train-mlogloss:1.10680	validation-mlogloss:1.14041
[440]	train-mlogloss:1.10662	validation-mlogloss:1.14029
[441]	train-mlogloss:1.10646	validation-mlogloss:1.14018
[442]	train-mlogloss:1.10616	validation-mlogloss:1.14008
[443]	train-mlogloss:1.10595	validation-mlogloss:1.13996
[444]	train-mlogloss:1.10581	validation-mlogloss:1.13986
[445]	train-mlogloss:1.10575	validation-mlogloss:1.13983
[446]	train-mlogloss:1.10566	validation-mlogloss:1.13975
[447]	train-mlogloss:1.10545	validation-mlogloss:1.13966
[448]	train-mlogloss:1.10532	validation-mlogloss:1.13963
[449]	train-mlogloss:1.10517	validation-mlogloss:1.13949
[450]	train-mlogloss:1.10500	validation-mlogloss:1.13928
[451]	train-mlogloss:1.10489	validation-mlogloss:1.13921
[452]	train-mlogloss:1.10470	validation-mlogloss:1.13903
[453]	train-mlogloss:1.10452	validation-mlogloss:1.13885
[454]	train-mlogloss:1.10438	validation-mlogloss:1.13870
[455]	train-mlogloss:1.10423	validation-mlogloss:1.13856
[456]	train-mlogloss:1.10414	validation-mlogloss:1.13850
[457]	train-mlogloss:1.10393	validation-mlogloss:1.13834
[458]	train-mlogloss:1.10384	validation-mlogloss:1.13827
[459]	train-mlogloss:1.10362	validation-mlogloss:1.13811
[460]	train-mlogloss:1.10353	validation-mlogloss:1.13802
[461]	train-mlogloss:1.10337	validation-mlogloss:1.13790
[462]	train-mlogloss:1.10325	validation-mlogloss:1.13783
[463]	train-mlogloss:1.10312	validation-mlogloss:1.13776
[464]	train-mlogloss:1.10303	validation-mlogloss:1.13774
[465]	train-mlogloss:1.10295	validation-mlogloss:1.13765
[466]	train-mlogloss:1.10281	validation-mlogloss:1.13752
[467]	train-mlogloss:1.10265	validation-mlogloss:1.13741
[468]	train-mlogloss:1.10248	validation-mlogloss:1.13734
[469]	train-mlogloss:1.10248	validation-mlogloss:1.13734
[470]	train-mlogloss:1.10238	validation-mlogloss:1.13722
[471]	train-mlogloss:1.10238	validation-mlogloss:1.13722
[472]	train-mlogloss:1.10225	validation-mlogloss:1.13712
[473]	train-mlogloss:1.10212	validation-mlogloss:1.13701
[474]	train-mlogloss:1.10207	validation-mlogloss:1.13701
[475]	train-mlogloss:1.10193	validation-mlogloss:1.13692
[476]	train-mlogloss:1.10170	validation-mlogloss:1.13672
[477]	train-mlogloss:1.10138	validation-mlogloss:1.13646
[478]	train-mlogloss:1.10125	validation-mlogloss:1.13635
[479]	train-mlogloss:1.10121	validation-mlogloss:1.13630
[480]	train-mlogloss:1.10110	validation-mlogloss:1.13623
[481]	train-mlogloss:1.10108	validation-mlogloss:1.13622
[482]	train-mlogloss:1.10089	validation-mlogloss:1.13612
[483]	train-mlogloss:1.10079	validation-mlogloss:1.13607
[484]	train-mlogloss:1.10071	validation-mlogloss:1.13599
[485]	train-mlogloss:1.10064	validation-mlogloss:1.13587
[486]	train-mlogloss:1.10050	validation-mlogloss:1.13584
[487]	train-mlogloss:1.10040	validation-mlogloss:1.13575
[488]	train-mlogloss:1.10030	validation-mlogloss:1.13574
[489]	train-mlogloss:1.10018	validation-mlogloss:1.13561
Saved model to disk.
2025-10-26 04:41:00.097816: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 04:41:00.118527: 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 00:00:1761450060.137722 2450303 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761450060.143557 2450303 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761450060.162548 2450303 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761450060.162664 2450303 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761450060.162711 2450303 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761450060.162822 2450303 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 04:41:00.166961: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67534	validation-mlogloss:2.67691
[1]	train-mlogloss:2.64315	validation-mlogloss:2.64599
[2]	train-mlogloss:2.61162	validation-mlogloss:2.61439
[3]	train-mlogloss:2.58318	validation-mlogloss:2.58692
[4]	train-mlogloss:2.55513	validation-mlogloss:2.55981
[5]	train-mlogloss:2.52803	validation-mlogloss:2.53302
[6]	train-mlogloss:2.50255	validation-mlogloss:2.50898
[7]	train-mlogloss:2.47749	validation-mlogloss:2.48374
[8]	train-mlogloss:2.45324	validation-mlogloss:2.46084
[9]	train-mlogloss:2.42963	validation-mlogloss:2.43822
[10]	train-mlogloss:2.40460	validation-mlogloss:2.41402
[11]	train-mlogloss:2.38319	validation-mlogloss:2.39335
[12]	train-mlogloss:2.36247	validation-mlogloss:2.37299
[13]	train-mlogloss:2.34137	validation-mlogloss:2.35229
[14]	train-mlogloss:2.32088	validation-mlogloss:2.33280
[15]	train-mlogloss:2.30073	validation-mlogloss:2.31360
[16]	train-mlogloss:2.28205	validation-mlogloss:2.29544
[17]	train-mlogloss:2.26378	validation-mlogloss:2.27750
[18]	train-mlogloss:2.24507	validation-mlogloss:2.25911
[19]	train-mlogloss:2.22827	validation-mlogloss:2.24280
[20]	train-mlogloss:2.21094	validation-mlogloss:2.22594
[21]	train-mlogloss:2.19398	validation-mlogloss:2.20894
[22]	train-mlogloss:2.17705	validation-mlogloss:2.19262
[23]	train-mlogloss:2.16190	validation-mlogloss:2.17705
[24]	train-mlogloss:2.14643	validation-mlogloss:2.16192
[25]	train-mlogloss:2.13058	validation-mlogloss:2.14661
[26]	train-mlogloss:2.11654	validation-mlogloss:2.13301
[27]	train-mlogloss:2.10170	validation-mlogloss:2.11810
[28]	train-mlogloss:2.08788	validation-mlogloss:2.10440
[29]	train-mlogloss:2.07427	validation-mlogloss:2.09058
[30]	train-mlogloss:2.06068	validation-mlogloss:2.07691
[31]	train-mlogloss:2.04723	validation-mlogloss:2.06402
[32]	train-mlogloss:2.03417	validation-mlogloss:2.05070
[33]	train-mlogloss:2.02160	validation-mlogloss:2.03842
[34]	train-mlogloss:2.00858	validation-mlogloss:2.02494
[35]	train-mlogloss:1.99628	validation-mlogloss:2.01260
[36]	train-mlogloss:1.98447	validation-mlogloss:2.00059
[37]	train-mlogloss:1.97246	validation-mlogloss:1.98840
[38]	train-mlogloss:1.96113	validation-mlogloss:1.97711
[39]	train-mlogloss:1.95003	validation-mlogloss:1.96605
[40]	train-mlogloss:1.93929	validation-mlogloss:1.95513
[41]	train-mlogloss:1.92835	validation-mlogloss:1.94430
[42]	train-mlogloss:1.91769	validation-mlogloss:1.93361
[43]	train-mlogloss:1.90732	validation-mlogloss:1.92320
[44]	train-mlogloss:1.89712	validation-mlogloss:1.91299
[45]	train-mlogloss:1.88722	validation-mlogloss:1.90310
[46]	train-mlogloss:1.87671	validation-mlogloss:1.89287
[47]	train-mlogloss:1.86659	validation-mlogloss:1.88221
[48]	train-mlogloss:1.85705	validation-mlogloss:1.87247
[49]	train-mlogloss:1.84798	validation-mlogloss:1.86335
[50]	train-mlogloss:1.83885	validation-mlogloss:1.85444
[51]	train-mlogloss:1.82984	validation-mlogloss:1.84534
[52]	train-mlogloss:1.82098	validation-mlogloss:1.83644
[53]	train-mlogloss:1.81256	validation-mlogloss:1.82804
[54]	train-mlogloss:1.80420	validation-mlogloss:1.81991
[55]	train-mlogloss:1.79580	validation-mlogloss:1.81137
[56]	train-mlogloss:1.78724	validation-mlogloss:1.80289
[57]	train-mlogloss:1.77916	validation-mlogloss:1.79459
[58]	train-mlogloss:1.77147	validation-mlogloss:1.78682
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[458]	train-mlogloss:1.10497	validation-mlogloss:1.14024
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[462]	train-mlogloss:1.10406	validation-mlogloss:1.13962
[463]	train-mlogloss:1.10400	validation-mlogloss:1.13958
[464]	train-mlogloss:1.10387	validation-mlogloss:1.13951
[465]	train-mlogloss:1.10372	validation-mlogloss:1.13943
[466]	train-mlogloss:1.10359	validation-mlogloss:1.13935
[467]	train-mlogloss:1.10344	validation-mlogloss:1.13925
[468]	train-mlogloss:1.10335	validation-mlogloss:1.13921
[469]	train-mlogloss:1.10331	validation-mlogloss:1.13919
[470]	train-mlogloss:1.10317	validation-mlogloss:1.13905
[471]	train-mlogloss:1.10302	validation-mlogloss:1.13899
[472]	train-mlogloss:1.10293	validation-mlogloss:1.13895
[473]	train-mlogloss:1.10273	validation-mlogloss:1.13884
[474]	train-mlogloss:1.10257	validation-mlogloss:1.13872
[475]	train-mlogloss:1.10246	validation-mlogloss:1.13868
[476]	train-mlogloss:1.10220	validation-mlogloss:1.13844
[477]	train-mlogloss:1.10204	validation-mlogloss:1.13832
[478]	train-mlogloss:1.10186	validation-mlogloss:1.13820
[479]	train-mlogloss:1.10178	validation-mlogloss:1.13820
[480]	train-mlogloss:1.10161	validation-mlogloss:1.13808
[481]	train-mlogloss:1.10149	validation-mlogloss:1.13800
[482]	train-mlogloss:1.10139	validation-mlogloss:1.13794
[483]	train-mlogloss:1.10129	validation-mlogloss:1.13791
[484]	train-mlogloss:1.10120	validation-mlogloss:1.13792
[485]	train-mlogloss:1.10102	validation-mlogloss:1.13788
[486]	train-mlogloss:1.10091	validation-mlogloss:1.13775
[487]	train-mlogloss:1.10075	validation-mlogloss:1.13765
[488]	train-mlogloss:1.10051	validation-mlogloss:1.13753
[489]	train-mlogloss:1.10044	validation-mlogloss:1.13751
Saved model to disk.
2025-10-26 05:49:55.940826: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 05:49:55.956747: 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 00:00:1761454195.975396 2518850 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761454195.981298 2518850 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761454195.995181 2518850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761454195.995285 2518850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761454195.995343 2518850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761454195.995399 2518850 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 05:49:55.999564: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[1]	train-mlogloss:2.64179	validation-mlogloss:2.64501
[2]	train-mlogloss:2.61132	validation-mlogloss:2.61496
[3]	train-mlogloss:2.58090	validation-mlogloss:2.58490
[4]	train-mlogloss:2.55345	validation-mlogloss:2.55877
[5]	train-mlogloss:2.52721	validation-mlogloss:2.53346
[6]	train-mlogloss:2.50050	validation-mlogloss:2.50846
[7]	train-mlogloss:2.47617	validation-mlogloss:2.48512
[8]	train-mlogloss:2.45135	validation-mlogloss:2.45920
[9]	train-mlogloss:2.42701	validation-mlogloss:2.43505
[10]	train-mlogloss:2.40452	validation-mlogloss:2.41357
[11]	train-mlogloss:2.38212	validation-mlogloss:2.39132
[12]	train-mlogloss:2.36037	validation-mlogloss:2.36958
[13]	train-mlogloss:2.33998	validation-mlogloss:2.34842
[14]	train-mlogloss:2.32012	validation-mlogloss:2.32870
[15]	train-mlogloss:2.30030	validation-mlogloss:2.30866
[16]	train-mlogloss:2.28082	validation-mlogloss:2.28873
[17]	train-mlogloss:2.26261	validation-mlogloss:2.27075
[18]	train-mlogloss:2.24446	validation-mlogloss:2.25309
[19]	train-mlogloss:2.22669	validation-mlogloss:2.23531
[20]	train-mlogloss:2.20964	validation-mlogloss:2.21816
[21]	train-mlogloss:2.19236	validation-mlogloss:2.20077
[22]	train-mlogloss:2.17636	validation-mlogloss:2.18451
[23]	train-mlogloss:2.16102	validation-mlogloss:2.16936
[24]	train-mlogloss:2.14501	validation-mlogloss:2.15371
[25]	train-mlogloss:2.12968	validation-mlogloss:2.13859
[26]	train-mlogloss:2.11494	validation-mlogloss:2.12419
[27]	train-mlogloss:2.10002	validation-mlogloss:2.10953
[28]	train-mlogloss:2.08593	validation-mlogloss:2.09553
[29]	train-mlogloss:2.07198	validation-mlogloss:2.08138
[30]	train-mlogloss:2.05833	validation-mlogloss:2.06799
[31]	train-mlogloss:2.04490	validation-mlogloss:2.05441
[32]	train-mlogloss:2.03207	validation-mlogloss:2.04134
[33]	train-mlogloss:2.01951	validation-mlogloss:2.02916
[34]	train-mlogloss:2.00714	validation-mlogloss:2.01750
[35]	train-mlogloss:1.99461	validation-mlogloss:2.00523
[36]	train-mlogloss:1.98308	validation-mlogloss:1.99389
[37]	train-mlogloss:1.97078	validation-mlogloss:1.98164
[38]	train-mlogloss:1.95903	validation-mlogloss:1.96998
[39]	train-mlogloss:1.94781	validation-mlogloss:1.95898
[40]	train-mlogloss:1.93691	validation-mlogloss:1.94781
[41]	train-mlogloss:1.92601	validation-mlogloss:1.93692
[42]	train-mlogloss:1.91570	validation-mlogloss:1.92659
[43]	train-mlogloss:1.90531	validation-mlogloss:1.91648
[44]	train-mlogloss:1.89475	validation-mlogloss:1.90576
[45]	train-mlogloss:1.88432	validation-mlogloss:1.89517
[46]	train-mlogloss:1.87454	validation-mlogloss:1.88560
[47]	train-mlogloss:1.86478	validation-mlogloss:1.87601
[48]	train-mlogloss:1.85519	validation-mlogloss:1.86632
[49]	train-mlogloss:1.84566	validation-mlogloss:1.85660
[50]	train-mlogloss:1.83689	validation-mlogloss:1.84819
[51]	train-mlogloss:1.82782	validation-mlogloss:1.83875
[52]	train-mlogloss:1.81896	validation-mlogloss:1.82945
[53]	train-mlogloss:1.81026	validation-mlogloss:1.82086
[54]	train-mlogloss:1.80159	validation-mlogloss:1.81211
[55]	train-mlogloss:1.79278	validation-mlogloss:1.80320
[56]	train-mlogloss:1.78400	validation-mlogloss:1.79425
[57]	train-mlogloss:1.77604	validation-mlogloss:1.78631
[58]	train-mlogloss:1.76796	validation-mlogloss:1.77810
[59]	train-mlogloss:1.76017	validation-mlogloss:1.77033
[60]	train-mlogloss:1.75275	validation-mlogloss:1.76295
[61]	train-mlogloss:1.74476	validation-mlogloss:1.75488
[62]	train-mlogloss:1.73733	validation-mlogloss:1.74778
[63]	train-mlogloss:1.73006	validation-mlogloss:1.74023
[64]	train-mlogloss:1.72300	validation-mlogloss:1.73334
[65]	train-mlogloss:1.71595	validation-mlogloss:1.72583
[66]	train-mlogloss:1.70892	validation-mlogloss:1.71897
[67]	train-mlogloss:1.70202	validation-mlogloss:1.71196
[68]	train-mlogloss:1.69495	validation-mlogloss:1.70489
[69]	train-mlogloss:1.68807	validation-mlogloss:1.69773
[70]	train-mlogloss:1.68164	validation-mlogloss:1.69116
[71]	train-mlogloss:1.67494	validation-mlogloss:1.68463
[72]	train-mlogloss:1.66868	validation-mlogloss:1.67816
[73]	train-mlogloss:1.66232	validation-mlogloss:1.67182
[74]	train-mlogloss:1.65625	validation-mlogloss:1.66574
[75]	train-mlogloss:1.65014	validation-mlogloss:1.65975
[76]	train-mlogloss:1.64411	validation-mlogloss:1.65383
[77]	train-mlogloss:1.63838	validation-mlogloss:1.64794
[78]	train-mlogloss:1.63233	validation-mlogloss:1.64179
[79]	train-mlogloss:1.62661	validation-mlogloss:1.63601
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[94]	train-mlogloss:1.54782	validation-mlogloss:1.55629
[95]	train-mlogloss:1.54309	validation-mlogloss:1.55177
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[99]	train-mlogloss:1.52496	validation-mlogloss:1.53445
[100]	train-mlogloss:1.52067	validation-mlogloss:1.53007
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[102]	train-mlogloss:1.51189	validation-mlogloss:1.52123
[103]	train-mlogloss:1.50762	validation-mlogloss:1.51679
[104]	train-mlogloss:1.50354	validation-mlogloss:1.51267
[105]	train-mlogloss:1.49928	validation-mlogloss:1.50838
[106]	train-mlogloss:1.49519	validation-mlogloss:1.50424
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[108]	train-mlogloss:1.48721	validation-mlogloss:1.49627
[109]	train-mlogloss:1.48326	validation-mlogloss:1.49219
[110]	train-mlogloss:1.47923	validation-mlogloss:1.48824
[111]	train-mlogloss:1.47539	validation-mlogloss:1.48430
[112]	train-mlogloss:1.47169	validation-mlogloss:1.48066
[113]	train-mlogloss:1.46793	validation-mlogloss:1.47692
[114]	train-mlogloss:1.46439	validation-mlogloss:1.47356
[115]	train-mlogloss:1.46052	validation-mlogloss:1.46958
[116]	train-mlogloss:1.45678	validation-mlogloss:1.46572
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[121]	train-mlogloss:1.43952	validation-mlogloss:1.44808
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[123]	train-mlogloss:1.43308	validation-mlogloss:1.44128
[124]	train-mlogloss:1.42972	validation-mlogloss:1.43792
[125]	train-mlogloss:1.42636	validation-mlogloss:1.43443
[126]	train-mlogloss:1.42318	validation-mlogloss:1.43136
[127]	train-mlogloss:1.42015	validation-mlogloss:1.42826
[128]	train-mlogloss:1.41699	validation-mlogloss:1.42488
[129]	train-mlogloss:1.41370	validation-mlogloss:1.42133
[130]	train-mlogloss:1.41061	validation-mlogloss:1.41818
[131]	train-mlogloss:1.40742	validation-mlogloss:1.41508
[132]	train-mlogloss:1.40444	validation-mlogloss:1.41209
[133]	train-mlogloss:1.40141	validation-mlogloss:1.40911
[134]	train-mlogloss:1.39846	validation-mlogloss:1.40609
[135]	train-mlogloss:1.39543	validation-mlogloss:1.40296
[136]	train-mlogloss:1.39241	validation-mlogloss:1.39997
[137]	train-mlogloss:1.38952	validation-mlogloss:1.39714
[138]	train-mlogloss:1.38664	validation-mlogloss:1.39431
[139]	train-mlogloss:1.38390	validation-mlogloss:1.39148
[140]	train-mlogloss:1.38133	validation-mlogloss:1.38908
[141]	train-mlogloss:1.37859	validation-mlogloss:1.38619
[142]	train-mlogloss:1.37593	validation-mlogloss:1.38350
[143]	train-mlogloss:1.37329	validation-mlogloss:1.38091
[144]	train-mlogloss:1.37056	validation-mlogloss:1.37825
[145]	train-mlogloss:1.36809	validation-mlogloss:1.37584
[146]	train-mlogloss:1.36572	validation-mlogloss:1.37370
[147]	train-mlogloss:1.36324	validation-mlogloss:1.37122
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[451]	train-mlogloss:1.10551	validation-mlogloss:1.14041
[452]	train-mlogloss:1.10541	validation-mlogloss:1.14040
[453]	train-mlogloss:1.10530	validation-mlogloss:1.14032
[454]	train-mlogloss:1.10521	validation-mlogloss:1.14022
[455]	train-mlogloss:1.10512	validation-mlogloss:1.14015
[456]	train-mlogloss:1.10489	validation-mlogloss:1.14000
[457]	train-mlogloss:1.10475	validation-mlogloss:1.13992
[458]	train-mlogloss:1.10473	validation-mlogloss:1.13990
[459]	train-mlogloss:1.10458	validation-mlogloss:1.13976
[460]	train-mlogloss:1.10444	validation-mlogloss:1.13965
[461]	train-mlogloss:1.10425	validation-mlogloss:1.13947
[462]	train-mlogloss:1.10407	validation-mlogloss:1.13934
[463]	train-mlogloss:1.10384	validation-mlogloss:1.13917
[464]	train-mlogloss:1.10365	validation-mlogloss:1.13906
[465]	train-mlogloss:1.10352	validation-mlogloss:1.13898
[466]	train-mlogloss:1.10346	validation-mlogloss:1.13897
[467]	train-mlogloss:1.10336	validation-mlogloss:1.13891
[468]	train-mlogloss:1.10321	validation-mlogloss:1.13885
[469]	train-mlogloss:1.10308	validation-mlogloss:1.13876
[470]	train-mlogloss:1.10295	validation-mlogloss:1.13867
[471]	train-mlogloss:1.10287	validation-mlogloss:1.13859
[472]	train-mlogloss:1.10280	validation-mlogloss:1.13850
[473]	train-mlogloss:1.10261	validation-mlogloss:1.13837
[474]	train-mlogloss:1.10253	validation-mlogloss:1.13833
[475]	train-mlogloss:1.10238	validation-mlogloss:1.13826
[476]	train-mlogloss:1.10233	validation-mlogloss:1.13824
[477]	train-mlogloss:1.10223	validation-mlogloss:1.13816
[478]	train-mlogloss:1.10211	validation-mlogloss:1.13806
[479]	train-mlogloss:1.10201	validation-mlogloss:1.13800
[480]	train-mlogloss:1.10194	validation-mlogloss:1.13796
[481]	train-mlogloss:1.10175	validation-mlogloss:1.13785
[482]	train-mlogloss:1.10167	validation-mlogloss:1.13783
[483]	train-mlogloss:1.10153	validation-mlogloss:1.13778
[484]	train-mlogloss:1.10134	validation-mlogloss:1.13769
[485]	train-mlogloss:1.10108	validation-mlogloss:1.13746
[486]	train-mlogloss:1.10094	validation-mlogloss:1.13740
[487]	train-mlogloss:1.10079	validation-mlogloss:1.13727
[488]	train-mlogloss:1.10059	validation-mlogloss:1.13716
[489]	train-mlogloss:1.10048	validation-mlogloss:1.13709
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.66 [%]
Global accuracy score (test) = 63.15 [%]
Global F1 score (train) = 66.46 [%]
Global F1 score (test) = 62.35 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.50      0.43      0.47       161
 CAMINAR CON MÓVIL O LIBRO       0.41      0.57      0.48       161
       CAMINAR USUAL SPEED       0.46      0.29      0.35       161
            CAMINAR ZIGZAG       0.71      0.57      0.63       161
          DE PIE BARRIENDO       0.72      0.61      0.66       161
   DE PIE DOBLANDO TOALLAS       0.44      0.53      0.48       161
    DE PIE MOVIENDO LIBROS       0.52      0.49      0.50       161
          DE PIE USANDO PC       0.83      0.93      0.88       161
        FASE REPOSO CON K5       0.77      0.93      0.84       161
INCREMENTAL CICLOERGOMETRO       1.00      0.98      0.99       161
           SENTADO LEYENDO       0.61      0.40      0.49       161
         SENTADO USANDO PC       0.49      0.61      0.54       161
      SENTADO VIENDO LA TV       0.49      0.32      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 10: 63.15 [%]
F1-score capturado en la ejecución 10: 62.35 [%]

=== EJECUCIÓN 11 ===

--- TRAIN (ejecución 11) ---

--- TEST (ejecución 11) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.47 [%]
Global accuracy score (test) = 63.31 [%]
Global F1 score (train) = 66.27 [%]
Global F1 score (test) = 62.5 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.49      0.45      0.47       161
 CAMINAR CON MÓVIL O LIBRO       0.40      0.58      0.47       161
       CAMINAR USUAL SPEED       0.51      0.27      0.36       161
            CAMINAR ZIGZAG       0.70      0.58      0.63       161
          DE PIE BARRIENDO       0.72      0.61      0.66       161
   DE PIE DOBLANDO TOALLAS       0.45      0.57      0.50       161
    DE PIE MOVIENDO LIBROS       0.54      0.48      0.51       161
          DE PIE USANDO PC       0.84      0.93      0.88       161
        FASE REPOSO CON K5       0.79      0.93      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.56      0.34      0.42       161
         SENTADO USANDO PC       0.47      0.61      0.53       161
      SENTADO VIENDO LA TV       0.51      0.36      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.65      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.63      2415
              weighted avg       0.64      0.63      0.63      2415


Accuracy capturado en la ejecución 11: 63.31 [%]
F1-score capturado en la ejecución 11: 62.5 [%]

=== EJECUCIÓN 12 ===

--- TRAIN (ejecución 12) ---

--- TEST (ejecución 12) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  6  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.9 [%]
Global accuracy score (test) = 62.61 [%]
Global F1 score (train) = 66.72 [%]
Global F1 score (test) = 61.71 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.50      0.42      0.45       161
 CAMINAR CON MÓVIL O LIBRO       0.43      0.59      0.49       161
       CAMINAR USUAL SPEED       0.40      0.29      0.33       161
            CAMINAR ZIGZAG       0.72      0.59      0.65       161
          DE PIE BARRIENDO       0.75      0.62      0.68       161
   DE PIE DOBLANDO TOALLAS       0.45      0.57      0.50       161
    DE PIE MOVIENDO LIBROS       0.51      0.45      0.48       161
          DE PIE USANDO PC       0.81      0.94      0.87       161
        FASE REPOSO CON K5       0.77      0.92      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.54      0.30      0.39       161
         SENTADO USANDO PC       0.47      0.59      0.52       161
      SENTADO VIENDO LA TV       0.46      0.35      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.65      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 12: 62.61 [%]
F1-score capturado en la ejecución 12: 61.71 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-10-26 06:28:22.012133: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 06:28:22.027811: 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 00:00:1761456502.046494 2557193 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761456502.052217 2557193 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761456502.066450 2557193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761456502.066560 2557193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761456502.066618 2557193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761456502.066673 2557193 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 06:28:22.070703: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67512	validation-mlogloss:2.67592
[1]	train-mlogloss:2.64284	validation-mlogloss:2.64383
[2]	train-mlogloss:2.61258	validation-mlogloss:2.61361
[3]	train-mlogloss:2.58275	validation-mlogloss:2.58485
[4]	train-mlogloss:2.55441	validation-mlogloss:2.55778
[5]	train-mlogloss:2.52623	validation-mlogloss:2.53014
[6]	train-mlogloss:2.49929	validation-mlogloss:2.50277
[7]	train-mlogloss:2.47369	validation-mlogloss:2.47790
[8]	train-mlogloss:2.44934	validation-mlogloss:2.45484
[9]	train-mlogloss:2.42535	validation-mlogloss:2.43107
[10]	train-mlogloss:2.40304	validation-mlogloss:2.40952
[11]	train-mlogloss:2.38127	validation-mlogloss:2.38796
[12]	train-mlogloss:2.35966	validation-mlogloss:2.36665
[13]	train-mlogloss:2.33852	validation-mlogloss:2.34579
[14]	train-mlogloss:2.31805	validation-mlogloss:2.32471
[15]	train-mlogloss:2.29763	validation-mlogloss:2.30502
[16]	train-mlogloss:2.27902	validation-mlogloss:2.28680
[17]	train-mlogloss:2.26025	validation-mlogloss:2.26926
[18]	train-mlogloss:2.24229	validation-mlogloss:2.25203
[19]	train-mlogloss:2.22448	validation-mlogloss:2.23500
[20]	train-mlogloss:2.20743	validation-mlogloss:2.21814
[21]	train-mlogloss:2.19092	validation-mlogloss:2.20165
[22]	train-mlogloss:2.17519	validation-mlogloss:2.18607
[23]	train-mlogloss:2.15904	validation-mlogloss:2.17012
[24]	train-mlogloss:2.14409	validation-mlogloss:2.15523
[25]	train-mlogloss:2.12904	validation-mlogloss:2.14062
[26]	train-mlogloss:2.11424	validation-mlogloss:2.12625
[27]	train-mlogloss:2.10069	validation-mlogloss:2.11304
[28]	train-mlogloss:2.08674	validation-mlogloss:2.09925
[29]	train-mlogloss:2.07248	validation-mlogloss:2.08505
[30]	train-mlogloss:2.05858	validation-mlogloss:2.07165
[31]	train-mlogloss:2.04605	validation-mlogloss:2.05969
[32]	train-mlogloss:2.03286	validation-mlogloss:2.04684
[33]	train-mlogloss:2.02014	validation-mlogloss:2.03390
[34]	train-mlogloss:2.00797	validation-mlogloss:2.02163
[35]	train-mlogloss:1.99552	validation-mlogloss:2.00891
[36]	train-mlogloss:1.98360	validation-mlogloss:1.99736
[37]	train-mlogloss:1.97159	validation-mlogloss:1.98566
[38]	train-mlogloss:1.96019	validation-mlogloss:1.97411
[39]	train-mlogloss:1.94819	validation-mlogloss:1.96218
[40]	train-mlogloss:1.93701	validation-mlogloss:1.95138
[41]	train-mlogloss:1.92620	validation-mlogloss:1.94044
[42]	train-mlogloss:1.91607	validation-mlogloss:1.93030
[43]	train-mlogloss:1.90578	validation-mlogloss:1.92032
[44]	train-mlogloss:1.89514	validation-mlogloss:1.90970
[45]	train-mlogloss:1.88509	validation-mlogloss:1.89944
[46]	train-mlogloss:1.87492	validation-mlogloss:1.88922
[47]	train-mlogloss:1.86521	validation-mlogloss:1.87961
[48]	train-mlogloss:1.85567	validation-mlogloss:1.86986
[49]	train-mlogloss:1.84590	validation-mlogloss:1.86014
[50]	train-mlogloss:1.83690	validation-mlogloss:1.85119
[51]	train-mlogloss:1.82781	validation-mlogloss:1.84217
[52]	train-mlogloss:1.81843	validation-mlogloss:1.83256
[53]	train-mlogloss:1.80981	validation-mlogloss:1.82392
[54]	train-mlogloss:1.80107	validation-mlogloss:1.81510
[55]	train-mlogloss:1.79257	validation-mlogloss:1.80621
[56]	train-mlogloss:1.78424	validation-mlogloss:1.79780
[57]	train-mlogloss:1.77563	validation-mlogloss:1.78908
[58]	train-mlogloss:1.76754	validation-mlogloss:1.78077
[59]	train-mlogloss:1.75950	validation-mlogloss:1.77278
[60]	train-mlogloss:1.75168	validation-mlogloss:1.76493
[61]	train-mlogloss:1.74365	validation-mlogloss:1.75675
[62]	train-mlogloss:1.73627	validation-mlogloss:1.74946
[63]	train-mlogloss:1.72892	validation-mlogloss:1.74213
[64]	train-mlogloss:1.72168	validation-mlogloss:1.73481
[65]	train-mlogloss:1.71444	validation-mlogloss:1.72755
[66]	train-mlogloss:1.70745	validation-mlogloss:1.72067
[67]	train-mlogloss:1.70020	validation-mlogloss:1.71302
[68]	train-mlogloss:1.69306	validation-mlogloss:1.70594
[69]	train-mlogloss:1.68629	validation-mlogloss:1.69925
[70]	train-mlogloss:1.67923	validation-mlogloss:1.69225
[71]	train-mlogloss:1.67280	validation-mlogloss:1.68569
[72]	train-mlogloss:1.66626	validation-mlogloss:1.67913
[73]	train-mlogloss:1.65978	validation-mlogloss:1.67250
[74]	train-mlogloss:1.65361	validation-mlogloss:1.66648
[75]	train-mlogloss:1.64780	validation-mlogloss:1.66052
[76]	train-mlogloss:1.64172	validation-mlogloss:1.65457
[77]	train-mlogloss:1.63587	validation-mlogloss:1.64862
[78]	train-mlogloss:1.63015	validation-mlogloss:1.64318
[79]	train-mlogloss:1.62427	validation-mlogloss:1.63744
[80]	train-mlogloss:1.61884	validation-mlogloss:1.63201
[81]	train-mlogloss:1.61312	validation-mlogloss:1.62588
[82]	train-mlogloss:1.60735	validation-mlogloss:1.61983
[83]	train-mlogloss:1.60197	validation-mlogloss:1.61460
[84]	train-mlogloss:1.59676	validation-mlogloss:1.60930
[85]	train-mlogloss:1.59157	validation-mlogloss:1.60420
[86]	train-mlogloss:1.58635	validation-mlogloss:1.59898
[87]	train-mlogloss:1.58122	validation-mlogloss:1.59404
[88]	train-mlogloss:1.57600	validation-mlogloss:1.58867
[89]	train-mlogloss:1.57062	validation-mlogloss:1.58327
[90]	train-mlogloss:1.56560	validation-mlogloss:1.57810
[91]	train-mlogloss:1.56034	validation-mlogloss:1.57277
[92]	train-mlogloss:1.55543	validation-mlogloss:1.56768
[93]	train-mlogloss:1.55044	validation-mlogloss:1.56261
[94]	train-mlogloss:1.54586	validation-mlogloss:1.55806
[95]	train-mlogloss:1.54104	validation-mlogloss:1.55306
[96]	train-mlogloss:1.53668	validation-mlogloss:1.54866
[97]	train-mlogloss:1.53232	validation-mlogloss:1.54392
[98]	train-mlogloss:1.52799	validation-mlogloss:1.53958
[99]	train-mlogloss:1.52373	validation-mlogloss:1.53509
[100]	train-mlogloss:1.51920	validation-mlogloss:1.53052
[101]	train-mlogloss:1.51483	validation-mlogloss:1.52597
[102]	train-mlogloss:1.51059	validation-mlogloss:1.52179
[103]	train-mlogloss:1.50640	validation-mlogloss:1.51745
[104]	train-mlogloss:1.50199	validation-mlogloss:1.51319
[105]	train-mlogloss:1.49800	validation-mlogloss:1.50933
[106]	train-mlogloss:1.49413	validation-mlogloss:1.50537
[107]	train-mlogloss:1.49033	validation-mlogloss:1.50168
[108]	train-mlogloss:1.48614	validation-mlogloss:1.49741
[109]	train-mlogloss:1.48256	validation-mlogloss:1.49401
[110]	train-mlogloss:1.47871	validation-mlogloss:1.49016
[111]	train-mlogloss:1.47490	validation-mlogloss:1.48638
[112]	train-mlogloss:1.47131	validation-mlogloss:1.48276
[113]	train-mlogloss:1.46762	validation-mlogloss:1.47909
[114]	train-mlogloss:1.46362	validation-mlogloss:1.47494
[115]	train-mlogloss:1.45991	validation-mlogloss:1.47118
[116]	train-mlogloss:1.45633	validation-mlogloss:1.46751
[117]	train-mlogloss:1.45282	validation-mlogloss:1.46384
[118]	train-mlogloss:1.44943	validation-mlogloss:1.46048
[119]	train-mlogloss:1.44613	validation-mlogloss:1.45728
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[423]	train-mlogloss:1.11019	validation-mlogloss:1.14301
[424]	train-mlogloss:1.11000	validation-mlogloss:1.14285
[425]	train-mlogloss:1.10973	validation-mlogloss:1.14272
[426]	train-mlogloss:1.10955	validation-mlogloss:1.14251
[427]	train-mlogloss:1.10935	validation-mlogloss:1.14248
[428]	train-mlogloss:1.10910	validation-mlogloss:1.14237
[429]	train-mlogloss:1.10882	validation-mlogloss:1.14213
[430]	train-mlogloss:1.10865	validation-mlogloss:1.14208
[431]	train-mlogloss:1.10852	validation-mlogloss:1.14199
[432]	train-mlogloss:1.10831	validation-mlogloss:1.14181
[433]	train-mlogloss:1.10815	validation-mlogloss:1.14165
[434]	train-mlogloss:1.10802	validation-mlogloss:1.14154
[435]	train-mlogloss:1.10784	validation-mlogloss:1.14144
[436]	train-mlogloss:1.10776	validation-mlogloss:1.14134
[437]	train-mlogloss:1.10755	validation-mlogloss:1.14119
[438]	train-mlogloss:1.10731	validation-mlogloss:1.14099
[439]	train-mlogloss:1.10719	validation-mlogloss:1.14091
[440]	train-mlogloss:1.10697	validation-mlogloss:1.14072
[441]	train-mlogloss:1.10691	validation-mlogloss:1.14070
[442]	train-mlogloss:1.10666	validation-mlogloss:1.14050
[443]	train-mlogloss:1.10642	validation-mlogloss:1.14040
[444]	train-mlogloss:1.10626	validation-mlogloss:1.14029
[445]	train-mlogloss:1.10612	validation-mlogloss:1.14022
[446]	train-mlogloss:1.10577	validation-mlogloss:1.13995
[447]	train-mlogloss:1.10563	validation-mlogloss:1.13981
[448]	train-mlogloss:1.10539	validation-mlogloss:1.13968
[449]	train-mlogloss:1.10512	validation-mlogloss:1.13958
[450]	train-mlogloss:1.10496	validation-mlogloss:1.13947
[451]	train-mlogloss:1.10478	validation-mlogloss:1.13931
[452]	train-mlogloss:1.10462	validation-mlogloss:1.13912
[453]	train-mlogloss:1.10449	validation-mlogloss:1.13897
[454]	train-mlogloss:1.10437	validation-mlogloss:1.13890
[455]	train-mlogloss:1.10419	validation-mlogloss:1.13882
[456]	train-mlogloss:1.10407	validation-mlogloss:1.13872
[457]	train-mlogloss:1.10394	validation-mlogloss:1.13858
[458]	train-mlogloss:1.10377	validation-mlogloss:1.13844
[459]	train-mlogloss:1.10369	validation-mlogloss:1.13839
[460]	train-mlogloss:1.10362	validation-mlogloss:1.13828
[461]	train-mlogloss:1.10341	validation-mlogloss:1.13812
[462]	train-mlogloss:1.10336	validation-mlogloss:1.13809
[463]	train-mlogloss:1.10333	validation-mlogloss:1.13808
[464]	train-mlogloss:1.10317	validation-mlogloss:1.13792
[465]	train-mlogloss:1.10292	validation-mlogloss:1.13786
[466]	train-mlogloss:1.10268	validation-mlogloss:1.13772
[467]	train-mlogloss:1.10256	validation-mlogloss:1.13767
[468]	train-mlogloss:1.10238	validation-mlogloss:1.13754
[469]	train-mlogloss:1.10220	validation-mlogloss:1.13741
[470]	train-mlogloss:1.10207	validation-mlogloss:1.13732
[471]	train-mlogloss:1.10193	validation-mlogloss:1.13720
[472]	train-mlogloss:1.10182	validation-mlogloss:1.13708
[473]	train-mlogloss:1.10148	validation-mlogloss:1.13682
[474]	train-mlogloss:1.10129	validation-mlogloss:1.13669
[475]	train-mlogloss:1.10111	validation-mlogloss:1.13653
[476]	train-mlogloss:1.10102	validation-mlogloss:1.13646
[477]	train-mlogloss:1.10086	validation-mlogloss:1.13636
[478]	train-mlogloss:1.10078	validation-mlogloss:1.13628
[479]	train-mlogloss:1.10062	validation-mlogloss:1.13618
[480]	train-mlogloss:1.10046	validation-mlogloss:1.13610
[481]	train-mlogloss:1.10041	validation-mlogloss:1.13606
[482]	train-mlogloss:1.10022	validation-mlogloss:1.13590
[483]	train-mlogloss:1.10006	validation-mlogloss:1.13585
[484]	train-mlogloss:1.09993	validation-mlogloss:1.13582
[485]	train-mlogloss:1.09972	validation-mlogloss:1.13571
[486]	train-mlogloss:1.09962	validation-mlogloss:1.13559
[487]	train-mlogloss:1.09949	validation-mlogloss:1.13550
[488]	train-mlogloss:1.09944	validation-mlogloss:1.13548
[489]	train-mlogloss:1.09930	validation-mlogloss:1.13541
Saved model to disk.
2025-10-26 07:05:55.779990: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:05:55.799813: 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 00:00:1761458755.818232 2595090 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761458755.823968 2595090 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761458755.849830 2595090 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761458755.849922 2595090 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761458755.849975 2595090 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761458755.850023 2595090 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:05:55.858113: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67454	validation-mlogloss:2.67459
[1]	train-mlogloss:2.64295	validation-mlogloss:2.64297
[2]	train-mlogloss:2.61125	validation-mlogloss:2.61240
[3]	train-mlogloss:2.58215	validation-mlogloss:2.58466
[4]	train-mlogloss:2.55365	validation-mlogloss:2.55706
[5]	train-mlogloss:2.52714	validation-mlogloss:2.53143
[6]	train-mlogloss:2.50058	validation-mlogloss:2.50595
[7]	train-mlogloss:2.47502	validation-mlogloss:2.48116
[8]	train-mlogloss:2.44980	validation-mlogloss:2.45569
[9]	train-mlogloss:2.42552	validation-mlogloss:2.43217
[10]	train-mlogloss:2.40373	validation-mlogloss:2.40983
[11]	train-mlogloss:2.38196	validation-mlogloss:2.38873
[12]	train-mlogloss:2.35996	validation-mlogloss:2.36734
[13]	train-mlogloss:2.33867	validation-mlogloss:2.34650
[14]	train-mlogloss:2.31773	validation-mlogloss:2.32608
[15]	train-mlogloss:2.29785	validation-mlogloss:2.30672
[16]	train-mlogloss:2.27903	validation-mlogloss:2.28851
[17]	train-mlogloss:2.25965	validation-mlogloss:2.27022
[18]	train-mlogloss:2.24123	validation-mlogloss:2.25144
[19]	train-mlogloss:2.22396	validation-mlogloss:2.23451
[20]	train-mlogloss:2.20686	validation-mlogloss:2.21812
[21]	train-mlogloss:2.19063	validation-mlogloss:2.20205
[22]	train-mlogloss:2.17505	validation-mlogloss:2.18652
[23]	train-mlogloss:2.15955	validation-mlogloss:2.17132
[24]	train-mlogloss:2.14372	validation-mlogloss:2.15579
[25]	train-mlogloss:2.12857	validation-mlogloss:2.14094
[26]	train-mlogloss:2.11405	validation-mlogloss:2.12655
[27]	train-mlogloss:2.09976	validation-mlogloss:2.11231
[28]	train-mlogloss:2.08640	validation-mlogloss:2.09855
[29]	train-mlogloss:2.07190	validation-mlogloss:2.08420
[30]	train-mlogloss:2.05884	validation-mlogloss:2.07113
[31]	train-mlogloss:2.04585	validation-mlogloss:2.05786
[32]	train-mlogloss:2.03226	validation-mlogloss:2.04460
[33]	train-mlogloss:2.01975	validation-mlogloss:2.03167
[34]	train-mlogloss:2.00674	validation-mlogloss:2.01814
[35]	train-mlogloss:1.99454	validation-mlogloss:2.00610
[36]	train-mlogloss:1.98221	validation-mlogloss:1.99356
[37]	train-mlogloss:1.97037	validation-mlogloss:1.98136
[38]	train-mlogloss:1.95885	validation-mlogloss:1.96995
[39]	train-mlogloss:1.94779	validation-mlogloss:1.95898
[40]	train-mlogloss:1.93683	validation-mlogloss:1.94798
[41]	train-mlogloss:1.92632	validation-mlogloss:1.93757
[42]	train-mlogloss:1.91541	validation-mlogloss:1.92654
[43]	train-mlogloss:1.90508	validation-mlogloss:1.91645
[44]	train-mlogloss:1.89473	validation-mlogloss:1.90660
[45]	train-mlogloss:1.88499	validation-mlogloss:1.89701
[46]	train-mlogloss:1.87488	validation-mlogloss:1.88702
[47]	train-mlogloss:1.86497	validation-mlogloss:1.87723
[48]	train-mlogloss:1.85555	validation-mlogloss:1.86794
[49]	train-mlogloss:1.84591	validation-mlogloss:1.85811
[50]	train-mlogloss:1.83651	validation-mlogloss:1.84883
[51]	train-mlogloss:1.82698	validation-mlogloss:1.83953
[52]	train-mlogloss:1.81794	validation-mlogloss:1.83039
[53]	train-mlogloss:1.80909	validation-mlogloss:1.82135
[54]	train-mlogloss:1.80022	validation-mlogloss:1.81243
[55]	train-mlogloss:1.79218	validation-mlogloss:1.80471
[56]	train-mlogloss:1.78355	validation-mlogloss:1.79627
[57]	train-mlogloss:1.77559	validation-mlogloss:1.78818
[58]	train-mlogloss:1.76760	validation-mlogloss:1.78020
[59]	train-mlogloss:1.75967	validation-mlogloss:1.77218
[60]	train-mlogloss:1.75193	validation-mlogloss:1.76462
[61]	train-mlogloss:1.74457	validation-mlogloss:1.75739
[62]	train-mlogloss:1.73725	validation-mlogloss:1.75018
[63]	train-mlogloss:1.73013	validation-mlogloss:1.74311
[64]	train-mlogloss:1.72276	validation-mlogloss:1.73561
[65]	train-mlogloss:1.71557	validation-mlogloss:1.72840
[66]	train-mlogloss:1.70831	validation-mlogloss:1.72115
[67]	train-mlogloss:1.70125	validation-mlogloss:1.71395
[68]	train-mlogloss:1.69466	validation-mlogloss:1.70722
[69]	train-mlogloss:1.68800	validation-mlogloss:1.70038
[70]	train-mlogloss:1.68132	validation-mlogloss:1.69372
[71]	train-mlogloss:1.67473	validation-mlogloss:1.68687
[72]	train-mlogloss:1.66857	validation-mlogloss:1.68083
[73]	train-mlogloss:1.66178	validation-mlogloss:1.67402
[74]	train-mlogloss:1.65537	validation-mlogloss:1.66761
[75]	train-mlogloss:1.64937	validation-mlogloss:1.66129
[76]	train-mlogloss:1.64311	validation-mlogloss:1.65505
[77]	train-mlogloss:1.63689	validation-mlogloss:1.64869
[78]	train-mlogloss:1.63076	validation-mlogloss:1.64255
[79]	train-mlogloss:1.62501	validation-mlogloss:1.63667
[80]	train-mlogloss:1.61935	validation-mlogloss:1.63103
[81]	train-mlogloss:1.61373	validation-mlogloss:1.62542
[82]	train-mlogloss:1.60807	validation-mlogloss:1.61956
[83]	train-mlogloss:1.60244	validation-mlogloss:1.61406
[84]	train-mlogloss:1.59719	validation-mlogloss:1.60888
[85]	train-mlogloss:1.59189	validation-mlogloss:1.60340
[86]	train-mlogloss:1.58665	validation-mlogloss:1.59802
[87]	train-mlogloss:1.58139	validation-mlogloss:1.59257
[88]	train-mlogloss:1.57634	validation-mlogloss:1.58762
[89]	train-mlogloss:1.57118	validation-mlogloss:1.58218
[90]	train-mlogloss:1.56596	validation-mlogloss:1.57694
[91]	train-mlogloss:1.56106	validation-mlogloss:1.57200
[92]	train-mlogloss:1.55621	validation-mlogloss:1.56753
[93]	train-mlogloss:1.55123	validation-mlogloss:1.56236
[94]	train-mlogloss:1.54669	validation-mlogloss:1.55805
[95]	train-mlogloss:1.54208	validation-mlogloss:1.55347
[96]	train-mlogloss:1.53763	validation-mlogloss:1.54916
[97]	train-mlogloss:1.53306	validation-mlogloss:1.54438
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[99]	train-mlogloss:1.52378	validation-mlogloss:1.53510
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[102]	train-mlogloss:1.51053	validation-mlogloss:1.52129
[103]	train-mlogloss:1.50615	validation-mlogloss:1.51673
[104]	train-mlogloss:1.50211	validation-mlogloss:1.51297
[105]	train-mlogloss:1.49792	validation-mlogloss:1.50876
[106]	train-mlogloss:1.49366	validation-mlogloss:1.50449
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[108]	train-mlogloss:1.48586	validation-mlogloss:1.49653
[109]	train-mlogloss:1.48212	validation-mlogloss:1.49269
[110]	train-mlogloss:1.47823	validation-mlogloss:1.48870
[111]	train-mlogloss:1.47446	validation-mlogloss:1.48483
[112]	train-mlogloss:1.47079	validation-mlogloss:1.48130
[113]	train-mlogloss:1.46730	validation-mlogloss:1.47751
[114]	train-mlogloss:1.46380	validation-mlogloss:1.47413
[115]	train-mlogloss:1.46030	validation-mlogloss:1.47060
[116]	train-mlogloss:1.45648	validation-mlogloss:1.46684
[117]	train-mlogloss:1.45294	validation-mlogloss:1.46324
[118]	train-mlogloss:1.44947	validation-mlogloss:1.45990
[119]	train-mlogloss:1.44597	validation-mlogloss:1.45642
[120]	train-mlogloss:1.44274	validation-mlogloss:1.45311
[121]	train-mlogloss:1.43951	validation-mlogloss:1.44983
[122]	train-mlogloss:1.43618	validation-mlogloss:1.44614
[123]	train-mlogloss:1.43304	validation-mlogloss:1.44288
[124]	train-mlogloss:1.42969	validation-mlogloss:1.43941
[125]	train-mlogloss:1.42634	validation-mlogloss:1.43591
[126]	train-mlogloss:1.42308	validation-mlogloss:1.43256
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[128]	train-mlogloss:1.41675	validation-mlogloss:1.42625
[129]	train-mlogloss:1.41357	validation-mlogloss:1.42311
[130]	train-mlogloss:1.41057	validation-mlogloss:1.42005
[131]	train-mlogloss:1.40748	validation-mlogloss:1.41683
[132]	train-mlogloss:1.40446	validation-mlogloss:1.41355
[133]	train-mlogloss:1.40155	validation-mlogloss:1.41067
[134]	train-mlogloss:1.39855	validation-mlogloss:1.40749
[135]	train-mlogloss:1.39558	validation-mlogloss:1.40435
[136]	train-mlogloss:1.39278	validation-mlogloss:1.40159
[137]	train-mlogloss:1.38981	validation-mlogloss:1.39861
[138]	train-mlogloss:1.38692	validation-mlogloss:1.39562
[139]	train-mlogloss:1.38403	validation-mlogloss:1.39279
[140]	train-mlogloss:1.38134	validation-mlogloss:1.39009
[141]	train-mlogloss:1.37858	validation-mlogloss:1.38740
[142]	train-mlogloss:1.37598	validation-mlogloss:1.38492
[143]	train-mlogloss:1.37317	validation-mlogloss:1.38219
[144]	train-mlogloss:1.37042	validation-mlogloss:1.37962
[145]	train-mlogloss:1.36768	validation-mlogloss:1.37679
[146]	train-mlogloss:1.36526	validation-mlogloss:1.37446
[147]	train-mlogloss:1.36273	validation-mlogloss:1.37197
[148]	train-mlogloss:1.36034	validation-mlogloss:1.36973
[149]	train-mlogloss:1.35789	validation-mlogloss:1.36733
[150]	train-mlogloss:1.35549	validation-mlogloss:1.36485
[151]	train-mlogloss:1.35307	validation-mlogloss:1.36244
[152]	train-mlogloss:1.35080	validation-mlogloss:1.36007
[153]	train-mlogloss:1.34829	validation-mlogloss:1.35762
[154]	train-mlogloss:1.34583	validation-mlogloss:1.35529
[155]	train-mlogloss:1.34359	validation-mlogloss:1.35307
[156]	train-mlogloss:1.34102	validation-mlogloss:1.35068
[157]	train-mlogloss:1.33867	validation-mlogloss:1.34834
[158]	train-mlogloss:1.33640	validation-mlogloss:1.34605
[159]	train-mlogloss:1.33415	validation-mlogloss:1.34359
[160]	train-mlogloss:1.33201	validation-mlogloss:1.34168
[161]	train-mlogloss:1.32998	validation-mlogloss:1.33969
[162]	train-mlogloss:1.32771	validation-mlogloss:1.33752
[163]	train-mlogloss:1.32551	validation-mlogloss:1.33534
[164]	train-mlogloss:1.32363	validation-mlogloss:1.33332
[165]	train-mlogloss:1.32151	validation-mlogloss:1.33122
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[167]	train-mlogloss:1.31729	validation-mlogloss:1.32682
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[170]	train-mlogloss:1.31091	validation-mlogloss:1.32059
[171]	train-mlogloss:1.30894	validation-mlogloss:1.31856
[172]	train-mlogloss:1.30696	validation-mlogloss:1.31660
[173]	train-mlogloss:1.30495	validation-mlogloss:1.31460
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[189]	train-mlogloss:1.27614	validation-mlogloss:1.28690
[190]	train-mlogloss:1.27444	validation-mlogloss:1.28534
[191]	train-mlogloss:1.27283	validation-mlogloss:1.28379
[192]	train-mlogloss:1.27131	validation-mlogloss:1.28221
[193]	train-mlogloss:1.26964	validation-mlogloss:1.28064
[194]	train-mlogloss:1.26794	validation-mlogloss:1.27902
[195]	train-mlogloss:1.26630	validation-mlogloss:1.27757
[196]	train-mlogloss:1.26472	validation-mlogloss:1.27613
[197]	train-mlogloss:1.26320	validation-mlogloss:1.27477
[198]	train-mlogloss:1.26170	validation-mlogloss:1.27333
[199]	train-mlogloss:1.26020	validation-mlogloss:1.27201
[200]	train-mlogloss:1.25871	validation-mlogloss:1.27065
[201]	train-mlogloss:1.25720	validation-mlogloss:1.26919
[202]	train-mlogloss:1.25562	validation-mlogloss:1.26785
[203]	train-mlogloss:1.25408	validation-mlogloss:1.26649
[204]	train-mlogloss:1.25256	validation-mlogloss:1.26509
[205]	train-mlogloss:1.25091	validation-mlogloss:1.26368
[206]	train-mlogloss:1.24959	validation-mlogloss:1.26244
[207]	train-mlogloss:1.24806	validation-mlogloss:1.26087
[208]	train-mlogloss:1.24654	validation-mlogloss:1.25955
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[486]	train-mlogloss:1.10072	validation-mlogloss:1.13546
[487]	train-mlogloss:1.10047	validation-mlogloss:1.13523
[488]	train-mlogloss:1.10039	validation-mlogloss:1.13518
[489]	train-mlogloss:1.10035	validation-mlogloss:1.13514
Saved model to disk.
2025-10-26 07:25:12.251638: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:25:12.264065: 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 00:00:1761459912.278009 2614531 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761459912.282247 2614531 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761459912.293295 2614531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459912.293316 2614531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459912.293319 2614531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459912.293321 2614531 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:25:12.296483: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[301]	train-mlogloss:1.15335	validation-mlogloss:1.17591
[302]	train-mlogloss:1.15276	validation-mlogloss:1.17545
[303]	train-mlogloss:1.15222	validation-mlogloss:1.17497
[304]	train-mlogloss:1.15146	validation-mlogloss:1.17428
[305]	train-mlogloss:1.15076	validation-mlogloss:1.17371
[306]	train-mlogloss:1.15008	validation-mlogloss:1.17307
[307]	train-mlogloss:1.14963	validation-mlogloss:1.17275
[308]	train-mlogloss:1.14910	validation-mlogloss:1.17233
[309]	train-mlogloss:1.14850	validation-mlogloss:1.17186
[310]	train-mlogloss:1.14792	validation-mlogloss:1.17130
[311]	train-mlogloss:1.14725	validation-mlogloss:1.17068
[312]	train-mlogloss:1.14668	validation-mlogloss:1.17030
[313]	train-mlogloss:1.14610	validation-mlogloss:1.16987
[314]	train-mlogloss:1.14543	validation-mlogloss:1.16935
[315]	train-mlogloss:1.14486	validation-mlogloss:1.16886
[316]	train-mlogloss:1.14429	validation-mlogloss:1.16835
[317]	train-mlogloss:1.14376	validation-mlogloss:1.16796
[318]	train-mlogloss:1.14311	validation-mlogloss:1.16744
[319]	train-mlogloss:1.14267	validation-mlogloss:1.16706
[320]	train-mlogloss:1.14219	validation-mlogloss:1.16662
[321]	train-mlogloss:1.14170	validation-mlogloss:1.16624
[322]	train-mlogloss:1.14119	validation-mlogloss:1.16590
[323]	train-mlogloss:1.14062	validation-mlogloss:1.16537
[324]	train-mlogloss:1.14015	validation-mlogloss:1.16491
[325]	train-mlogloss:1.13968	validation-mlogloss:1.16463
[326]	train-mlogloss:1.13926	validation-mlogloss:1.16436
[327]	train-mlogloss:1.13879	validation-mlogloss:1.16392
[328]	train-mlogloss:1.13837	validation-mlogloss:1.16357
[329]	train-mlogloss:1.13789	validation-mlogloss:1.16321
[330]	train-mlogloss:1.13735	validation-mlogloss:1.16268
[331]	train-mlogloss:1.13702	validation-mlogloss:1.16248
[332]	train-mlogloss:1.13664	validation-mlogloss:1.16222
[333]	train-mlogloss:1.13600	validation-mlogloss:1.16163
[334]	train-mlogloss:1.13545	validation-mlogloss:1.16116
[335]	train-mlogloss:1.13492	validation-mlogloss:1.16070
[336]	train-mlogloss:1.13461	validation-mlogloss:1.16039
[337]	train-mlogloss:1.13412	validation-mlogloss:1.15997
[338]	train-mlogloss:1.13346	validation-mlogloss:1.15935
[339]	train-mlogloss:1.13303	validation-mlogloss:1.15908
[340]	train-mlogloss:1.13265	validation-mlogloss:1.15882
[341]	train-mlogloss:1.13221	validation-mlogloss:1.15850
[342]	train-mlogloss:1.13178	validation-mlogloss:1.15820
[343]	train-mlogloss:1.13129	validation-mlogloss:1.15796
[344]	train-mlogloss:1.13090	validation-mlogloss:1.15770
[345]	train-mlogloss:1.13050	validation-mlogloss:1.15728
[346]	train-mlogloss:1.13005	validation-mlogloss:1.15685
[347]	train-mlogloss:1.12976	validation-mlogloss:1.15662
[348]	train-mlogloss:1.12937	validation-mlogloss:1.15632
[349]	train-mlogloss:1.12886	validation-mlogloss:1.15597
[350]	train-mlogloss:1.12839	validation-mlogloss:1.15562
[351]	train-mlogloss:1.12794	validation-mlogloss:1.15532
[352]	train-mlogloss:1.12754	validation-mlogloss:1.15507
[353]	train-mlogloss:1.12711	validation-mlogloss:1.15466
[354]	train-mlogloss:1.12667	validation-mlogloss:1.15438
[355]	train-mlogloss:1.12636	validation-mlogloss:1.15407
[356]	train-mlogloss:1.12591	validation-mlogloss:1.15378
[357]	train-mlogloss:1.12566	validation-mlogloss:1.15355
[358]	train-mlogloss:1.12534	validation-mlogloss:1.15326
[359]	train-mlogloss:1.12477	validation-mlogloss:1.15281
[360]	train-mlogloss:1.12463	validation-mlogloss:1.15271
[361]	train-mlogloss:1.12420	validation-mlogloss:1.15232
[362]	train-mlogloss:1.12389	validation-mlogloss:1.15206
[363]	train-mlogloss:1.12362	validation-mlogloss:1.15184
[364]	train-mlogloss:1.12336	validation-mlogloss:1.15163
[365]	train-mlogloss:1.12299	validation-mlogloss:1.15136
[366]	train-mlogloss:1.12265	validation-mlogloss:1.15107
[367]	train-mlogloss:1.12226	validation-mlogloss:1.15072
[368]	train-mlogloss:1.12199	validation-mlogloss:1.15052
[369]	train-mlogloss:1.12169	validation-mlogloss:1.15038
[370]	train-mlogloss:1.12138	validation-mlogloss:1.15017
[371]	train-mlogloss:1.12105	validation-mlogloss:1.14990
[372]	train-mlogloss:1.12079	validation-mlogloss:1.14965
[373]	train-mlogloss:1.12045	validation-mlogloss:1.14942
[374]	train-mlogloss:1.12011	validation-mlogloss:1.14925
[375]	train-mlogloss:1.11991	validation-mlogloss:1.14906
[376]	train-mlogloss:1.11970	validation-mlogloss:1.14896
[377]	train-mlogloss:1.11934	validation-mlogloss:1.14868
[378]	train-mlogloss:1.11909	validation-mlogloss:1.14848
[379]	train-mlogloss:1.11880	validation-mlogloss:1.14821
[380]	train-mlogloss:1.11850	validation-mlogloss:1.14794
[381]	train-mlogloss:1.11806	validation-mlogloss:1.14766
[382]	train-mlogloss:1.11769	validation-mlogloss:1.14737
[383]	train-mlogloss:1.11743	validation-mlogloss:1.14720
[384]	train-mlogloss:1.11715	validation-mlogloss:1.14695
[385]	train-mlogloss:1.11682	validation-mlogloss:1.14671
[386]	train-mlogloss:1.11656	validation-mlogloss:1.14648
[387]	train-mlogloss:1.11627	validation-mlogloss:1.14622
[388]	train-mlogloss:1.11613	validation-mlogloss:1.14612
[389]	train-mlogloss:1.11589	validation-mlogloss:1.14597
[390]	train-mlogloss:1.11546	validation-mlogloss:1.14563
[391]	train-mlogloss:1.11527	validation-mlogloss:1.14554
[392]	train-mlogloss:1.11495	validation-mlogloss:1.14535
[393]	train-mlogloss:1.11466	validation-mlogloss:1.14505
[394]	train-mlogloss:1.11452	validation-mlogloss:1.14496
[395]	train-mlogloss:1.11428	validation-mlogloss:1.14468
[396]	train-mlogloss:1.11402	validation-mlogloss:1.14445
[397]	train-mlogloss:1.11378	validation-mlogloss:1.14433
[398]	train-mlogloss:1.11364	validation-mlogloss:1.14426
[399]	train-mlogloss:1.11341	validation-mlogloss:1.14416
[400]	train-mlogloss:1.11309	validation-mlogloss:1.14394
[401]	train-mlogloss:1.11290	validation-mlogloss:1.14377
[402]	train-mlogloss:1.11263	validation-mlogloss:1.14356
[403]	train-mlogloss:1.11236	validation-mlogloss:1.14332
[404]	train-mlogloss:1.11219	validation-mlogloss:1.14326
[405]	train-mlogloss:1.11195	validation-mlogloss:1.14299
[406]	train-mlogloss:1.11171	validation-mlogloss:1.14284
[407]	train-mlogloss:1.11150	validation-mlogloss:1.14265
[408]	train-mlogloss:1.11137	validation-mlogloss:1.14260
[409]	train-mlogloss:1.11122	validation-mlogloss:1.14251
[410]	train-mlogloss:1.11111	validation-mlogloss:1.14241
[411]	train-mlogloss:1.11089	validation-mlogloss:1.14226
[412]	train-mlogloss:1.11068	validation-mlogloss:1.14211
[413]	train-mlogloss:1.11053	validation-mlogloss:1.14198
[414]	train-mlogloss:1.11042	validation-mlogloss:1.14187
[415]	train-mlogloss:1.11014	validation-mlogloss:1.14166
[416]	train-mlogloss:1.10987	validation-mlogloss:1.14140
[417]	train-mlogloss:1.10974	validation-mlogloss:1.14131
[418]	train-mlogloss:1.10958	validation-mlogloss:1.14128
[419]	train-mlogloss:1.10943	validation-mlogloss:1.14121
[420]	train-mlogloss:1.10915	validation-mlogloss:1.14092
[421]	train-mlogloss:1.10904	validation-mlogloss:1.14083
[422]	train-mlogloss:1.10885	validation-mlogloss:1.14066
[423]	train-mlogloss:1.10865	validation-mlogloss:1.14047
[424]	train-mlogloss:1.10848	validation-mlogloss:1.14031
[425]	train-mlogloss:1.10822	validation-mlogloss:1.14011
[426]	train-mlogloss:1.10807	validation-mlogloss:1.14000
[427]	train-mlogloss:1.10786	validation-mlogloss:1.13984
[428]	train-mlogloss:1.10777	validation-mlogloss:1.13978
[429]	train-mlogloss:1.10769	validation-mlogloss:1.13977
[430]	train-mlogloss:1.10754	validation-mlogloss:1.13965
[431]	train-mlogloss:1.10731	validation-mlogloss:1.13940
[432]	train-mlogloss:1.10707	validation-mlogloss:1.13927
[433]	train-mlogloss:1.10697	validation-mlogloss:1.13921
[434]	train-mlogloss:1.10680	validation-mlogloss:1.13897
[435]	train-mlogloss:1.10669	validation-mlogloss:1.13891
[436]	train-mlogloss:1.10643	validation-mlogloss:1.13863
[437]	train-mlogloss:1.10619	validation-mlogloss:1.13852
[438]	train-mlogloss:1.10601	validation-mlogloss:1.13840
[439]	train-mlogloss:1.10592	validation-mlogloss:1.13832
[440]	train-mlogloss:1.10577	validation-mlogloss:1.13820
[441]	train-mlogloss:1.10564	validation-mlogloss:1.13808
[442]	train-mlogloss:1.10550	validation-mlogloss:1.13796
[443]	train-mlogloss:1.10542	validation-mlogloss:1.13789
[444]	train-mlogloss:1.10529	validation-mlogloss:1.13777
[445]	train-mlogloss:1.10502	validation-mlogloss:1.13745
[446]	train-mlogloss:1.10480	validation-mlogloss:1.13742
[447]	train-mlogloss:1.10461	validation-mlogloss:1.13733
[448]	train-mlogloss:1.10439	validation-mlogloss:1.13715
[449]	train-mlogloss:1.10423	validation-mlogloss:1.13697
[450]	train-mlogloss:1.10402	validation-mlogloss:1.13684
[451]	train-mlogloss:1.10385	validation-mlogloss:1.13676
[452]	train-mlogloss:1.10380	validation-mlogloss:1.13670
[453]	train-mlogloss:1.10363	validation-mlogloss:1.13654
[454]	train-mlogloss:1.10348	validation-mlogloss:1.13642
[455]	train-mlogloss:1.10339	validation-mlogloss:1.13639
[456]	train-mlogloss:1.10313	validation-mlogloss:1.13628
[457]	train-mlogloss:1.10307	validation-mlogloss:1.13623
[458]	train-mlogloss:1.10300	validation-mlogloss:1.13615
[459]	train-mlogloss:1.10290	validation-mlogloss:1.13611
[460]	train-mlogloss:1.10285	validation-mlogloss:1.13604
[461]	train-mlogloss:1.10267	validation-mlogloss:1.13586
[462]	train-mlogloss:1.10257	validation-mlogloss:1.13582
[463]	train-mlogloss:1.10233	validation-mlogloss:1.13567
[464]	train-mlogloss:1.10222	validation-mlogloss:1.13556
[465]	train-mlogloss:1.10204	validation-mlogloss:1.13542
[466]	train-mlogloss:1.10196	validation-mlogloss:1.13534
[467]	train-mlogloss:1.10179	validation-mlogloss:1.13521
[468]	train-mlogloss:1.10174	validation-mlogloss:1.13517
[469]	train-mlogloss:1.10162	validation-mlogloss:1.13506
[470]	train-mlogloss:1.10155	validation-mlogloss:1.13497
[471]	train-mlogloss:1.10139	validation-mlogloss:1.13479
[472]	train-mlogloss:1.10126	validation-mlogloss:1.13468
[473]	train-mlogloss:1.10111	validation-mlogloss:1.13458
[474]	train-mlogloss:1.10096	validation-mlogloss:1.13449
[475]	train-mlogloss:1.10087	validation-mlogloss:1.13437
[476]	train-mlogloss:1.10077	validation-mlogloss:1.13429
[477]	train-mlogloss:1.10065	validation-mlogloss:1.13424
[478]	train-mlogloss:1.10051	validation-mlogloss:1.13415
[479]	train-mlogloss:1.10042	validation-mlogloss:1.13412
[480]	train-mlogloss:1.10030	validation-mlogloss:1.13404
[481]	train-mlogloss:1.10019	validation-mlogloss:1.13397
[482]	train-mlogloss:1.10014	validation-mlogloss:1.13393
[483]	train-mlogloss:1.10007	validation-mlogloss:1.13389
[484]	train-mlogloss:1.10003	validation-mlogloss:1.13391
[485]	train-mlogloss:1.09992	validation-mlogloss:1.13381
[486]	train-mlogloss:1.09979	validation-mlogloss:1.13375
[487]	train-mlogloss:1.09962	validation-mlogloss:1.13367
[488]	train-mlogloss:1.09952	validation-mlogloss:1.13358
[489]	train-mlogloss:1.09950	validation-mlogloss:1.13354
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.54 [%]
Global accuracy score (test) = 62.48 [%]
Global F1 score (train) = 66.35 [%]
Global F1 score (test) = 61.57 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.49      0.47      0.48       161
 CAMINAR CON MÓVIL O LIBRO       0.40      0.58      0.47       161
       CAMINAR USUAL SPEED       0.45      0.24      0.31       161
            CAMINAR ZIGZAG       0.73      0.58      0.65       161
          DE PIE BARRIENDO       0.73      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.43      0.58      0.50       161
    DE PIE MOVIENDO LIBROS       0.54      0.45      0.49       161
          DE PIE USANDO PC       0.82      0.89      0.85       161
        FASE REPOSO CON K5       0.75      0.92      0.82       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.58      0.39      0.47       161
         SENTADO USANDO PC       0.45      0.59      0.51       161
      SENTADO VIENDO LA TV       0.48      0.28      0.35       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.62      2415
                 macro avg       0.63      0.62      0.62      2415
              weighted avg       0.63      0.62      0.62      2415


Accuracy capturado en la ejecución 13: 62.48 [%]
F1-score capturado en la ejecución 13: 61.57 [%]

=== EJECUCIÓN 14 ===

--- TRAIN (ejecución 14) ---

--- TEST (ejecución 14) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.49 [%]
Global accuracy score (test) = 63.11 [%]
Global F1 score (train) = 66.3 [%]
Global F1 score (test) = 62.37 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.50      0.46      0.48       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.55      0.46       161
       CAMINAR USUAL SPEED       0.47      0.29      0.36       161
            CAMINAR ZIGZAG       0.74      0.58      0.65       161
          DE PIE BARRIENDO       0.73      0.63      0.67       161
   DE PIE DOBLANDO TOALLAS       0.44      0.55      0.49       161
    DE PIE MOVIENDO LIBROS       0.53      0.48      0.50       161
          DE PIE USANDO PC       0.82      0.93      0.87       161
        FASE REPOSO CON K5       0.78      0.93      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.56      0.37      0.44       161
         SENTADO USANDO PC       0.47      0.58      0.52       161
      SENTADO VIENDO LA TV       0.51      0.35      0.42       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.62      2415
              weighted avg       0.64      0.63      0.62      2415


Accuracy capturado en la ejecución 14: 63.11 [%]
F1-score capturado en la ejecución 14: 62.37 [%]

=== EJECUCIÓN 15 ===

--- TRAIN (ejecución 15) ---

--- TEST (ejecución 15) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.77 [%]
Global accuracy score (test) = 62.73 [%]
Global F1 score (train) = 66.6 [%]
Global F1 score (test) = 61.97 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.47      0.42      0.44       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.55      0.45       161
       CAMINAR USUAL SPEED       0.46      0.28      0.35       161
            CAMINAR ZIGZAG       0.73      0.58      0.65       161
          DE PIE BARRIENDO       0.75      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.43      0.60      0.50       161
    DE PIE MOVIENDO LIBROS       0.56      0.48      0.52       161
          DE PIE USANDO PC       0.83      0.89      0.86       161
        FASE REPOSO CON K5       0.76      0.92      0.83       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.57      0.35      0.44       161
         SENTADO USANDO PC       0.47      0.61      0.53       161
      SENTADO VIENDO LA TV       0.47      0.32      0.38       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 15: 62.73 [%]
F1-score capturado en la ejecución 15: 61.97 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-10-26 07:25:51.359945: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:25:51.372567: 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 00:00:1761459951.386109 2615321 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761459951.390510 2615321 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761459951.401353 2615321 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459951.401376 2615321 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459951.401379 2615321 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459951.401381 2615321 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:25:51.404710: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67397	validation-mlogloss:2.67535
[1]	train-mlogloss:2.64169	validation-mlogloss:2.64415
[2]	train-mlogloss:2.61330	validation-mlogloss:2.61696
[3]	train-mlogloss:2.58488	validation-mlogloss:2.59086
[4]	train-mlogloss:2.55736	validation-mlogloss:2.56342
[5]	train-mlogloss:2.53081	validation-mlogloss:2.53671
[6]	train-mlogloss:2.50434	validation-mlogloss:2.51104
[7]	train-mlogloss:2.47850	validation-mlogloss:2.48597
[8]	train-mlogloss:2.45457	validation-mlogloss:2.46325
[9]	train-mlogloss:2.43131	validation-mlogloss:2.44006
[10]	train-mlogloss:2.40752	validation-mlogloss:2.41669
[11]	train-mlogloss:2.38511	validation-mlogloss:2.39431
[12]	train-mlogloss:2.36334	validation-mlogloss:2.37368
[13]	train-mlogloss:2.34244	validation-mlogloss:2.35334
[14]	train-mlogloss:2.32240	validation-mlogloss:2.33406
[15]	train-mlogloss:2.30265	validation-mlogloss:2.31470
[16]	train-mlogloss:2.28376	validation-mlogloss:2.29634
[17]	train-mlogloss:2.26426	validation-mlogloss:2.27696
[18]	train-mlogloss:2.24618	validation-mlogloss:2.25908
[19]	train-mlogloss:2.22840	validation-mlogloss:2.24168
[20]	train-mlogloss:2.21120	validation-mlogloss:2.22454
[21]	train-mlogloss:2.19372	validation-mlogloss:2.20775
[22]	train-mlogloss:2.17745	validation-mlogloss:2.19221
[23]	train-mlogloss:2.16066	validation-mlogloss:2.17562
[24]	train-mlogloss:2.14508	validation-mlogloss:2.16019
[25]	train-mlogloss:2.13021	validation-mlogloss:2.14574
[26]	train-mlogloss:2.11545	validation-mlogloss:2.13125
[27]	train-mlogloss:2.10094	validation-mlogloss:2.11680
[28]	train-mlogloss:2.08703	validation-mlogloss:2.10284
[29]	train-mlogloss:2.07263	validation-mlogloss:2.08901
[30]	train-mlogloss:2.05869	validation-mlogloss:2.07529
[31]	train-mlogloss:2.04574	validation-mlogloss:2.06245
[32]	train-mlogloss:2.03232	validation-mlogloss:2.04894
[33]	train-mlogloss:2.01985	validation-mlogloss:2.03662
[34]	train-mlogloss:2.00725	validation-mlogloss:2.02355
[35]	train-mlogloss:1.99463	validation-mlogloss:2.01071
[36]	train-mlogloss:1.98286	validation-mlogloss:1.99928
[37]	train-mlogloss:1.97131	validation-mlogloss:1.98756
[38]	train-mlogloss:1.95969	validation-mlogloss:1.97596
[39]	train-mlogloss:1.94821	validation-mlogloss:1.96434
[40]	train-mlogloss:1.93717	validation-mlogloss:1.95337
[41]	train-mlogloss:1.92663	validation-mlogloss:1.94326
[42]	train-mlogloss:1.91597	validation-mlogloss:1.93262
[43]	train-mlogloss:1.90574	validation-mlogloss:1.92248
[44]	train-mlogloss:1.89501	validation-mlogloss:1.91159
[45]	train-mlogloss:1.88465	validation-mlogloss:1.90133
[46]	train-mlogloss:1.87463	validation-mlogloss:1.89110
[47]	train-mlogloss:1.86511	validation-mlogloss:1.88194
[48]	train-mlogloss:1.85575	validation-mlogloss:1.87283
[49]	train-mlogloss:1.84627	validation-mlogloss:1.86297
[50]	train-mlogloss:1.83713	validation-mlogloss:1.85384
[51]	train-mlogloss:1.82773	validation-mlogloss:1.84399
[52]	train-mlogloss:1.81906	validation-mlogloss:1.83518
[53]	train-mlogloss:1.81055	validation-mlogloss:1.82686
[54]	train-mlogloss:1.80203	validation-mlogloss:1.81819
[55]	train-mlogloss:1.79385	validation-mlogloss:1.81014
[56]	train-mlogloss:1.78548	validation-mlogloss:1.80196
[57]	train-mlogloss:1.77713	validation-mlogloss:1.79353
[58]	train-mlogloss:1.76875	validation-mlogloss:1.78475
[59]	train-mlogloss:1.76096	validation-mlogloss:1.77705
[60]	train-mlogloss:1.75338	validation-mlogloss:1.76907
[61]	train-mlogloss:1.74575	validation-mlogloss:1.76153
[62]	train-mlogloss:1.73780	validation-mlogloss:1.75325
[63]	train-mlogloss:1.72991	validation-mlogloss:1.74509
[64]	train-mlogloss:1.72261	validation-mlogloss:1.73791
[65]	train-mlogloss:1.71535	validation-mlogloss:1.73064
[66]	train-mlogloss:1.70796	validation-mlogloss:1.72305
[67]	train-mlogloss:1.70088	validation-mlogloss:1.71590
[68]	train-mlogloss:1.69390	validation-mlogloss:1.70888
[69]	train-mlogloss:1.68738	validation-mlogloss:1.70225
[70]	train-mlogloss:1.68069	validation-mlogloss:1.69524
[71]	train-mlogloss:1.67398	validation-mlogloss:1.68839
[72]	train-mlogloss:1.66736	validation-mlogloss:1.68186
[73]	train-mlogloss:1.66123	validation-mlogloss:1.67550
[74]	train-mlogloss:1.65520	validation-mlogloss:1.66927
[75]	train-mlogloss:1.64961	validation-mlogloss:1.66360
[76]	train-mlogloss:1.64333	validation-mlogloss:1.65727
[77]	train-mlogloss:1.63728	validation-mlogloss:1.65135
[78]	train-mlogloss:1.63152	validation-mlogloss:1.64581
[79]	train-mlogloss:1.62586	validation-mlogloss:1.64015
[80]	train-mlogloss:1.62003	validation-mlogloss:1.63429
[81]	train-mlogloss:1.61449	validation-mlogloss:1.62861
[82]	train-mlogloss:1.60863	validation-mlogloss:1.62260
[83]	train-mlogloss:1.60296	validation-mlogloss:1.61688
[84]	train-mlogloss:1.59765	validation-mlogloss:1.61167
[85]	train-mlogloss:1.59251	validation-mlogloss:1.60622
[86]	train-mlogloss:1.58731	validation-mlogloss:1.60102
[87]	train-mlogloss:1.58227	validation-mlogloss:1.59604
[88]	train-mlogloss:1.57689	validation-mlogloss:1.59060
[89]	train-mlogloss:1.57167	validation-mlogloss:1.58545
[90]	train-mlogloss:1.56674	validation-mlogloss:1.58069
[91]	train-mlogloss:1.56170	validation-mlogloss:1.57573
[92]	train-mlogloss:1.55692	validation-mlogloss:1.57072
[93]	train-mlogloss:1.55194	validation-mlogloss:1.56567
[94]	train-mlogloss:1.54718	validation-mlogloss:1.56085
[95]	train-mlogloss:1.54250	validation-mlogloss:1.55609
[96]	train-mlogloss:1.53786	validation-mlogloss:1.55146
[97]	train-mlogloss:1.53328	validation-mlogloss:1.54674
[98]	train-mlogloss:1.52874	validation-mlogloss:1.54219
[99]	train-mlogloss:1.52437	validation-mlogloss:1.53796
[100]	train-mlogloss:1.51990	validation-mlogloss:1.53343
[101]	train-mlogloss:1.51578	validation-mlogloss:1.52952
[102]	train-mlogloss:1.51138	validation-mlogloss:1.52505
[103]	train-mlogloss:1.50704	validation-mlogloss:1.52060
[104]	train-mlogloss:1.50271	validation-mlogloss:1.51617
[105]	train-mlogloss:1.49857	validation-mlogloss:1.51199
[106]	train-mlogloss:1.49464	validation-mlogloss:1.50808
[107]	train-mlogloss:1.49073	validation-mlogloss:1.50414
[108]	train-mlogloss:1.48675	validation-mlogloss:1.50013
[109]	train-mlogloss:1.48279	validation-mlogloss:1.49612
[110]	train-mlogloss:1.47898	validation-mlogloss:1.49237
[111]	train-mlogloss:1.47524	validation-mlogloss:1.48874
[112]	train-mlogloss:1.47136	validation-mlogloss:1.48470
[113]	train-mlogloss:1.46754	validation-mlogloss:1.48068
[114]	train-mlogloss:1.46379	validation-mlogloss:1.47679
[115]	train-mlogloss:1.46010	validation-mlogloss:1.47305
[116]	train-mlogloss:1.45648	validation-mlogloss:1.46935
[117]	train-mlogloss:1.45276	validation-mlogloss:1.46560
[118]	train-mlogloss:1.44919	validation-mlogloss:1.46197
[119]	train-mlogloss:1.44547	validation-mlogloss:1.45816
[120]	train-mlogloss:1.44192	validation-mlogloss:1.45469
[121]	train-mlogloss:1.43851	validation-mlogloss:1.45113
[122]	train-mlogloss:1.43532	validation-mlogloss:1.44791
[123]	train-mlogloss:1.43189	validation-mlogloss:1.44446
[124]	train-mlogloss:1.42859	validation-mlogloss:1.44107
[125]	train-mlogloss:1.42530	validation-mlogloss:1.43782
[126]	train-mlogloss:1.42199	validation-mlogloss:1.43447
[127]	train-mlogloss:1.41874	validation-mlogloss:1.43126
[128]	train-mlogloss:1.41553	validation-mlogloss:1.42802
[129]	train-mlogloss:1.41235	validation-mlogloss:1.42494
[130]	train-mlogloss:1.40919	validation-mlogloss:1.42177
[131]	train-mlogloss:1.40624	validation-mlogloss:1.41883
[132]	train-mlogloss:1.40305	validation-mlogloss:1.41547
[133]	train-mlogloss:1.40001	validation-mlogloss:1.41250
[134]	train-mlogloss:1.39701	validation-mlogloss:1.40944
[135]	train-mlogloss:1.39428	validation-mlogloss:1.40657
[136]	train-mlogloss:1.39131	validation-mlogloss:1.40359
[137]	train-mlogloss:1.38849	validation-mlogloss:1.40053
[138]	train-mlogloss:1.38560	validation-mlogloss:1.39766
[139]	train-mlogloss:1.38300	validation-mlogloss:1.39515
[140]	train-mlogloss:1.38039	validation-mlogloss:1.39251
[141]	train-mlogloss:1.37761	validation-mlogloss:1.38959
[142]	train-mlogloss:1.37503	validation-mlogloss:1.38699
[143]	train-mlogloss:1.37245	validation-mlogloss:1.38442
[144]	train-mlogloss:1.36967	validation-mlogloss:1.38168
[145]	train-mlogloss:1.36697	validation-mlogloss:1.37913
[146]	train-mlogloss:1.36445	validation-mlogloss:1.37653
[147]	train-mlogloss:1.36189	validation-mlogloss:1.37377
[148]	train-mlogloss:1.35949	validation-mlogloss:1.37142
[149]	train-mlogloss:1.35709	validation-mlogloss:1.36885
[150]	train-mlogloss:1.35463	validation-mlogloss:1.36657
[151]	train-mlogloss:1.35209	validation-mlogloss:1.36396
[152]	train-mlogloss:1.34980	validation-mlogloss:1.36163
[153]	train-mlogloss:1.34746	validation-mlogloss:1.35929
[154]	train-mlogloss:1.34515	validation-mlogloss:1.35695
[155]	train-mlogloss:1.34281	validation-mlogloss:1.35472
[156]	train-mlogloss:1.34049	validation-mlogloss:1.35242
[157]	train-mlogloss:1.33808	validation-mlogloss:1.34999
[158]	train-mlogloss:1.33583	validation-mlogloss:1.34764
[159]	train-mlogloss:1.33373	validation-mlogloss:1.34577
[160]	train-mlogloss:1.33134	validation-mlogloss:1.34336
[161]	train-mlogloss:1.32903	validation-mlogloss:1.34110
[162]	train-mlogloss:1.32687	validation-mlogloss:1.33891
[163]	train-mlogloss:1.32465	validation-mlogloss:1.33668
[164]	train-mlogloss:1.32260	validation-mlogloss:1.33464
[165]	train-mlogloss:1.32048	validation-mlogloss:1.33258
[166]	train-mlogloss:1.31826	validation-mlogloss:1.33035
[167]	train-mlogloss:1.31618	validation-mlogloss:1.32836
[168]	train-mlogloss:1.31414	validation-mlogloss:1.32643
[169]	train-mlogloss:1.31213	validation-mlogloss:1.32452
[170]	train-mlogloss:1.31004	validation-mlogloss:1.32244
[171]	train-mlogloss:1.30802	validation-mlogloss:1.32056
[172]	train-mlogloss:1.30594	validation-mlogloss:1.31854
[173]	train-mlogloss:1.30414	validation-mlogloss:1.31678
[174]	train-mlogloss:1.30220	validation-mlogloss:1.31488
[175]	train-mlogloss:1.30024	validation-mlogloss:1.31307
[176]	train-mlogloss:1.29833	validation-mlogloss:1.31110
[177]	train-mlogloss:1.29649	validation-mlogloss:1.30943
[178]	train-mlogloss:1.29463	validation-mlogloss:1.30769
[179]	train-mlogloss:1.29281	validation-mlogloss:1.30588
[180]	train-mlogloss:1.29096	validation-mlogloss:1.30398
[181]	train-mlogloss:1.28911	validation-mlogloss:1.30213
[182]	train-mlogloss:1.28725	validation-mlogloss:1.30030
[183]	train-mlogloss:1.28533	validation-mlogloss:1.29854
[184]	train-mlogloss:1.28357	validation-mlogloss:1.29670
[185]	train-mlogloss:1.28185	validation-mlogloss:1.29504
[186]	train-mlogloss:1.28001	validation-mlogloss:1.29324
[187]	train-mlogloss:1.27824	validation-mlogloss:1.29154
[188]	train-mlogloss:1.27664	validation-mlogloss:1.29001
[189]	train-mlogloss:1.27499	validation-mlogloss:1.28849
[190]	train-mlogloss:1.27328	validation-mlogloss:1.28687
[191]	train-mlogloss:1.27157	validation-mlogloss:1.28527
[192]	train-mlogloss:1.27001	validation-mlogloss:1.28380
[193]	train-mlogloss:1.26846	validation-mlogloss:1.28226
[194]	train-mlogloss:1.26694	validation-mlogloss:1.28077
[195]	train-mlogloss:1.26529	validation-mlogloss:1.27899
[196]	train-mlogloss:1.26376	validation-mlogloss:1.27752
[197]	train-mlogloss:1.26213	validation-mlogloss:1.27605
[198]	train-mlogloss:1.26059	validation-mlogloss:1.27458
[199]	train-mlogloss:1.25888	validation-mlogloss:1.27286
[200]	train-mlogloss:1.25737	validation-mlogloss:1.27141
[201]	train-mlogloss:1.25583	validation-mlogloss:1.27014
[202]	train-mlogloss:1.25434	validation-mlogloss:1.26868
[203]	train-mlogloss:1.25289	validation-mlogloss:1.26720
[204]	train-mlogloss:1.25149	validation-mlogloss:1.26586
[205]	train-mlogloss:1.25003	validation-mlogloss:1.26458
[206]	train-mlogloss:1.24858	validation-mlogloss:1.26320
[207]	train-mlogloss:1.24712	validation-mlogloss:1.26182
[208]	train-mlogloss:1.24568	validation-mlogloss:1.26049
[209]	train-mlogloss:1.24412	validation-mlogloss:1.25923
[210]	train-mlogloss:1.24275	validation-mlogloss:1.25795
[211]	train-mlogloss:1.24132	validation-mlogloss:1.25660
[212]	train-mlogloss:1.23981	validation-mlogloss:1.25519
[213]	train-mlogloss:1.23846	validation-mlogloss:1.25386
[214]	train-mlogloss:1.23709	validation-mlogloss:1.25256
[215]	train-mlogloss:1.23572	validation-mlogloss:1.25133
[216]	train-mlogloss:1.23443	validation-mlogloss:1.25023
[217]	train-mlogloss:1.23317	validation-mlogloss:1.24904
[218]	train-mlogloss:1.23195	validation-mlogloss:1.24791
[219]	train-mlogloss:1.23064	validation-mlogloss:1.24676
[220]	train-mlogloss:1.22920	validation-mlogloss:1.24552
[221]	train-mlogloss:1.22794	validation-mlogloss:1.24427
[222]	train-mlogloss:1.22659	validation-mlogloss:1.24298
[223]	train-mlogloss:1.22529	validation-mlogloss:1.24181
[224]	train-mlogloss:1.22410	validation-mlogloss:1.24084
[225]	train-mlogloss:1.22278	validation-mlogloss:1.23961
[226]	train-mlogloss:1.22159	validation-mlogloss:1.23856
[227]	train-mlogloss:1.22039	validation-mlogloss:1.23750
[228]	train-mlogloss:1.21922	validation-mlogloss:1.23648
[229]	train-mlogloss:1.21800	validation-mlogloss:1.23539
[230]	train-mlogloss:1.21682	validation-mlogloss:1.23438
[231]	train-mlogloss:1.21564	validation-mlogloss:1.23337
[232]	train-mlogloss:1.21441	validation-mlogloss:1.23224
[233]	train-mlogloss:1.21321	validation-mlogloss:1.23119
[234]	train-mlogloss:1.21199	validation-mlogloss:1.23014
[235]	train-mlogloss:1.21079	validation-mlogloss:1.22905
[236]	train-mlogloss:1.20961	validation-mlogloss:1.22799
[237]	train-mlogloss:1.20845	validation-mlogloss:1.22685
[238]	train-mlogloss:1.20737	validation-mlogloss:1.22586
[239]	train-mlogloss:1.20628	validation-mlogloss:1.22494
[240]	train-mlogloss:1.20512	validation-mlogloss:1.22388
[241]	train-mlogloss:1.20399	validation-mlogloss:1.22297
[242]	train-mlogloss:1.20296	validation-mlogloss:1.22207
[243]	train-mlogloss:1.20189	validation-mlogloss:1.22110
[244]	train-mlogloss:1.20088	validation-mlogloss:1.22025
[245]	train-mlogloss:1.19981	validation-mlogloss:1.21930
[246]	train-mlogloss:1.19869	validation-mlogloss:1.21828
[247]	train-mlogloss:1.19775	validation-mlogloss:1.21744
[248]	train-mlogloss:1.19661	validation-mlogloss:1.21638
[249]	train-mlogloss:1.19559	validation-mlogloss:1.21548
[250]	train-mlogloss:1.19456	validation-mlogloss:1.21460
[251]	train-mlogloss:1.19364	validation-mlogloss:1.21388
[252]	train-mlogloss:1.19252	validation-mlogloss:1.21294
[253]	train-mlogloss:1.19141	validation-mlogloss:1.21197
[254]	train-mlogloss:1.19050	validation-mlogloss:1.21128
[255]	train-mlogloss:1.18943	validation-mlogloss:1.21025
[256]	train-mlogloss:1.18843	validation-mlogloss:1.20938
[257]	train-mlogloss:1.18752	validation-mlogloss:1.20863
[258]	train-mlogloss:1.18645	validation-mlogloss:1.20770
[259]	train-mlogloss:1.18545	validation-mlogloss:1.20675
[260]	train-mlogloss:1.18452	validation-mlogloss:1.20589
[261]	train-mlogloss:1.18352	validation-mlogloss:1.20505
[262]	train-mlogloss:1.18280	validation-mlogloss:1.20439
[263]	train-mlogloss:1.18186	validation-mlogloss:1.20348
[264]	train-mlogloss:1.18098	validation-mlogloss:1.20267
[265]	train-mlogloss:1.18006	validation-mlogloss:1.20183
[266]	train-mlogloss:1.17908	validation-mlogloss:1.20091
[267]	train-mlogloss:1.17814	validation-mlogloss:1.20009
[268]	train-mlogloss:1.17718	validation-mlogloss:1.19926
[269]	train-mlogloss:1.17636	validation-mlogloss:1.19847
[270]	train-mlogloss:1.17551	validation-mlogloss:1.19770
[271]	train-mlogloss:1.17456	validation-mlogloss:1.19693
[272]	train-mlogloss:1.17370	validation-mlogloss:1.19620
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[337]	train-mlogloss:1.13420	validation-mlogloss:1.16370
[338]	train-mlogloss:1.13384	validation-mlogloss:1.16351
[339]	train-mlogloss:1.13332	validation-mlogloss:1.16306
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[341]	train-mlogloss:1.13255	validation-mlogloss:1.16250
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[352]	train-mlogloss:1.12809	validation-mlogloss:1.15916
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[357]	train-mlogloss:1.12648	validation-mlogloss:1.15784
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[359]	train-mlogloss:1.12567	validation-mlogloss:1.15723
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[439]	train-mlogloss:1.10576	validation-mlogloss:1.14173
[440]	train-mlogloss:1.10561	validation-mlogloss:1.14166
[441]	train-mlogloss:1.10542	validation-mlogloss:1.14157
[442]	train-mlogloss:1.10513	validation-mlogloss:1.14132
[443]	train-mlogloss:1.10496	validation-mlogloss:1.14118
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[453]	train-mlogloss:1.10312	validation-mlogloss:1.13995
[454]	train-mlogloss:1.10297	validation-mlogloss:1.13992
[455]	train-mlogloss:1.10267	validation-mlogloss:1.13986
[456]	train-mlogloss:1.10237	validation-mlogloss:1.13967
[457]	train-mlogloss:1.10217	validation-mlogloss:1.13951
[458]	train-mlogloss:1.10203	validation-mlogloss:1.13945
[459]	train-mlogloss:1.10180	validation-mlogloss:1.13933
[460]	train-mlogloss:1.10169	validation-mlogloss:1.13926
[461]	train-mlogloss:1.10158	validation-mlogloss:1.13920
[462]	train-mlogloss:1.10139	validation-mlogloss:1.13905
[463]	train-mlogloss:1.10130	validation-mlogloss:1.13897
[464]	train-mlogloss:1.10109	validation-mlogloss:1.13882
[465]	train-mlogloss:1.10088	validation-mlogloss:1.13876
[466]	train-mlogloss:1.10079	validation-mlogloss:1.13873
[467]	train-mlogloss:1.10071	validation-mlogloss:1.13865
[468]	train-mlogloss:1.10058	validation-mlogloss:1.13855
[469]	train-mlogloss:1.10043	validation-mlogloss:1.13843
[470]	train-mlogloss:1.10030	validation-mlogloss:1.13839
[471]	train-mlogloss:1.10030	validation-mlogloss:1.13839
[472]	train-mlogloss:1.10025	validation-mlogloss:1.13834
[473]	train-mlogloss:1.10012	validation-mlogloss:1.13826
[474]	train-mlogloss:1.10009	validation-mlogloss:1.13825
[475]	train-mlogloss:1.09993	validation-mlogloss:1.13817
[476]	train-mlogloss:1.09984	validation-mlogloss:1.13813
[477]	train-mlogloss:1.09975	validation-mlogloss:1.13808
[478]	train-mlogloss:1.09962	validation-mlogloss:1.13800
[479]	train-mlogloss:1.09945	validation-mlogloss:1.13796
[480]	train-mlogloss:1.09926	validation-mlogloss:1.13783
[481]	train-mlogloss:1.09914	validation-mlogloss:1.13769
[482]	train-mlogloss:1.09913	validation-mlogloss:1.13768
[483]	train-mlogloss:1.09905	validation-mlogloss:1.13766
[484]	train-mlogloss:1.09889	validation-mlogloss:1.13749
[485]	train-mlogloss:1.09882	validation-mlogloss:1.13747
[486]	train-mlogloss:1.09868	validation-mlogloss:1.13736
[487]	train-mlogloss:1.09844	validation-mlogloss:1.13719
[488]	train-mlogloss:1.09833	validation-mlogloss:1.13706
[489]	train-mlogloss:1.09822	validation-mlogloss:1.13694
Saved model to disk.
2025-10-26 07:26:30.860732: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:26:30.873178: 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 00:00:1761459990.886705 2616115 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761459990.891070 2616115 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761459990.901771 2616115 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459990.901794 2616115 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459990.901798 2616115 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761459990.901812 2616115 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:26:30.905181: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67510	validation-mlogloss:2.67592
[1]	train-mlogloss:2.64514	validation-mlogloss:2.64769
[2]	train-mlogloss:2.61433	validation-mlogloss:2.61790
[3]	train-mlogloss:2.58439	validation-mlogloss:2.58879
[4]	train-mlogloss:2.55653	validation-mlogloss:2.56184
[5]	train-mlogloss:2.53000	validation-mlogloss:2.53607
[6]	train-mlogloss:2.50360	validation-mlogloss:2.51116
[7]	train-mlogloss:2.47769	validation-mlogloss:2.48559
[8]	train-mlogloss:2.45342	validation-mlogloss:2.46230
[9]	train-mlogloss:2.43009	validation-mlogloss:2.43960
[10]	train-mlogloss:2.40723	validation-mlogloss:2.41711
[11]	train-mlogloss:2.38520	validation-mlogloss:2.39518
[12]	train-mlogloss:2.36257	validation-mlogloss:2.37200
[13]	train-mlogloss:2.34129	validation-mlogloss:2.35133
[14]	train-mlogloss:2.32058	validation-mlogloss:2.33107
[15]	train-mlogloss:2.30150	validation-mlogloss:2.31168
[16]	train-mlogloss:2.28171	validation-mlogloss:2.29116
[17]	train-mlogloss:2.26295	validation-mlogloss:2.27239
[18]	train-mlogloss:2.24439	validation-mlogloss:2.25382
[19]	train-mlogloss:2.22716	validation-mlogloss:2.23707
[20]	train-mlogloss:2.21025	validation-mlogloss:2.22071
[21]	train-mlogloss:2.19362	validation-mlogloss:2.20419
[22]	train-mlogloss:2.17778	validation-mlogloss:2.18919
[23]	train-mlogloss:2.16161	validation-mlogloss:2.17415
[24]	train-mlogloss:2.14673	validation-mlogloss:2.15943
[25]	train-mlogloss:2.13215	validation-mlogloss:2.14554
[26]	train-mlogloss:2.11727	validation-mlogloss:2.13072
[27]	train-mlogloss:2.10238	validation-mlogloss:2.11615
[28]	train-mlogloss:2.08816	validation-mlogloss:2.10235
[29]	train-mlogloss:2.07429	validation-mlogloss:2.08891
[30]	train-mlogloss:2.06095	validation-mlogloss:2.07555
[31]	train-mlogloss:2.04710	validation-mlogloss:2.06213
[32]	train-mlogloss:2.03418	validation-mlogloss:2.04942
[33]	train-mlogloss:2.02183	validation-mlogloss:2.03700
[34]	train-mlogloss:2.00910	validation-mlogloss:2.02391
[35]	train-mlogloss:1.99702	validation-mlogloss:2.01150
[36]	train-mlogloss:1.98486	validation-mlogloss:1.99845
[37]	train-mlogloss:1.97289	validation-mlogloss:1.98640
[38]	train-mlogloss:1.96157	validation-mlogloss:1.97566
[39]	train-mlogloss:1.94993	validation-mlogloss:1.96419
[40]	train-mlogloss:1.93873	validation-mlogloss:1.95305
[41]	train-mlogloss:1.92769	validation-mlogloss:1.94175
[42]	train-mlogloss:1.91695	validation-mlogloss:1.93101
[43]	train-mlogloss:1.90627	validation-mlogloss:1.92044
[44]	train-mlogloss:1.89539	validation-mlogloss:1.90956
[45]	train-mlogloss:1.88526	validation-mlogloss:1.89935
[46]	train-mlogloss:1.87549	validation-mlogloss:1.88974
[47]	train-mlogloss:1.86576	validation-mlogloss:1.88001
[48]	train-mlogloss:1.85596	validation-mlogloss:1.86986
[49]	train-mlogloss:1.84679	validation-mlogloss:1.86082
[50]	train-mlogloss:1.83738	validation-mlogloss:1.85131
[51]	train-mlogloss:1.82830	validation-mlogloss:1.84258
[52]	train-mlogloss:1.81934	validation-mlogloss:1.83386
[53]	train-mlogloss:1.81054	validation-mlogloss:1.82510
[54]	train-mlogloss:1.80169	validation-mlogloss:1.81609
[55]	train-mlogloss:1.79294	validation-mlogloss:1.80743
[56]	train-mlogloss:1.78468	validation-mlogloss:1.79888
[57]	train-mlogloss:1.77689	validation-mlogloss:1.79100
[58]	train-mlogloss:1.76914	validation-mlogloss:1.78306
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[440]	train-mlogloss:1.10503	validation-mlogloss:1.13913
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[455]	train-mlogloss:1.10252	validation-mlogloss:1.13717
[456]	train-mlogloss:1.10239	validation-mlogloss:1.13706
[457]	train-mlogloss:1.10226	validation-mlogloss:1.13698
[458]	train-mlogloss:1.10201	validation-mlogloss:1.13678
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[460]	train-mlogloss:1.10167	validation-mlogloss:1.13656
[461]	train-mlogloss:1.10151	validation-mlogloss:1.13647
[462]	train-mlogloss:1.10136	validation-mlogloss:1.13641
[463]	train-mlogloss:1.10119	validation-mlogloss:1.13627
[464]	train-mlogloss:1.10098	validation-mlogloss:1.13606
[465]	train-mlogloss:1.10086	validation-mlogloss:1.13602
[466]	train-mlogloss:1.10073	validation-mlogloss:1.13592
[467]	train-mlogloss:1.10065	validation-mlogloss:1.13589
[468]	train-mlogloss:1.10047	validation-mlogloss:1.13572
[469]	train-mlogloss:1.10041	validation-mlogloss:1.13570
[470]	train-mlogloss:1.10025	validation-mlogloss:1.13564
[471]	train-mlogloss:1.10008	validation-mlogloss:1.13550
[472]	train-mlogloss:1.09988	validation-mlogloss:1.13535
[473]	train-mlogloss:1.09968	validation-mlogloss:1.13523
[474]	train-mlogloss:1.09954	validation-mlogloss:1.13515
[475]	train-mlogloss:1.09950	validation-mlogloss:1.13510
[476]	train-mlogloss:1.09935	validation-mlogloss:1.13493
[477]	train-mlogloss:1.09925	validation-mlogloss:1.13486
[478]	train-mlogloss:1.09907	validation-mlogloss:1.13479
[479]	train-mlogloss:1.09891	validation-mlogloss:1.13469
[480]	train-mlogloss:1.09872	validation-mlogloss:1.13459
[481]	train-mlogloss:1.09861	validation-mlogloss:1.13447
[482]	train-mlogloss:1.09852	validation-mlogloss:1.13440
[483]	train-mlogloss:1.09842	validation-mlogloss:1.13434
[484]	train-mlogloss:1.09827	validation-mlogloss:1.13424
[485]	train-mlogloss:1.09804	validation-mlogloss:1.13408
[486]	train-mlogloss:1.09804	validation-mlogloss:1.13408
[487]	train-mlogloss:1.09794	validation-mlogloss:1.13398
[488]	train-mlogloss:1.09785	validation-mlogloss:1.13396
[489]	train-mlogloss:1.09776	validation-mlogloss:1.13392
Saved model to disk.
2025-10-26 07:27:10.851354: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:27:10.864058: 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 00:00:1761460030.877598 2616909 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460030.881843 2616909 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460030.892685 2616909 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460030.892706 2616909 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460030.892708 2616909 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460030.892709 2616909 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:27:10.896198: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[1]	train-mlogloss:2.64236	validation-mlogloss:2.64409
[2]	train-mlogloss:2.61211	validation-mlogloss:2.61630
[3]	train-mlogloss:2.58307	validation-mlogloss:2.58753
[4]	train-mlogloss:2.55583	validation-mlogloss:2.56066
[5]	train-mlogloss:2.52935	validation-mlogloss:2.53461
[6]	train-mlogloss:2.50331	validation-mlogloss:2.50915
[7]	train-mlogloss:2.47840	validation-mlogloss:2.48535
[8]	train-mlogloss:2.45334	validation-mlogloss:2.46078
[9]	train-mlogloss:2.42995	validation-mlogloss:2.43817
[10]	train-mlogloss:2.40714	validation-mlogloss:2.41627
[11]	train-mlogloss:2.38489	validation-mlogloss:2.39444
[12]	train-mlogloss:2.36387	validation-mlogloss:2.37390
[13]	train-mlogloss:2.34272	validation-mlogloss:2.35408
[14]	train-mlogloss:2.32176	validation-mlogloss:2.33313
[15]	train-mlogloss:2.30231	validation-mlogloss:2.31443
[16]	train-mlogloss:2.28330	validation-mlogloss:2.29624
[17]	train-mlogloss:2.26499	validation-mlogloss:2.27873
[18]	train-mlogloss:2.24654	validation-mlogloss:2.26067
[19]	train-mlogloss:2.22878	validation-mlogloss:2.24315
[20]	train-mlogloss:2.21084	validation-mlogloss:2.22566
[21]	train-mlogloss:2.19418	validation-mlogloss:2.20936
[22]	train-mlogloss:2.17750	validation-mlogloss:2.19248
[23]	train-mlogloss:2.16195	validation-mlogloss:2.17675
[24]	train-mlogloss:2.14619	validation-mlogloss:2.16141
[25]	train-mlogloss:2.13059	validation-mlogloss:2.14615
[26]	train-mlogloss:2.11605	validation-mlogloss:2.13109
[27]	train-mlogloss:2.10159	validation-mlogloss:2.11661
[28]	train-mlogloss:2.08748	validation-mlogloss:2.10244
[29]	train-mlogloss:2.07334	validation-mlogloss:2.08818
[30]	train-mlogloss:2.05966	validation-mlogloss:2.07428
[31]	train-mlogloss:2.04627	validation-mlogloss:2.06082
[32]	train-mlogloss:2.03303	validation-mlogloss:2.04777
[33]	train-mlogloss:2.01971	validation-mlogloss:2.03443
[34]	train-mlogloss:2.00781	validation-mlogloss:2.02299
[35]	train-mlogloss:1.99565	validation-mlogloss:2.01062
[36]	train-mlogloss:1.98407	validation-mlogloss:1.99878
[37]	train-mlogloss:1.97210	validation-mlogloss:1.98679
[38]	train-mlogloss:1.96089	validation-mlogloss:1.97553
[39]	train-mlogloss:1.94928	validation-mlogloss:1.96369
[40]	train-mlogloss:1.93831	validation-mlogloss:1.95273
[41]	train-mlogloss:1.92744	validation-mlogloss:1.94165
[42]	train-mlogloss:1.91681	validation-mlogloss:1.93096
[43]	train-mlogloss:1.90631	validation-mlogloss:1.92046
[44]	train-mlogloss:1.89647	validation-mlogloss:1.91066
[45]	train-mlogloss:1.88641	validation-mlogloss:1.89993
[46]	train-mlogloss:1.87664	validation-mlogloss:1.89024
[47]	train-mlogloss:1.86662	validation-mlogloss:1.88014
[48]	train-mlogloss:1.85718	validation-mlogloss:1.87053
[49]	train-mlogloss:1.84817	validation-mlogloss:1.86167
[50]	train-mlogloss:1.83885	validation-mlogloss:1.85213
[51]	train-mlogloss:1.82945	validation-mlogloss:1.84282
[52]	train-mlogloss:1.82012	validation-mlogloss:1.83332
[53]	train-mlogloss:1.81141	validation-mlogloss:1.82464
[54]	train-mlogloss:1.80296	validation-mlogloss:1.81617
[55]	train-mlogloss:1.79447	validation-mlogloss:1.80750
[56]	train-mlogloss:1.78617	validation-mlogloss:1.79927
[57]	train-mlogloss:1.77795	validation-mlogloss:1.79092
[58]	train-mlogloss:1.77018	validation-mlogloss:1.78306
[59]	train-mlogloss:1.76187	validation-mlogloss:1.77451
[60]	train-mlogloss:1.75414	validation-mlogloss:1.76704
[61]	train-mlogloss:1.74623	validation-mlogloss:1.75906
[62]	train-mlogloss:1.73829	validation-mlogloss:1.75106
[63]	train-mlogloss:1.73096	validation-mlogloss:1.74351
[64]	train-mlogloss:1.72371	validation-mlogloss:1.73610
[65]	train-mlogloss:1.71669	validation-mlogloss:1.72904
[66]	train-mlogloss:1.70963	validation-mlogloss:1.72220
[67]	train-mlogloss:1.70255	validation-mlogloss:1.71493
[68]	train-mlogloss:1.69576	validation-mlogloss:1.70800
[69]	train-mlogloss:1.68906	validation-mlogloss:1.70103
[70]	train-mlogloss:1.68217	validation-mlogloss:1.69386
[71]	train-mlogloss:1.67585	validation-mlogloss:1.68754
[72]	train-mlogloss:1.66922	validation-mlogloss:1.68096
[73]	train-mlogloss:1.66291	validation-mlogloss:1.67454
[74]	train-mlogloss:1.65666	validation-mlogloss:1.66833
[75]	train-mlogloss:1.65054	validation-mlogloss:1.66222
[76]	train-mlogloss:1.64408	validation-mlogloss:1.65568
[77]	train-mlogloss:1.63811	validation-mlogloss:1.64958
[78]	train-mlogloss:1.63216	validation-mlogloss:1.64342
[79]	train-mlogloss:1.62642	validation-mlogloss:1.63753
[80]	train-mlogloss:1.62061	validation-mlogloss:1.63158
[81]	train-mlogloss:1.61493	validation-mlogloss:1.62601
[82]	train-mlogloss:1.60932	validation-mlogloss:1.62022
[83]	train-mlogloss:1.60361	validation-mlogloss:1.61458
[84]	train-mlogloss:1.59800	validation-mlogloss:1.60901
[85]	train-mlogloss:1.59233	validation-mlogloss:1.60338
[86]	train-mlogloss:1.58693	validation-mlogloss:1.59778
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[89]	train-mlogloss:1.57119	validation-mlogloss:1.58200
[90]	train-mlogloss:1.56604	validation-mlogloss:1.57653
[91]	train-mlogloss:1.56082	validation-mlogloss:1.57136
[92]	train-mlogloss:1.55596	validation-mlogloss:1.56656
[93]	train-mlogloss:1.55098	validation-mlogloss:1.56165
[94]	train-mlogloss:1.54621	validation-mlogloss:1.55687
[95]	train-mlogloss:1.54168	validation-mlogloss:1.55251
[96]	train-mlogloss:1.53694	validation-mlogloss:1.54752
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[99]	train-mlogloss:1.52330	validation-mlogloss:1.53382
[100]	train-mlogloss:1.51902	validation-mlogloss:1.52947
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[102]	train-mlogloss:1.51039	validation-mlogloss:1.52077
[103]	train-mlogloss:1.50595	validation-mlogloss:1.51665
[104]	train-mlogloss:1.50181	validation-mlogloss:1.51238
[105]	train-mlogloss:1.49756	validation-mlogloss:1.50820
[106]	train-mlogloss:1.49344	validation-mlogloss:1.50391
[107]	train-mlogloss:1.48927	validation-mlogloss:1.49952
[108]	train-mlogloss:1.48527	validation-mlogloss:1.49533
[109]	train-mlogloss:1.48145	validation-mlogloss:1.49135
[110]	train-mlogloss:1.47761	validation-mlogloss:1.48728
[111]	train-mlogloss:1.47373	validation-mlogloss:1.48337
[112]	train-mlogloss:1.46998	validation-mlogloss:1.47957
[113]	train-mlogloss:1.46630	validation-mlogloss:1.47590
[114]	train-mlogloss:1.46265	validation-mlogloss:1.47200
[115]	train-mlogloss:1.45895	validation-mlogloss:1.46826
[116]	train-mlogloss:1.45537	validation-mlogloss:1.46471
[117]	train-mlogloss:1.45182	validation-mlogloss:1.46121
[118]	train-mlogloss:1.44836	validation-mlogloss:1.45777
[119]	train-mlogloss:1.44471	validation-mlogloss:1.45402
[120]	train-mlogloss:1.44116	validation-mlogloss:1.45058
[121]	train-mlogloss:1.43771	validation-mlogloss:1.44711
[122]	train-mlogloss:1.43442	validation-mlogloss:1.44399
[123]	train-mlogloss:1.43106	validation-mlogloss:1.44061
[124]	train-mlogloss:1.42778	validation-mlogloss:1.43724
[125]	train-mlogloss:1.42458	validation-mlogloss:1.43417
[126]	train-mlogloss:1.42146	validation-mlogloss:1.43113
[127]	train-mlogloss:1.41830	validation-mlogloss:1.42803
[128]	train-mlogloss:1.41505	validation-mlogloss:1.42464
[129]	train-mlogloss:1.41190	validation-mlogloss:1.42129
[130]	train-mlogloss:1.40875	validation-mlogloss:1.41808
[131]	train-mlogloss:1.40564	validation-mlogloss:1.41483
[132]	train-mlogloss:1.40248	validation-mlogloss:1.41148
[133]	train-mlogloss:1.39932	validation-mlogloss:1.40826
[134]	train-mlogloss:1.39625	validation-mlogloss:1.40529
[135]	train-mlogloss:1.39336	validation-mlogloss:1.40236
[136]	train-mlogloss:1.39052	validation-mlogloss:1.39973
[137]	train-mlogloss:1.38787	validation-mlogloss:1.39700
[138]	train-mlogloss:1.38507	validation-mlogloss:1.39422
[139]	train-mlogloss:1.38224	validation-mlogloss:1.39134
[140]	train-mlogloss:1.37942	validation-mlogloss:1.38846
[141]	train-mlogloss:1.37665	validation-mlogloss:1.38586
[142]	train-mlogloss:1.37402	validation-mlogloss:1.38322
[143]	train-mlogloss:1.37151	validation-mlogloss:1.38083
[144]	train-mlogloss:1.36888	validation-mlogloss:1.37811
[145]	train-mlogloss:1.36628	validation-mlogloss:1.37546
[146]	train-mlogloss:1.36360	validation-mlogloss:1.37283
[147]	train-mlogloss:1.36107	validation-mlogloss:1.37011
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[451]	train-mlogloss:1.10331	validation-mlogloss:1.13791
[452]	train-mlogloss:1.10328	validation-mlogloss:1.13791
[453]	train-mlogloss:1.10306	validation-mlogloss:1.13779
[454]	train-mlogloss:1.10291	validation-mlogloss:1.13769
[455]	train-mlogloss:1.10280	validation-mlogloss:1.13762
[456]	train-mlogloss:1.10271	validation-mlogloss:1.13755
[457]	train-mlogloss:1.10260	validation-mlogloss:1.13758
[458]	train-mlogloss:1.10247	validation-mlogloss:1.13746
[459]	train-mlogloss:1.10225	validation-mlogloss:1.13732
[460]	train-mlogloss:1.10207	validation-mlogloss:1.13721
[461]	train-mlogloss:1.10192	validation-mlogloss:1.13706
[462]	train-mlogloss:1.10184	validation-mlogloss:1.13700
[463]	train-mlogloss:1.10177	validation-mlogloss:1.13692
[464]	train-mlogloss:1.10163	validation-mlogloss:1.13680
[465]	train-mlogloss:1.10142	validation-mlogloss:1.13663
[466]	train-mlogloss:1.10128	validation-mlogloss:1.13653
[467]	train-mlogloss:1.10124	validation-mlogloss:1.13648
[468]	train-mlogloss:1.10112	validation-mlogloss:1.13637
[469]	train-mlogloss:1.10088	validation-mlogloss:1.13622
[470]	train-mlogloss:1.10070	validation-mlogloss:1.13614
[471]	train-mlogloss:1.10063	validation-mlogloss:1.13608
[472]	train-mlogloss:1.10048	validation-mlogloss:1.13595
[473]	train-mlogloss:1.10044	validation-mlogloss:1.13591
[474]	train-mlogloss:1.10031	validation-mlogloss:1.13576
[475]	train-mlogloss:1.10019	validation-mlogloss:1.13563
[476]	train-mlogloss:1.10011	validation-mlogloss:1.13558
[477]	train-mlogloss:1.09985	validation-mlogloss:1.13546
[478]	train-mlogloss:1.09970	validation-mlogloss:1.13536
[479]	train-mlogloss:1.09951	validation-mlogloss:1.13527
[480]	train-mlogloss:1.09939	validation-mlogloss:1.13517
[481]	train-mlogloss:1.09923	validation-mlogloss:1.13510
[482]	train-mlogloss:1.09910	validation-mlogloss:1.13502
[483]	train-mlogloss:1.09897	validation-mlogloss:1.13491
[484]	train-mlogloss:1.09890	validation-mlogloss:1.13482
[485]	train-mlogloss:1.09875	validation-mlogloss:1.13465
[486]	train-mlogloss:1.09871	validation-mlogloss:1.13462
[487]	train-mlogloss:1.09858	validation-mlogloss:1.13451
[488]	train-mlogloss:1.09847	validation-mlogloss:1.13448
[489]	train-mlogloss:1.09841	validation-mlogloss:1.13437
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.62 [%]
Global accuracy score (test) = 63.02 [%]
Global F1 score (train) = 66.45 [%]
Global F1 score (test) = 62.28 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.53      0.42      0.47       161
 CAMINAR CON MÓVIL O LIBRO       0.40      0.60      0.48       161
       CAMINAR USUAL SPEED       0.40      0.27      0.33       161
            CAMINAR ZIGZAG       0.73      0.58      0.65       161
          DE PIE BARRIENDO       0.72      0.63      0.67       161
   DE PIE DOBLANDO TOALLAS       0.43      0.57      0.49       161
    DE PIE MOVIENDO LIBROS       0.54      0.47      0.50       161
          DE PIE USANDO PC       0.84      0.91      0.88       161
        FASE REPOSO CON K5       0.77      0.93      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.59      0.39      0.47       161
         SENTADO USANDO PC       0.47      0.60      0.53       161
      SENTADO VIENDO LA TV       0.48      0.32      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 16: 63.02 [%]
F1-score capturado en la ejecución 16: 62.28 [%]

=== EJECUCIÓN 17 ===

--- TRAIN (ejecución 17) ---

--- TEST (ejecución 17) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.59 [%]
Global accuracy score (test) = 62.28 [%]
Global F1 score (train) = 66.41 [%]
Global F1 score (test) = 61.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.45      0.45      0.45       161
 CAMINAR CON MÓVIL O LIBRO       0.41      0.55      0.47       161
       CAMINAR USUAL SPEED       0.42      0.25      0.32       161
            CAMINAR ZIGZAG       0.72      0.58      0.64       161
          DE PIE BARRIENDO       0.73      0.61      0.67       161
   DE PIE DOBLANDO TOALLAS       0.40      0.52      0.45       161
    DE PIE MOVIENDO LIBROS       0.49      0.43      0.46       161
          DE PIE USANDO PC       0.80      0.89      0.85       161
        FASE REPOSO CON K5       0.78      0.93      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.61      0.39      0.48       161
         SENTADO USANDO PC       0.48      0.62      0.54       161
      SENTADO VIENDO LA TV       0.50      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.65      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.62      2415
                 macro avg       0.63      0.62      0.62      2415
              weighted avg       0.63      0.62      0.62      2415


Accuracy capturado en la ejecución 17: 62.28 [%]
F1-score capturado en la ejecución 17: 61.56 [%]

=== EJECUCIÓN 18 ===

--- TRAIN (ejecución 18) ---

--- TEST (ejecución 18) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.75 [%]
Global accuracy score (test) = 62.77 [%]
Global F1 score (train) = 66.57 [%]
Global F1 score (test) = 61.99 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.51      0.45      0.48       161
 CAMINAR CON MÓVIL O LIBRO       0.40      0.58      0.47       161
       CAMINAR USUAL SPEED       0.48      0.29      0.36       161
            CAMINAR ZIGZAG       0.72      0.58      0.64       161
          DE PIE BARRIENDO       0.74      0.61      0.67       161
   DE PIE DOBLANDO TOALLAS       0.43      0.60      0.50       161
    DE PIE MOVIENDO LIBROS       0.53      0.43      0.47       161
          DE PIE USANDO PC       0.82      0.91      0.86       161
        FASE REPOSO CON K5       0.77      0.92      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.57      0.35      0.44       161
         SENTADO USANDO PC       0.45      0.60      0.51       161
      SENTADO VIENDO LA TV       0.50      0.33      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 18: 62.77 [%]
F1-score capturado en la ejecución 18: 61.99 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-10-26 07:27:50.031863: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:27:50.044443: 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 00:00:1761460070.058053 2617689 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460070.062455 2617689 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460070.073398 2617689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460070.073418 2617689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460070.073421 2617689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460070.073422 2617689 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:27:50.076765: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67500	validation-mlogloss:2.67576
[1]	train-mlogloss:2.64204	validation-mlogloss:2.64363
[2]	train-mlogloss:2.61307	validation-mlogloss:2.61811
[3]	train-mlogloss:2.58456	validation-mlogloss:2.59057
[4]	train-mlogloss:2.55494	validation-mlogloss:2.56182
[5]	train-mlogloss:2.52770	validation-mlogloss:2.53518
[6]	train-mlogloss:2.50219	validation-mlogloss:2.51111
[7]	train-mlogloss:2.47700	validation-mlogloss:2.48638
[8]	train-mlogloss:2.45199	validation-mlogloss:2.46264
[9]	train-mlogloss:2.42836	validation-mlogloss:2.43892
[10]	train-mlogloss:2.40450	validation-mlogloss:2.41505
[11]	train-mlogloss:2.38236	validation-mlogloss:2.39302
[12]	train-mlogloss:2.36184	validation-mlogloss:2.37298
[13]	train-mlogloss:2.34197	validation-mlogloss:2.35328
[14]	train-mlogloss:2.32254	validation-mlogloss:2.33440
[15]	train-mlogloss:2.30413	validation-mlogloss:2.31578
[16]	train-mlogloss:2.28392	validation-mlogloss:2.29532
[17]	train-mlogloss:2.26565	validation-mlogloss:2.27734
[18]	train-mlogloss:2.24697	validation-mlogloss:2.25908
[19]	train-mlogloss:2.22947	validation-mlogloss:2.24199
[20]	train-mlogloss:2.21259	validation-mlogloss:2.22519
[21]	train-mlogloss:2.19583	validation-mlogloss:2.20884
[22]	train-mlogloss:2.17975	validation-mlogloss:2.19308
[23]	train-mlogloss:2.16432	validation-mlogloss:2.17826
[24]	train-mlogloss:2.14850	validation-mlogloss:2.16334
[25]	train-mlogloss:2.13332	validation-mlogloss:2.14835
[26]	train-mlogloss:2.11902	validation-mlogloss:2.13411
[27]	train-mlogloss:2.10445	validation-mlogloss:2.11983
[28]	train-mlogloss:2.09081	validation-mlogloss:2.10589
[29]	train-mlogloss:2.07693	validation-mlogloss:2.09191
[30]	train-mlogloss:2.06294	validation-mlogloss:2.07781
[31]	train-mlogloss:2.04988	validation-mlogloss:2.06486
[32]	train-mlogloss:2.03668	validation-mlogloss:2.05222
[33]	train-mlogloss:2.02398	validation-mlogloss:2.03929
[34]	train-mlogloss:2.01050	validation-mlogloss:2.02567
[35]	train-mlogloss:1.99781	validation-mlogloss:2.01274
[36]	train-mlogloss:1.98576	validation-mlogloss:2.00074
[37]	train-mlogloss:1.97389	validation-mlogloss:1.98901
[38]	train-mlogloss:1.96250	validation-mlogloss:1.97758
[39]	train-mlogloss:1.95109	validation-mlogloss:1.96585
[40]	train-mlogloss:1.93931	validation-mlogloss:1.95383
[41]	train-mlogloss:1.92848	validation-mlogloss:1.94287
[42]	train-mlogloss:1.91767	validation-mlogloss:1.93206
[43]	train-mlogloss:1.90767	validation-mlogloss:1.92194
[44]	train-mlogloss:1.89797	validation-mlogloss:1.91204
[45]	train-mlogloss:1.88794	validation-mlogloss:1.90192
[46]	train-mlogloss:1.87756	validation-mlogloss:1.89152
[47]	train-mlogloss:1.86776	validation-mlogloss:1.88172
[48]	train-mlogloss:1.85820	validation-mlogloss:1.87221
[49]	train-mlogloss:1.84862	validation-mlogloss:1.86279
[50]	train-mlogloss:1.83965	validation-mlogloss:1.85382
[51]	train-mlogloss:1.83068	validation-mlogloss:1.84485
[52]	train-mlogloss:1.82197	validation-mlogloss:1.83598
[53]	train-mlogloss:1.81345	validation-mlogloss:1.82731
[54]	train-mlogloss:1.80496	validation-mlogloss:1.81900
[55]	train-mlogloss:1.79631	validation-mlogloss:1.81032
[56]	train-mlogloss:1.78782	validation-mlogloss:1.80163
[57]	train-mlogloss:1.77953	validation-mlogloss:1.79329
[58]	train-mlogloss:1.77118	validation-mlogloss:1.78453
[59]	train-mlogloss:1.76317	validation-mlogloss:1.77638
[60]	train-mlogloss:1.75536	validation-mlogloss:1.76860
[61]	train-mlogloss:1.74773	validation-mlogloss:1.76117
[62]	train-mlogloss:1.74015	validation-mlogloss:1.75336
[63]	train-mlogloss:1.73255	validation-mlogloss:1.74542
[64]	train-mlogloss:1.72536	validation-mlogloss:1.73834
[65]	train-mlogloss:1.71855	validation-mlogloss:1.73141
[66]	train-mlogloss:1.71147	validation-mlogloss:1.72416
[67]	train-mlogloss:1.70391	validation-mlogloss:1.71637
[68]	train-mlogloss:1.69692	validation-mlogloss:1.70914
[69]	train-mlogloss:1.69005	validation-mlogloss:1.70195
[70]	train-mlogloss:1.68370	validation-mlogloss:1.69585
[71]	train-mlogloss:1.67707	validation-mlogloss:1.68907
[72]	train-mlogloss:1.67061	validation-mlogloss:1.68254
[73]	train-mlogloss:1.66434	validation-mlogloss:1.67631
[74]	train-mlogloss:1.65782	validation-mlogloss:1.66981
[75]	train-mlogloss:1.65169	validation-mlogloss:1.66371
[76]	train-mlogloss:1.64504	validation-mlogloss:1.65671
[77]	train-mlogloss:1.63890	validation-mlogloss:1.65042
[78]	train-mlogloss:1.63329	validation-mlogloss:1.64497
[79]	train-mlogloss:1.62749	validation-mlogloss:1.63926
[80]	train-mlogloss:1.62182	validation-mlogloss:1.63348
[81]	train-mlogloss:1.61616	validation-mlogloss:1.62769
[82]	train-mlogloss:1.61039	validation-mlogloss:1.62197
[83]	train-mlogloss:1.60469	validation-mlogloss:1.61621
[84]	train-mlogloss:1.59924	validation-mlogloss:1.61061
[85]	train-mlogloss:1.59365	validation-mlogloss:1.60478
[86]	train-mlogloss:1.58822	validation-mlogloss:1.59942
[87]	train-mlogloss:1.58290	validation-mlogloss:1.59403
[88]	train-mlogloss:1.57769	validation-mlogloss:1.58870
[89]	train-mlogloss:1.57254	validation-mlogloss:1.58335
[90]	train-mlogloss:1.56751	validation-mlogloss:1.57839
[91]	train-mlogloss:1.56264	validation-mlogloss:1.57346
[92]	train-mlogloss:1.55766	validation-mlogloss:1.56861
[93]	train-mlogloss:1.55296	validation-mlogloss:1.56390
[94]	train-mlogloss:1.54798	validation-mlogloss:1.55894
[95]	train-mlogloss:1.54339	validation-mlogloss:1.55413
[96]	train-mlogloss:1.53885	validation-mlogloss:1.54961
[97]	train-mlogloss:1.53421	validation-mlogloss:1.54496
[98]	train-mlogloss:1.52977	validation-mlogloss:1.54039
[99]	train-mlogloss:1.52548	validation-mlogloss:1.53602
[100]	train-mlogloss:1.52099	validation-mlogloss:1.53140
[101]	train-mlogloss:1.51640	validation-mlogloss:1.52686
[102]	train-mlogloss:1.51223	validation-mlogloss:1.52272
[103]	train-mlogloss:1.50809	validation-mlogloss:1.51869
[104]	train-mlogloss:1.50394	validation-mlogloss:1.51454
[105]	train-mlogloss:1.49964	validation-mlogloss:1.51025
[106]	train-mlogloss:1.49552	validation-mlogloss:1.50622
[107]	train-mlogloss:1.49133	validation-mlogloss:1.50200
[108]	train-mlogloss:1.48712	validation-mlogloss:1.49776
[109]	train-mlogloss:1.48313	validation-mlogloss:1.49360
[110]	train-mlogloss:1.47949	validation-mlogloss:1.49019
[111]	train-mlogloss:1.47577	validation-mlogloss:1.48639
[112]	train-mlogloss:1.47201	validation-mlogloss:1.48286
[113]	train-mlogloss:1.46837	validation-mlogloss:1.47925
[114]	train-mlogloss:1.46457	validation-mlogloss:1.47534
[115]	train-mlogloss:1.46098	validation-mlogloss:1.47200
[116]	train-mlogloss:1.45721	validation-mlogloss:1.46813
[117]	train-mlogloss:1.45384	validation-mlogloss:1.46493
[118]	train-mlogloss:1.45012	validation-mlogloss:1.46117
[119]	train-mlogloss:1.44641	validation-mlogloss:1.45738
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[423]	train-mlogloss:1.10793	validation-mlogloss:1.14240
[424]	train-mlogloss:1.10762	validation-mlogloss:1.14220
[425]	train-mlogloss:1.10748	validation-mlogloss:1.14209
[426]	train-mlogloss:1.10745	validation-mlogloss:1.14211
[427]	train-mlogloss:1.10725	validation-mlogloss:1.14192
[428]	train-mlogloss:1.10705	validation-mlogloss:1.14171
[429]	train-mlogloss:1.10688	validation-mlogloss:1.14160
[430]	train-mlogloss:1.10679	validation-mlogloss:1.14154
[431]	train-mlogloss:1.10658	validation-mlogloss:1.14138
[432]	train-mlogloss:1.10641	validation-mlogloss:1.14128
[433]	train-mlogloss:1.10633	validation-mlogloss:1.14121
[434]	train-mlogloss:1.10612	validation-mlogloss:1.14103
[435]	train-mlogloss:1.10598	validation-mlogloss:1.14092
[436]	train-mlogloss:1.10578	validation-mlogloss:1.14075
[437]	train-mlogloss:1.10549	validation-mlogloss:1.14056
[438]	train-mlogloss:1.10536	validation-mlogloss:1.14043
[439]	train-mlogloss:1.10521	validation-mlogloss:1.14034
[440]	train-mlogloss:1.10509	validation-mlogloss:1.14022
[441]	train-mlogloss:1.10493	validation-mlogloss:1.14012
[442]	train-mlogloss:1.10471	validation-mlogloss:1.14002
[443]	train-mlogloss:1.10448	validation-mlogloss:1.13984
[444]	train-mlogloss:1.10431	validation-mlogloss:1.13972
[445]	train-mlogloss:1.10412	validation-mlogloss:1.13963
[446]	train-mlogloss:1.10394	validation-mlogloss:1.13941
[447]	train-mlogloss:1.10380	validation-mlogloss:1.13934
[448]	train-mlogloss:1.10367	validation-mlogloss:1.13926
[449]	train-mlogloss:1.10338	validation-mlogloss:1.13905
[450]	train-mlogloss:1.10316	validation-mlogloss:1.13888
[451]	train-mlogloss:1.10295	validation-mlogloss:1.13867
[452]	train-mlogloss:1.10285	validation-mlogloss:1.13859
[453]	train-mlogloss:1.10275	validation-mlogloss:1.13852
[454]	train-mlogloss:1.10256	validation-mlogloss:1.13839
[455]	train-mlogloss:1.10241	validation-mlogloss:1.13828
[456]	train-mlogloss:1.10215	validation-mlogloss:1.13801
[457]	train-mlogloss:1.10194	validation-mlogloss:1.13778
[458]	train-mlogloss:1.10184	validation-mlogloss:1.13775
[459]	train-mlogloss:1.10169	validation-mlogloss:1.13767
[460]	train-mlogloss:1.10153	validation-mlogloss:1.13749
[461]	train-mlogloss:1.10143	validation-mlogloss:1.13743
[462]	train-mlogloss:1.10124	validation-mlogloss:1.13731
[463]	train-mlogloss:1.10104	validation-mlogloss:1.13714
[464]	train-mlogloss:1.10094	validation-mlogloss:1.13709
[465]	train-mlogloss:1.10077	validation-mlogloss:1.13697
[466]	train-mlogloss:1.10066	validation-mlogloss:1.13693
[467]	train-mlogloss:1.10054	validation-mlogloss:1.13687
[468]	train-mlogloss:1.10032	validation-mlogloss:1.13676
[469]	train-mlogloss:1.10015	validation-mlogloss:1.13663
[470]	train-mlogloss:1.09995	validation-mlogloss:1.13643
[471]	train-mlogloss:1.09984	validation-mlogloss:1.13630
[472]	train-mlogloss:1.09975	validation-mlogloss:1.13621
[473]	train-mlogloss:1.09966	validation-mlogloss:1.13615
[474]	train-mlogloss:1.09955	validation-mlogloss:1.13607
[475]	train-mlogloss:1.09945	validation-mlogloss:1.13600
[476]	train-mlogloss:1.09931	validation-mlogloss:1.13588
[477]	train-mlogloss:1.09917	validation-mlogloss:1.13576
[478]	train-mlogloss:1.09914	validation-mlogloss:1.13576
[479]	train-mlogloss:1.09911	validation-mlogloss:1.13573
[480]	train-mlogloss:1.09902	validation-mlogloss:1.13569
[481]	train-mlogloss:1.09897	validation-mlogloss:1.13563
[482]	train-mlogloss:1.09879	validation-mlogloss:1.13554
[483]	train-mlogloss:1.09868	validation-mlogloss:1.13549
[484]	train-mlogloss:1.09860	validation-mlogloss:1.13543
[485]	train-mlogloss:1.09847	validation-mlogloss:1.13527
[486]	train-mlogloss:1.09828	validation-mlogloss:1.13518
[487]	train-mlogloss:1.09818	validation-mlogloss:1.13512
[488]	train-mlogloss:1.09806	validation-mlogloss:1.13506
[489]	train-mlogloss:1.09798	validation-mlogloss:1.13499
Saved model to disk.
2025-10-26 07:28:29.710648: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:28:29.723301: 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 00:00:1761460109.737071 2618475 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460109.741543 2618475 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460109.752614 2618475 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460109.752638 2618475 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460109.752641 2618475 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460109.752644 2618475 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:28:29.756038: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67681	validation-mlogloss:2.67824
[1]	train-mlogloss:2.64352	validation-mlogloss:2.64709
[2]	train-mlogloss:2.61303	validation-mlogloss:2.61734
[3]	train-mlogloss:2.58218	validation-mlogloss:2.58681
[4]	train-mlogloss:2.55280	validation-mlogloss:2.55786
[5]	train-mlogloss:2.52580	validation-mlogloss:2.53176
[6]	train-mlogloss:2.49977	validation-mlogloss:2.50743
[7]	train-mlogloss:2.47488	validation-mlogloss:2.48321
[8]	train-mlogloss:2.45109	validation-mlogloss:2.45926
[9]	train-mlogloss:2.42847	validation-mlogloss:2.43767
[10]	train-mlogloss:2.40646	validation-mlogloss:2.41608
[11]	train-mlogloss:2.38374	validation-mlogloss:2.39338
[12]	train-mlogloss:2.36280	validation-mlogloss:2.37262
[13]	train-mlogloss:2.34213	validation-mlogloss:2.35243
[14]	train-mlogloss:2.32212	validation-mlogloss:2.33291
[15]	train-mlogloss:2.30348	validation-mlogloss:2.31547
[16]	train-mlogloss:2.28423	validation-mlogloss:2.29621
[17]	train-mlogloss:2.26505	validation-mlogloss:2.27721
[18]	train-mlogloss:2.24758	validation-mlogloss:2.26028
[19]	train-mlogloss:2.22951	validation-mlogloss:2.24306
[20]	train-mlogloss:2.21271	validation-mlogloss:2.22632
[21]	train-mlogloss:2.19559	validation-mlogloss:2.20854
[22]	train-mlogloss:2.17990	validation-mlogloss:2.19314
[23]	train-mlogloss:2.16325	validation-mlogloss:2.17659
[24]	train-mlogloss:2.14739	validation-mlogloss:2.16060
[25]	train-mlogloss:2.13230	validation-mlogloss:2.14572
[26]	train-mlogloss:2.11757	validation-mlogloss:2.13057
[27]	train-mlogloss:2.10293	validation-mlogloss:2.11621
[28]	train-mlogloss:2.08835	validation-mlogloss:2.10131
[29]	train-mlogloss:2.07451	validation-mlogloss:2.08804
[30]	train-mlogloss:2.06136	validation-mlogloss:2.07483
[31]	train-mlogloss:2.04844	validation-mlogloss:2.06165
[32]	train-mlogloss:2.03542	validation-mlogloss:2.04870
[33]	train-mlogloss:2.02285	validation-mlogloss:2.03603
[34]	train-mlogloss:2.01037	validation-mlogloss:2.02322
[35]	train-mlogloss:1.99794	validation-mlogloss:2.01068
[36]	train-mlogloss:1.98586	validation-mlogloss:1.99908
[37]	train-mlogloss:1.97457	validation-mlogloss:1.98785
[38]	train-mlogloss:1.96306	validation-mlogloss:1.97622
[39]	train-mlogloss:1.95202	validation-mlogloss:1.96506
[40]	train-mlogloss:1.94103	validation-mlogloss:1.95417
[41]	train-mlogloss:1.92993	validation-mlogloss:1.94292
[42]	train-mlogloss:1.91876	validation-mlogloss:1.93169
[43]	train-mlogloss:1.90833	validation-mlogloss:1.92145
[44]	train-mlogloss:1.89786	validation-mlogloss:1.91084
[45]	train-mlogloss:1.88792	validation-mlogloss:1.90105
[46]	train-mlogloss:1.87784	validation-mlogloss:1.89099
[47]	train-mlogloss:1.86816	validation-mlogloss:1.88137
[48]	train-mlogloss:1.85865	validation-mlogloss:1.87164
[49]	train-mlogloss:1.84912	validation-mlogloss:1.86204
[50]	train-mlogloss:1.83957	validation-mlogloss:1.85238
[51]	train-mlogloss:1.83079	validation-mlogloss:1.84399
[52]	train-mlogloss:1.82205	validation-mlogloss:1.83506
[53]	train-mlogloss:1.81317	validation-mlogloss:1.82616
[54]	train-mlogloss:1.80491	validation-mlogloss:1.81779
[55]	train-mlogloss:1.79680	validation-mlogloss:1.80991
[56]	train-mlogloss:1.78816	validation-mlogloss:1.80103
[57]	train-mlogloss:1.77998	validation-mlogloss:1.79253
[58]	train-mlogloss:1.77181	validation-mlogloss:1.78426
[59]	train-mlogloss:1.76365	validation-mlogloss:1.77635
[60]	train-mlogloss:1.75591	validation-mlogloss:1.76849
[61]	train-mlogloss:1.74813	validation-mlogloss:1.76088
[62]	train-mlogloss:1.74044	validation-mlogloss:1.75310
[63]	train-mlogloss:1.73311	validation-mlogloss:1.74567
[64]	train-mlogloss:1.72579	validation-mlogloss:1.73849
[65]	train-mlogloss:1.71855	validation-mlogloss:1.73132
[66]	train-mlogloss:1.71146	validation-mlogloss:1.72438
[67]	train-mlogloss:1.70435	validation-mlogloss:1.71679
[68]	train-mlogloss:1.69754	validation-mlogloss:1.70983
[69]	train-mlogloss:1.69074	validation-mlogloss:1.70309
[70]	train-mlogloss:1.68395	validation-mlogloss:1.69617
[71]	train-mlogloss:1.67715	validation-mlogloss:1.68921
[72]	train-mlogloss:1.67097	validation-mlogloss:1.68313
[73]	train-mlogloss:1.66450	validation-mlogloss:1.67662
[74]	train-mlogloss:1.65820	validation-mlogloss:1.67019
[75]	train-mlogloss:1.65174	validation-mlogloss:1.66359
[76]	train-mlogloss:1.64576	validation-mlogloss:1.65735
[77]	train-mlogloss:1.63960	validation-mlogloss:1.65113
[78]	train-mlogloss:1.63373	validation-mlogloss:1.64536
[79]	train-mlogloss:1.62774	validation-mlogloss:1.63934
[80]	train-mlogloss:1.62183	validation-mlogloss:1.63333
[81]	train-mlogloss:1.61623	validation-mlogloss:1.62776
[82]	train-mlogloss:1.61062	validation-mlogloss:1.62194
[83]	train-mlogloss:1.60523	validation-mlogloss:1.61664
[84]	train-mlogloss:1.59964	validation-mlogloss:1.61083
[85]	train-mlogloss:1.59427	validation-mlogloss:1.60521
[86]	train-mlogloss:1.58898	validation-mlogloss:1.59973
[87]	train-mlogloss:1.58365	validation-mlogloss:1.59404
[88]	train-mlogloss:1.57854	validation-mlogloss:1.58882
[89]	train-mlogloss:1.57337	validation-mlogloss:1.58371
[90]	train-mlogloss:1.56822	validation-mlogloss:1.57851
[91]	train-mlogloss:1.56332	validation-mlogloss:1.57336
[92]	train-mlogloss:1.55829	validation-mlogloss:1.56825
[93]	train-mlogloss:1.55327	validation-mlogloss:1.56318
[94]	train-mlogloss:1.54864	validation-mlogloss:1.55850
[95]	train-mlogloss:1.54399	validation-mlogloss:1.55360
[96]	train-mlogloss:1.53930	validation-mlogloss:1.54886
[97]	train-mlogloss:1.53491	validation-mlogloss:1.54446
[98]	train-mlogloss:1.53032	validation-mlogloss:1.53989
[99]	train-mlogloss:1.52596	validation-mlogloss:1.53545
[100]	train-mlogloss:1.52132	validation-mlogloss:1.53076
[101]	train-mlogloss:1.51705	validation-mlogloss:1.52659
[102]	train-mlogloss:1.51304	validation-mlogloss:1.52274
[103]	train-mlogloss:1.50864	validation-mlogloss:1.51827
[104]	train-mlogloss:1.50453	validation-mlogloss:1.51412
[105]	train-mlogloss:1.50033	validation-mlogloss:1.50992
[106]	train-mlogloss:1.49631	validation-mlogloss:1.50572
[107]	train-mlogloss:1.49262	validation-mlogloss:1.50192
[108]	train-mlogloss:1.48855	validation-mlogloss:1.49799
[109]	train-mlogloss:1.48463	validation-mlogloss:1.49397
[110]	train-mlogloss:1.48075	validation-mlogloss:1.49020
[111]	train-mlogloss:1.47723	validation-mlogloss:1.48671
[112]	train-mlogloss:1.47343	validation-mlogloss:1.48275
[113]	train-mlogloss:1.46977	validation-mlogloss:1.47913
[114]	train-mlogloss:1.46598	validation-mlogloss:1.47522
[115]	train-mlogloss:1.46216	validation-mlogloss:1.47143
[116]	train-mlogloss:1.45851	validation-mlogloss:1.46752
[117]	train-mlogloss:1.45500	validation-mlogloss:1.46398
[118]	train-mlogloss:1.45150	validation-mlogloss:1.46054
[119]	train-mlogloss:1.44811	validation-mlogloss:1.45730
[120]	train-mlogloss:1.44440	validation-mlogloss:1.45372
[121]	train-mlogloss:1.44099	validation-mlogloss:1.45016
[122]	train-mlogloss:1.43758	validation-mlogloss:1.44663
[123]	train-mlogloss:1.43430	validation-mlogloss:1.44328
[124]	train-mlogloss:1.43089	validation-mlogloss:1.43972
[125]	train-mlogloss:1.42751	validation-mlogloss:1.43633
[126]	train-mlogloss:1.42421	validation-mlogloss:1.43306
[127]	train-mlogloss:1.42115	validation-mlogloss:1.42991
[128]	train-mlogloss:1.41785	validation-mlogloss:1.42692
[129]	train-mlogloss:1.41466	validation-mlogloss:1.42361
[130]	train-mlogloss:1.41164	validation-mlogloss:1.42053
[131]	train-mlogloss:1.40871	validation-mlogloss:1.41763
[132]	train-mlogloss:1.40567	validation-mlogloss:1.41451
[133]	train-mlogloss:1.40266	validation-mlogloss:1.41154
[134]	train-mlogloss:1.39986	validation-mlogloss:1.40869
[135]	train-mlogloss:1.39700	validation-mlogloss:1.40579
[136]	train-mlogloss:1.39414	validation-mlogloss:1.40293
[137]	train-mlogloss:1.39128	validation-mlogloss:1.40025
[138]	train-mlogloss:1.38853	validation-mlogloss:1.39743
[139]	train-mlogloss:1.38563	validation-mlogloss:1.39459
[140]	train-mlogloss:1.38287	validation-mlogloss:1.39166
[141]	train-mlogloss:1.38012	validation-mlogloss:1.38914
[142]	train-mlogloss:1.37740	validation-mlogloss:1.38624
[143]	train-mlogloss:1.37492	validation-mlogloss:1.38378
[144]	train-mlogloss:1.37214	validation-mlogloss:1.38079
[145]	train-mlogloss:1.36944	validation-mlogloss:1.37820
[146]	train-mlogloss:1.36689	validation-mlogloss:1.37565
[147]	train-mlogloss:1.36444	validation-mlogloss:1.37317
[148]	train-mlogloss:1.36199	validation-mlogloss:1.37080
[149]	train-mlogloss:1.35948	validation-mlogloss:1.36825
[150]	train-mlogloss:1.35688	validation-mlogloss:1.36576
[151]	train-mlogloss:1.35436	validation-mlogloss:1.36323
[152]	train-mlogloss:1.35183	validation-mlogloss:1.36063
[153]	train-mlogloss:1.34970	validation-mlogloss:1.35856
[154]	train-mlogloss:1.34753	validation-mlogloss:1.35647
[155]	train-mlogloss:1.34505	validation-mlogloss:1.35409
[156]	train-mlogloss:1.34279	validation-mlogloss:1.35167
[157]	train-mlogloss:1.34032	validation-mlogloss:1.34910
[158]	train-mlogloss:1.33792	validation-mlogloss:1.34669
[159]	train-mlogloss:1.33553	validation-mlogloss:1.34417
[160]	train-mlogloss:1.33326	validation-mlogloss:1.34184
[161]	train-mlogloss:1.33101	validation-mlogloss:1.33968
[162]	train-mlogloss:1.32876	validation-mlogloss:1.33748
[163]	train-mlogloss:1.32657	validation-mlogloss:1.33532
[164]	train-mlogloss:1.32436	validation-mlogloss:1.33321
[165]	train-mlogloss:1.32220	validation-mlogloss:1.33123
[166]	train-mlogloss:1.32007	validation-mlogloss:1.32911
[167]	train-mlogloss:1.31817	validation-mlogloss:1.32734
[168]	train-mlogloss:1.31609	validation-mlogloss:1.32530
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[170]	train-mlogloss:1.31183	validation-mlogloss:1.32119
[171]	train-mlogloss:1.30993	validation-mlogloss:1.31932
[172]	train-mlogloss:1.30783	validation-mlogloss:1.31708
[173]	train-mlogloss:1.30570	validation-mlogloss:1.31511
[174]	train-mlogloss:1.30370	validation-mlogloss:1.31306
[175]	train-mlogloss:1.30179	validation-mlogloss:1.31116
[176]	train-mlogloss:1.29989	validation-mlogloss:1.30926
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[179]	train-mlogloss:1.29430	validation-mlogloss:1.30382
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[182]	train-mlogloss:1.28872	validation-mlogloss:1.29816
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[185]	train-mlogloss:1.28346	validation-mlogloss:1.29298
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[187]	train-mlogloss:1.28001	validation-mlogloss:1.28956
[188]	train-mlogloss:1.27827	validation-mlogloss:1.28786
[189]	train-mlogloss:1.27656	validation-mlogloss:1.28632
[190]	train-mlogloss:1.27488	validation-mlogloss:1.28468
[191]	train-mlogloss:1.27318	validation-mlogloss:1.28313
[192]	train-mlogloss:1.27164	validation-mlogloss:1.28176
[193]	train-mlogloss:1.27002	validation-mlogloss:1.27998
[194]	train-mlogloss:1.26840	validation-mlogloss:1.27830
[195]	train-mlogloss:1.26678	validation-mlogloss:1.27673
[196]	train-mlogloss:1.26500	validation-mlogloss:1.27511
[197]	train-mlogloss:1.26336	validation-mlogloss:1.27357
[198]	train-mlogloss:1.26193	validation-mlogloss:1.27217
[199]	train-mlogloss:1.26038	validation-mlogloss:1.27072
[200]	train-mlogloss:1.25891	validation-mlogloss:1.26928
[201]	train-mlogloss:1.25747	validation-mlogloss:1.26794
[202]	train-mlogloss:1.25593	validation-mlogloss:1.26648
[203]	train-mlogloss:1.25448	validation-mlogloss:1.26506
[204]	train-mlogloss:1.25304	validation-mlogloss:1.26364
[205]	train-mlogloss:1.25164	validation-mlogloss:1.26232
[206]	train-mlogloss:1.25022	validation-mlogloss:1.26096
[207]	train-mlogloss:1.24875	validation-mlogloss:1.25958
[208]	train-mlogloss:1.24741	validation-mlogloss:1.25829
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[478]	train-mlogloss:1.10078	validation-mlogloss:1.13443
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[482]	train-mlogloss:1.10006	validation-mlogloss:1.13386
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[485]	train-mlogloss:1.09979	validation-mlogloss:1.13361
[486]	train-mlogloss:1.09969	validation-mlogloss:1.13352
[487]	train-mlogloss:1.09955	validation-mlogloss:1.13339
[488]	train-mlogloss:1.09943	validation-mlogloss:1.13327
[489]	train-mlogloss:1.09931	validation-mlogloss:1.13321
Saved model to disk.
2025-10-26 07:29:09.614821: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:29:09.627312: 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 00:00:1761460149.640866 2619271 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460149.645180 2619271 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460149.655975 2619271 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460149.655996 2619271 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460149.655998 2619271 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460149.656000 2619271 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:29:09.659350: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[301]	train-mlogloss:1.15340	validation-mlogloss:1.17416
[302]	train-mlogloss:1.15279	validation-mlogloss:1.17353
[303]	train-mlogloss:1.15214	validation-mlogloss:1.17302
[304]	train-mlogloss:1.15152	validation-mlogloss:1.17252
[305]	train-mlogloss:1.15084	validation-mlogloss:1.17195
[306]	train-mlogloss:1.15026	validation-mlogloss:1.17144
[307]	train-mlogloss:1.14982	validation-mlogloss:1.17111
[308]	train-mlogloss:1.14915	validation-mlogloss:1.17056
[309]	train-mlogloss:1.14855	validation-mlogloss:1.17009
[310]	train-mlogloss:1.14803	validation-mlogloss:1.16965
[311]	train-mlogloss:1.14732	validation-mlogloss:1.16911
[312]	train-mlogloss:1.14674	validation-mlogloss:1.16862
[313]	train-mlogloss:1.14611	validation-mlogloss:1.16801
[314]	train-mlogloss:1.14543	validation-mlogloss:1.16738
[315]	train-mlogloss:1.14481	validation-mlogloss:1.16686
[316]	train-mlogloss:1.14437	validation-mlogloss:1.16660
[317]	train-mlogloss:1.14399	validation-mlogloss:1.16623
[318]	train-mlogloss:1.14354	validation-mlogloss:1.16589
[319]	train-mlogloss:1.14303	validation-mlogloss:1.16552
[320]	train-mlogloss:1.14247	validation-mlogloss:1.16510
[321]	train-mlogloss:1.14205	validation-mlogloss:1.16477
[322]	train-mlogloss:1.14161	validation-mlogloss:1.16444
[323]	train-mlogloss:1.14120	validation-mlogloss:1.16410
[324]	train-mlogloss:1.14066	validation-mlogloss:1.16373
[325]	train-mlogloss:1.14014	validation-mlogloss:1.16336
[326]	train-mlogloss:1.13967	validation-mlogloss:1.16295
[327]	train-mlogloss:1.13916	validation-mlogloss:1.16261
[328]	train-mlogloss:1.13859	validation-mlogloss:1.16216
[329]	train-mlogloss:1.13814	validation-mlogloss:1.16182
[330]	train-mlogloss:1.13759	validation-mlogloss:1.16140
[331]	train-mlogloss:1.13711	validation-mlogloss:1.16100
[332]	train-mlogloss:1.13662	validation-mlogloss:1.16062
[333]	train-mlogloss:1.13621	validation-mlogloss:1.16028
[334]	train-mlogloss:1.13562	validation-mlogloss:1.15986
[335]	train-mlogloss:1.13520	validation-mlogloss:1.15959
[336]	train-mlogloss:1.13487	validation-mlogloss:1.15934
[337]	train-mlogloss:1.13444	validation-mlogloss:1.15905
[338]	train-mlogloss:1.13406	validation-mlogloss:1.15876
[339]	train-mlogloss:1.13364	validation-mlogloss:1.15848
[340]	train-mlogloss:1.13310	validation-mlogloss:1.15801
[341]	train-mlogloss:1.13283	validation-mlogloss:1.15783
[342]	train-mlogloss:1.13227	validation-mlogloss:1.15737
[343]	train-mlogloss:1.13187	validation-mlogloss:1.15707
[344]	train-mlogloss:1.13141	validation-mlogloss:1.15679
[345]	train-mlogloss:1.13098	validation-mlogloss:1.15653
[346]	train-mlogloss:1.13073	validation-mlogloss:1.15632
[347]	train-mlogloss:1.13039	validation-mlogloss:1.15605
[348]	train-mlogloss:1.12997	validation-mlogloss:1.15563
[349]	train-mlogloss:1.12957	validation-mlogloss:1.15527
[350]	train-mlogloss:1.12912	validation-mlogloss:1.15485
[351]	train-mlogloss:1.12877	validation-mlogloss:1.15459
[352]	train-mlogloss:1.12837	validation-mlogloss:1.15429
[353]	train-mlogloss:1.12816	validation-mlogloss:1.15417
[354]	train-mlogloss:1.12786	validation-mlogloss:1.15401
[355]	train-mlogloss:1.12744	validation-mlogloss:1.15371
[356]	train-mlogloss:1.12713	validation-mlogloss:1.15344
[357]	train-mlogloss:1.12684	validation-mlogloss:1.15323
[358]	train-mlogloss:1.12651	validation-mlogloss:1.15305
[359]	train-mlogloss:1.12618	validation-mlogloss:1.15285
[360]	train-mlogloss:1.12593	validation-mlogloss:1.15268
[361]	train-mlogloss:1.12567	validation-mlogloss:1.15248
[362]	train-mlogloss:1.12540	validation-mlogloss:1.15220
[363]	train-mlogloss:1.12520	validation-mlogloss:1.15202
[364]	train-mlogloss:1.12484	validation-mlogloss:1.15179
[365]	train-mlogloss:1.12442	validation-mlogloss:1.15151
[366]	train-mlogloss:1.12416	validation-mlogloss:1.15132
[367]	train-mlogloss:1.12386	validation-mlogloss:1.15110
[368]	train-mlogloss:1.12355	validation-mlogloss:1.15087
[369]	train-mlogloss:1.12328	validation-mlogloss:1.15059
[370]	train-mlogloss:1.12286	validation-mlogloss:1.15032
[371]	train-mlogloss:1.12238	validation-mlogloss:1.14997
[372]	train-mlogloss:1.12211	validation-mlogloss:1.14966
[373]	train-mlogloss:1.12183	validation-mlogloss:1.14945
[374]	train-mlogloss:1.12151	validation-mlogloss:1.14924
[375]	train-mlogloss:1.12112	validation-mlogloss:1.14891
[376]	train-mlogloss:1.12081	validation-mlogloss:1.14867
[377]	train-mlogloss:1.12047	validation-mlogloss:1.14842
[378]	train-mlogloss:1.12003	validation-mlogloss:1.14809
[379]	train-mlogloss:1.11978	validation-mlogloss:1.14792
[380]	train-mlogloss:1.11938	validation-mlogloss:1.14765
[381]	train-mlogloss:1.11914	validation-mlogloss:1.14748
[382]	train-mlogloss:1.11874	validation-mlogloss:1.14714
[383]	train-mlogloss:1.11862	validation-mlogloss:1.14705
[384]	train-mlogloss:1.11826	validation-mlogloss:1.14681
[385]	train-mlogloss:1.11817	validation-mlogloss:1.14674
[386]	train-mlogloss:1.11788	validation-mlogloss:1.14648
[387]	train-mlogloss:1.11751	validation-mlogloss:1.14619
[388]	train-mlogloss:1.11730	validation-mlogloss:1.14598
[389]	train-mlogloss:1.11699	validation-mlogloss:1.14573
[390]	train-mlogloss:1.11671	validation-mlogloss:1.14545
[391]	train-mlogloss:1.11642	validation-mlogloss:1.14533
[392]	train-mlogloss:1.11610	validation-mlogloss:1.14517
[393]	train-mlogloss:1.11571	validation-mlogloss:1.14488
[394]	train-mlogloss:1.11550	validation-mlogloss:1.14472
[395]	train-mlogloss:1.11538	validation-mlogloss:1.14465
[396]	train-mlogloss:1.11509	validation-mlogloss:1.14448
[397]	train-mlogloss:1.11484	validation-mlogloss:1.14427
[398]	train-mlogloss:1.11446	validation-mlogloss:1.14398
[399]	train-mlogloss:1.11418	validation-mlogloss:1.14381
[400]	train-mlogloss:1.11400	validation-mlogloss:1.14368
[401]	train-mlogloss:1.11374	validation-mlogloss:1.14343
[402]	train-mlogloss:1.11347	validation-mlogloss:1.14324
[403]	train-mlogloss:1.11318	validation-mlogloss:1.14305
[404]	train-mlogloss:1.11291	validation-mlogloss:1.14284
[405]	train-mlogloss:1.11268	validation-mlogloss:1.14261
[406]	train-mlogloss:1.11242	validation-mlogloss:1.14241
[407]	train-mlogloss:1.11223	validation-mlogloss:1.14227
[408]	train-mlogloss:1.11204	validation-mlogloss:1.14217
[409]	train-mlogloss:1.11182	validation-mlogloss:1.14201
[410]	train-mlogloss:1.11163	validation-mlogloss:1.14193
[411]	train-mlogloss:1.11151	validation-mlogloss:1.14183
[412]	train-mlogloss:1.11131	validation-mlogloss:1.14168
[413]	train-mlogloss:1.11100	validation-mlogloss:1.14149
[414]	train-mlogloss:1.11077	validation-mlogloss:1.14126
[415]	train-mlogloss:1.11059	validation-mlogloss:1.14116
[416]	train-mlogloss:1.11047	validation-mlogloss:1.14108
[417]	train-mlogloss:1.11026	validation-mlogloss:1.14091
[418]	train-mlogloss:1.11003	validation-mlogloss:1.14080
[419]	train-mlogloss:1.10976	validation-mlogloss:1.14061
[420]	train-mlogloss:1.10956	validation-mlogloss:1.14050
[421]	train-mlogloss:1.10921	validation-mlogloss:1.14029
[422]	train-mlogloss:1.10895	validation-mlogloss:1.14014
[423]	train-mlogloss:1.10878	validation-mlogloss:1.13996
[424]	train-mlogloss:1.10858	validation-mlogloss:1.13980
[425]	train-mlogloss:1.10839	validation-mlogloss:1.13965
[426]	train-mlogloss:1.10829	validation-mlogloss:1.13953
[427]	train-mlogloss:1.10814	validation-mlogloss:1.13946
[428]	train-mlogloss:1.10800	validation-mlogloss:1.13934
[429]	train-mlogloss:1.10785	validation-mlogloss:1.13925
[430]	train-mlogloss:1.10765	validation-mlogloss:1.13912
[431]	train-mlogloss:1.10755	validation-mlogloss:1.13909
[432]	train-mlogloss:1.10729	validation-mlogloss:1.13889
[433]	train-mlogloss:1.10729	validation-mlogloss:1.13889
[434]	train-mlogloss:1.10705	validation-mlogloss:1.13871
[435]	train-mlogloss:1.10678	validation-mlogloss:1.13845
[436]	train-mlogloss:1.10664	validation-mlogloss:1.13829
[437]	train-mlogloss:1.10644	validation-mlogloss:1.13817
[438]	train-mlogloss:1.10633	validation-mlogloss:1.13806
[439]	train-mlogloss:1.10605	validation-mlogloss:1.13785
[440]	train-mlogloss:1.10588	validation-mlogloss:1.13780
[441]	train-mlogloss:1.10579	validation-mlogloss:1.13774
[442]	train-mlogloss:1.10565	validation-mlogloss:1.13760
[443]	train-mlogloss:1.10543	validation-mlogloss:1.13751
[444]	train-mlogloss:1.10525	validation-mlogloss:1.13736
[445]	train-mlogloss:1.10525	validation-mlogloss:1.13736
[446]	train-mlogloss:1.10504	validation-mlogloss:1.13719
[447]	train-mlogloss:1.10501	validation-mlogloss:1.13715
[448]	train-mlogloss:1.10484	validation-mlogloss:1.13698
[449]	train-mlogloss:1.10465	validation-mlogloss:1.13682
[450]	train-mlogloss:1.10438	validation-mlogloss:1.13659
[451]	train-mlogloss:1.10423	validation-mlogloss:1.13649
[452]	train-mlogloss:1.10413	validation-mlogloss:1.13641
[453]	train-mlogloss:1.10399	validation-mlogloss:1.13629
[454]	train-mlogloss:1.10391	validation-mlogloss:1.13628
[455]	train-mlogloss:1.10375	validation-mlogloss:1.13620
[456]	train-mlogloss:1.10373	validation-mlogloss:1.13620
[457]	train-mlogloss:1.10363	validation-mlogloss:1.13609
[458]	train-mlogloss:1.10347	validation-mlogloss:1.13601
[459]	train-mlogloss:1.10336	validation-mlogloss:1.13588
[460]	train-mlogloss:1.10313	validation-mlogloss:1.13573
[461]	train-mlogloss:1.10301	validation-mlogloss:1.13567
[462]	train-mlogloss:1.10273	validation-mlogloss:1.13547
[463]	train-mlogloss:1.10270	validation-mlogloss:1.13544
[464]	train-mlogloss:1.10258	validation-mlogloss:1.13537
[465]	train-mlogloss:1.10245	validation-mlogloss:1.13530
[466]	train-mlogloss:1.10235	validation-mlogloss:1.13525
[467]	train-mlogloss:1.10227	validation-mlogloss:1.13521
[468]	train-mlogloss:1.10224	validation-mlogloss:1.13518
[469]	train-mlogloss:1.10211	validation-mlogloss:1.13511
[470]	train-mlogloss:1.10187	validation-mlogloss:1.13497
[471]	train-mlogloss:1.10182	validation-mlogloss:1.13490
[472]	train-mlogloss:1.10176	validation-mlogloss:1.13484
[473]	train-mlogloss:1.10158	validation-mlogloss:1.13474
[474]	train-mlogloss:1.10140	validation-mlogloss:1.13459
[475]	train-mlogloss:1.10136	validation-mlogloss:1.13456
[476]	train-mlogloss:1.10124	validation-mlogloss:1.13443
[477]	train-mlogloss:1.10104	validation-mlogloss:1.13435
[478]	train-mlogloss:1.10096	validation-mlogloss:1.13429
[479]	train-mlogloss:1.10092	validation-mlogloss:1.13428
[480]	train-mlogloss:1.10084	validation-mlogloss:1.13422
[481]	train-mlogloss:1.10074	validation-mlogloss:1.13418
[482]	train-mlogloss:1.10068	validation-mlogloss:1.13417
[483]	train-mlogloss:1.10056	validation-mlogloss:1.13412
[484]	train-mlogloss:1.10032	validation-mlogloss:1.13400
[485]	train-mlogloss:1.10020	validation-mlogloss:1.13396
[486]	train-mlogloss:1.10001	validation-mlogloss:1.13382
[487]	train-mlogloss:1.09984	validation-mlogloss:1.13377
[488]	train-mlogloss:1.09973	validation-mlogloss:1.13370
[489]	train-mlogloss:1.09941	validation-mlogloss:1.13340
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.6 [%]
Global accuracy score (test) = 62.9 [%]
Global F1 score (train) = 66.42 [%]
Global F1 score (test) = 62.21 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.46      0.37      0.41       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.58      0.47       161
       CAMINAR USUAL SPEED       0.45      0.29      0.35       161
            CAMINAR ZIGZAG       0.73      0.57      0.64       161
          DE PIE BARRIENDO       0.73      0.61      0.67       161
   DE PIE DOBLANDO TOALLAS       0.44      0.60      0.51       161
    DE PIE MOVIENDO LIBROS       0.54      0.47      0.50       161
          DE PIE USANDO PC       0.81      0.89      0.85       161
        FASE REPOSO CON K5       0.78      0.93      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.59      0.39      0.47       161
         SENTADO USANDO PC       0.48      0.61      0.54       161
      SENTADO VIENDO LA TV       0.54      0.36      0.43       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.93      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.62      2415
              weighted avg       0.64      0.63      0.62      2415


Accuracy capturado en la ejecución 19: 62.9 [%]
F1-score capturado en la ejecución 19: 62.21 [%]

=== EJECUCIÓN 20 ===

--- TRAIN (ejecución 20) ---

--- TEST (ejecución 20) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.75 [%]
Global accuracy score (test) = 62.36 [%]
Global F1 score (train) = 66.55 [%]
Global F1 score (test) = 61.5 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.52      0.47      0.49       161
 CAMINAR CON MÓVIL O LIBRO       0.42      0.58      0.48       161
       CAMINAR USUAL SPEED       0.44      0.26      0.33       161
            CAMINAR ZIGZAG       0.72      0.58      0.64       161
          DE PIE BARRIENDO       0.72      0.61      0.66       161
   DE PIE DOBLANDO TOALLAS       0.41      0.50      0.45       161
    DE PIE MOVIENDO LIBROS       0.50      0.47      0.48       161
          DE PIE USANDO PC       0.80      0.91      0.85       161
        FASE REPOSO CON K5       0.76      0.91      0.83       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.59      0.37      0.46       161
         SENTADO USANDO PC       0.47      0.61      0.53       161
      SENTADO VIENDO LA TV       0.49      0.31      0.38       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.62      2415
                 macro avg       0.63      0.62      0.62      2415
              weighted avg       0.63      0.62      0.62      2415


Accuracy capturado en la ejecución 20: 62.36 [%]
F1-score capturado en la ejecución 20: 61.5 [%]

=== EJECUCIÓN 21 ===

--- TRAIN (ejecución 21) ---

--- TEST (ejecución 21) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.58 [%]
Global accuracy score (test) = 62.77 [%]
Global F1 score (train) = 66.4 [%]
Global F1 score (test) = 61.91 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.48      0.41      0.44       161
 CAMINAR CON MÓVIL O LIBRO       0.41      0.57      0.48       161
       CAMINAR USUAL SPEED       0.40      0.25      0.31       161
            CAMINAR ZIGZAG       0.71      0.58      0.64       161
          DE PIE BARRIENDO       0.74      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.45      0.56      0.50       161
    DE PIE MOVIENDO LIBROS       0.53      0.48      0.51       161
          DE PIE USANDO PC       0.83      0.93      0.88       161
        FASE REPOSO CON K5       0.77      0.91      0.83       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.60      0.36      0.45       161
         SENTADO USANDO PC       0.48      0.61      0.54       161
      SENTADO VIENDO LA TV       0.46      0.34      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 21: 62.77 [%]
F1-score capturado en la ejecución 21: 61.91 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-10-26 07:29:48.439728: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:29:48.452134: 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 00:00:1761460188.465604 2620055 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460188.470088 2620055 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460188.480943 2620055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460188.480967 2620055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460188.480969 2620055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460188.480971 2620055 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:29:48.484335: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67482	validation-mlogloss:2.67601
[1]	train-mlogloss:2.64250	validation-mlogloss:2.64586
[2]	train-mlogloss:2.61175	validation-mlogloss:2.61746
[3]	train-mlogloss:2.58273	validation-mlogloss:2.58930
[4]	train-mlogloss:2.55395	validation-mlogloss:2.56080
[5]	train-mlogloss:2.52761	validation-mlogloss:2.53480
[6]	train-mlogloss:2.50085	validation-mlogloss:2.50742
[7]	train-mlogloss:2.47649	validation-mlogloss:2.48367
[8]	train-mlogloss:2.45165	validation-mlogloss:2.45901
[9]	train-mlogloss:2.42825	validation-mlogloss:2.43712
[10]	train-mlogloss:2.40531	validation-mlogloss:2.41491
[11]	train-mlogloss:2.38327	validation-mlogloss:2.39337
[12]	train-mlogloss:2.36183	validation-mlogloss:2.37241
[13]	train-mlogloss:2.34073	validation-mlogloss:2.35092
[14]	train-mlogloss:2.31991	validation-mlogloss:2.33086
[15]	train-mlogloss:2.30076	validation-mlogloss:2.31220
[16]	train-mlogloss:2.28204	validation-mlogloss:2.29423
[17]	train-mlogloss:2.26246	validation-mlogloss:2.27531
[18]	train-mlogloss:2.24514	validation-mlogloss:2.25787
[19]	train-mlogloss:2.22739	validation-mlogloss:2.24037
[20]	train-mlogloss:2.21005	validation-mlogloss:2.22315
[21]	train-mlogloss:2.19262	validation-mlogloss:2.20537
[22]	train-mlogloss:2.17625	validation-mlogloss:2.18978
[23]	train-mlogloss:2.15905	validation-mlogloss:2.17280
[24]	train-mlogloss:2.14313	validation-mlogloss:2.15700
[25]	train-mlogloss:2.12774	validation-mlogloss:2.14162
[26]	train-mlogloss:2.11344	validation-mlogloss:2.12726
[27]	train-mlogloss:2.09958	validation-mlogloss:2.11378
[28]	train-mlogloss:2.08543	validation-mlogloss:2.09979
[29]	train-mlogloss:2.07175	validation-mlogloss:2.08616
[30]	train-mlogloss:2.05803	validation-mlogloss:2.07205
[31]	train-mlogloss:2.04578	validation-mlogloss:2.05984
[32]	train-mlogloss:2.03300	validation-mlogloss:2.04734
[33]	train-mlogloss:2.01982	validation-mlogloss:2.03363
[34]	train-mlogloss:2.00789	validation-mlogloss:2.02220
[35]	train-mlogloss:1.99564	validation-mlogloss:2.01062
[36]	train-mlogloss:1.98421	validation-mlogloss:1.99947
[37]	train-mlogloss:1.97250	validation-mlogloss:1.98788
[38]	train-mlogloss:1.96102	validation-mlogloss:1.97656
[39]	train-mlogloss:1.94973	validation-mlogloss:1.96546
[40]	train-mlogloss:1.93892	validation-mlogloss:1.95462
[41]	train-mlogloss:1.92782	validation-mlogloss:1.94339
[42]	train-mlogloss:1.91685	validation-mlogloss:1.93229
[43]	train-mlogloss:1.90681	validation-mlogloss:1.92292
[44]	train-mlogloss:1.89670	validation-mlogloss:1.91276
[45]	train-mlogloss:1.88614	validation-mlogloss:1.90178
[46]	train-mlogloss:1.87577	validation-mlogloss:1.89112
[47]	train-mlogloss:1.86576	validation-mlogloss:1.88117
[48]	train-mlogloss:1.85562	validation-mlogloss:1.87072
[49]	train-mlogloss:1.84633	validation-mlogloss:1.86115
[50]	train-mlogloss:1.83682	validation-mlogloss:1.85155
[51]	train-mlogloss:1.82791	validation-mlogloss:1.84274
[52]	train-mlogloss:1.81871	validation-mlogloss:1.83382
[53]	train-mlogloss:1.80984	validation-mlogloss:1.82482
[54]	train-mlogloss:1.80154	validation-mlogloss:1.81646
[55]	train-mlogloss:1.79305	validation-mlogloss:1.80778
[56]	train-mlogloss:1.78475	validation-mlogloss:1.79952
[57]	train-mlogloss:1.77684	validation-mlogloss:1.79182
[58]	train-mlogloss:1.76877	validation-mlogloss:1.78339
[59]	train-mlogloss:1.76091	validation-mlogloss:1.77529
[60]	train-mlogloss:1.75320	validation-mlogloss:1.76744
[61]	train-mlogloss:1.74552	validation-mlogloss:1.75994
[62]	train-mlogloss:1.73787	validation-mlogloss:1.75259
[63]	train-mlogloss:1.73019	validation-mlogloss:1.74496
[64]	train-mlogloss:1.72276	validation-mlogloss:1.73718
[65]	train-mlogloss:1.71566	validation-mlogloss:1.73000
[66]	train-mlogloss:1.70834	validation-mlogloss:1.72285
[67]	train-mlogloss:1.70095	validation-mlogloss:1.71529
[68]	train-mlogloss:1.69403	validation-mlogloss:1.70805
[69]	train-mlogloss:1.68699	validation-mlogloss:1.70068
[70]	train-mlogloss:1.68014	validation-mlogloss:1.69360
[71]	train-mlogloss:1.67368	validation-mlogloss:1.68704
[72]	train-mlogloss:1.66730	validation-mlogloss:1.68072
[73]	train-mlogloss:1.66113	validation-mlogloss:1.67460
[74]	train-mlogloss:1.65458	validation-mlogloss:1.66809
[75]	train-mlogloss:1.64849	validation-mlogloss:1.66193
[76]	train-mlogloss:1.64204	validation-mlogloss:1.65533
[77]	train-mlogloss:1.63587	validation-mlogloss:1.64910
[78]	train-mlogloss:1.62994	validation-mlogloss:1.64300
[79]	train-mlogloss:1.62391	validation-mlogloss:1.63674
[80]	train-mlogloss:1.61796	validation-mlogloss:1.63071
[81]	train-mlogloss:1.61213	validation-mlogloss:1.62479
[82]	train-mlogloss:1.60658	validation-mlogloss:1.61924
[83]	train-mlogloss:1.60132	validation-mlogloss:1.61407
[84]	train-mlogloss:1.59588	validation-mlogloss:1.60835
[85]	train-mlogloss:1.59036	validation-mlogloss:1.60276
[86]	train-mlogloss:1.58510	validation-mlogloss:1.59744
[87]	train-mlogloss:1.57982	validation-mlogloss:1.59202
[88]	train-mlogloss:1.57461	validation-mlogloss:1.58662
[89]	train-mlogloss:1.56927	validation-mlogloss:1.58128
[90]	train-mlogloss:1.56437	validation-mlogloss:1.57632
[91]	train-mlogloss:1.55940	validation-mlogloss:1.57123
[92]	train-mlogloss:1.55459	validation-mlogloss:1.56656
[93]	train-mlogloss:1.54972	validation-mlogloss:1.56141
[94]	train-mlogloss:1.54510	validation-mlogloss:1.55672
[95]	train-mlogloss:1.54038	validation-mlogloss:1.55203
[96]	train-mlogloss:1.53594	validation-mlogloss:1.54740
[97]	train-mlogloss:1.53157	validation-mlogloss:1.54323
[98]	train-mlogloss:1.52721	validation-mlogloss:1.53868
[99]	train-mlogloss:1.52260	validation-mlogloss:1.53395
[100]	train-mlogloss:1.51806	validation-mlogloss:1.52932
[101]	train-mlogloss:1.51368	validation-mlogloss:1.52491
[102]	train-mlogloss:1.50930	validation-mlogloss:1.52037
[103]	train-mlogloss:1.50515	validation-mlogloss:1.51609
[104]	train-mlogloss:1.50079	validation-mlogloss:1.51171
[105]	train-mlogloss:1.49686	validation-mlogloss:1.50776
[106]	train-mlogloss:1.49300	validation-mlogloss:1.50391
[107]	train-mlogloss:1.48887	validation-mlogloss:1.49969
[108]	train-mlogloss:1.48503	validation-mlogloss:1.49579
[109]	train-mlogloss:1.48097	validation-mlogloss:1.49188
[110]	train-mlogloss:1.47692	validation-mlogloss:1.48782
[111]	train-mlogloss:1.47301	validation-mlogloss:1.48396
[112]	train-mlogloss:1.46935	validation-mlogloss:1.48031
[113]	train-mlogloss:1.46561	validation-mlogloss:1.47651
[114]	train-mlogloss:1.46196	validation-mlogloss:1.47284
[115]	train-mlogloss:1.45816	validation-mlogloss:1.46907
[116]	train-mlogloss:1.45446	validation-mlogloss:1.46547
[117]	train-mlogloss:1.45068	validation-mlogloss:1.46163
[118]	train-mlogloss:1.44701	validation-mlogloss:1.45788
[119]	train-mlogloss:1.44335	validation-mlogloss:1.45420
[120]	train-mlogloss:1.44002	validation-mlogloss:1.45075
[121]	train-mlogloss:1.43657	validation-mlogloss:1.44731
[122]	train-mlogloss:1.43321	validation-mlogloss:1.44386
[123]	train-mlogloss:1.42999	validation-mlogloss:1.44059
[124]	train-mlogloss:1.42672	validation-mlogloss:1.43752
[125]	train-mlogloss:1.42336	validation-mlogloss:1.43399
[126]	train-mlogloss:1.42001	validation-mlogloss:1.43066
[127]	train-mlogloss:1.41702	validation-mlogloss:1.42753
[128]	train-mlogloss:1.41388	validation-mlogloss:1.42457
[129]	train-mlogloss:1.41090	validation-mlogloss:1.42161
[130]	train-mlogloss:1.40776	validation-mlogloss:1.41843
[131]	train-mlogloss:1.40465	validation-mlogloss:1.41530
[132]	train-mlogloss:1.40160	validation-mlogloss:1.41228
[133]	train-mlogloss:1.39857	validation-mlogloss:1.40938
[134]	train-mlogloss:1.39568	validation-mlogloss:1.40650
[135]	train-mlogloss:1.39267	validation-mlogloss:1.40336
[136]	train-mlogloss:1.38978	validation-mlogloss:1.40053
[137]	train-mlogloss:1.38693	validation-mlogloss:1.39769
[138]	train-mlogloss:1.38407	validation-mlogloss:1.39480
[139]	train-mlogloss:1.38134	validation-mlogloss:1.39205
[140]	train-mlogloss:1.37879	validation-mlogloss:1.38951
[141]	train-mlogloss:1.37612	validation-mlogloss:1.38675
[142]	train-mlogloss:1.37342	validation-mlogloss:1.38397
[143]	train-mlogloss:1.37074	validation-mlogloss:1.38133
[144]	train-mlogloss:1.36816	validation-mlogloss:1.37870
[145]	train-mlogloss:1.36567	validation-mlogloss:1.37643
[146]	train-mlogloss:1.36322	validation-mlogloss:1.37391
[147]	train-mlogloss:1.36093	validation-mlogloss:1.37151
[148]	train-mlogloss:1.35840	validation-mlogloss:1.36899
[149]	train-mlogloss:1.35584	validation-mlogloss:1.36633
[150]	train-mlogloss:1.35322	validation-mlogloss:1.36377
[151]	train-mlogloss:1.35063	validation-mlogloss:1.36117
[152]	train-mlogloss:1.34827	validation-mlogloss:1.35875
[153]	train-mlogloss:1.34587	validation-mlogloss:1.35643
[154]	train-mlogloss:1.34359	validation-mlogloss:1.35433
[155]	train-mlogloss:1.34110	validation-mlogloss:1.35177
[156]	train-mlogloss:1.33866	validation-mlogloss:1.34927
[157]	train-mlogloss:1.33646	validation-mlogloss:1.34718
[158]	train-mlogloss:1.33428	validation-mlogloss:1.34503
[159]	train-mlogloss:1.33202	validation-mlogloss:1.34272
[160]	train-mlogloss:1.32985	validation-mlogloss:1.34037
[161]	train-mlogloss:1.32773	validation-mlogloss:1.33824
[162]	train-mlogloss:1.32568	validation-mlogloss:1.33626
[163]	train-mlogloss:1.32353	validation-mlogloss:1.33425
[164]	train-mlogloss:1.32146	validation-mlogloss:1.33217
[165]	train-mlogloss:1.31927	validation-mlogloss:1.32999
[166]	train-mlogloss:1.31708	validation-mlogloss:1.32768
[167]	train-mlogloss:1.31504	validation-mlogloss:1.32583
[168]	train-mlogloss:1.31301	validation-mlogloss:1.32380
[169]	train-mlogloss:1.31096	validation-mlogloss:1.32172
[170]	train-mlogloss:1.30884	validation-mlogloss:1.31965
[171]	train-mlogloss:1.30694	validation-mlogloss:1.31782
[172]	train-mlogloss:1.30498	validation-mlogloss:1.31596
[173]	train-mlogloss:1.30304	validation-mlogloss:1.31409
[174]	train-mlogloss:1.30120	validation-mlogloss:1.31225
[175]	train-mlogloss:1.29935	validation-mlogloss:1.31076
[176]	train-mlogloss:1.29737	validation-mlogloss:1.30875
[177]	train-mlogloss:1.29546	validation-mlogloss:1.30699
[178]	train-mlogloss:1.29365	validation-mlogloss:1.30500
[179]	train-mlogloss:1.29180	validation-mlogloss:1.30316
[180]	train-mlogloss:1.29006	validation-mlogloss:1.30153
[181]	train-mlogloss:1.28828	validation-mlogloss:1.29988
[182]	train-mlogloss:1.28649	validation-mlogloss:1.29815
[183]	train-mlogloss:1.28469	validation-mlogloss:1.29652
[184]	train-mlogloss:1.28281	validation-mlogloss:1.29480
[185]	train-mlogloss:1.28095	validation-mlogloss:1.29318
[186]	train-mlogloss:1.27915	validation-mlogloss:1.29146
[187]	train-mlogloss:1.27741	validation-mlogloss:1.28970
[188]	train-mlogloss:1.27570	validation-mlogloss:1.28811
[189]	train-mlogloss:1.27399	validation-mlogloss:1.28650
[190]	train-mlogloss:1.27236	validation-mlogloss:1.28471
[191]	train-mlogloss:1.27077	validation-mlogloss:1.28318
[192]	train-mlogloss:1.26908	validation-mlogloss:1.28161
[193]	train-mlogloss:1.26747	validation-mlogloss:1.28007
[194]	train-mlogloss:1.26579	validation-mlogloss:1.27834
[195]	train-mlogloss:1.26420	validation-mlogloss:1.27681
[196]	train-mlogloss:1.26261	validation-mlogloss:1.27536
[197]	train-mlogloss:1.26097	validation-mlogloss:1.27378
[198]	train-mlogloss:1.25947	validation-mlogloss:1.27235
[199]	train-mlogloss:1.25798	validation-mlogloss:1.27081
[200]	train-mlogloss:1.25646	validation-mlogloss:1.26919
[201]	train-mlogloss:1.25500	validation-mlogloss:1.26786
[202]	train-mlogloss:1.25354	validation-mlogloss:1.26652
[203]	train-mlogloss:1.25208	validation-mlogloss:1.26500
[204]	train-mlogloss:1.25067	validation-mlogloss:1.26364
[205]	train-mlogloss:1.24923	validation-mlogloss:1.26232
[206]	train-mlogloss:1.24777	validation-mlogloss:1.26087
[207]	train-mlogloss:1.24634	validation-mlogloss:1.25942
[208]	train-mlogloss:1.24476	validation-mlogloss:1.25788
[209]	train-mlogloss:1.24328	validation-mlogloss:1.25653
[210]	train-mlogloss:1.24191	validation-mlogloss:1.25535
[211]	train-mlogloss:1.24050	validation-mlogloss:1.25408
[212]	train-mlogloss:1.23901	validation-mlogloss:1.25278
[213]	train-mlogloss:1.23752	validation-mlogloss:1.25140
[214]	train-mlogloss:1.23601	validation-mlogloss:1.25008
[215]	train-mlogloss:1.23463	validation-mlogloss:1.24876
[216]	train-mlogloss:1.23337	validation-mlogloss:1.24761
[217]	train-mlogloss:1.23213	validation-mlogloss:1.24640
[218]	train-mlogloss:1.23075	validation-mlogloss:1.24522
[219]	train-mlogloss:1.22934	validation-mlogloss:1.24395
[220]	train-mlogloss:1.22804	validation-mlogloss:1.24290
[221]	train-mlogloss:1.22674	validation-mlogloss:1.24170
[222]	train-mlogloss:1.22548	validation-mlogloss:1.24040
[223]	train-mlogloss:1.22412	validation-mlogloss:1.23920
[224]	train-mlogloss:1.22286	validation-mlogloss:1.23800
[225]	train-mlogloss:1.22166	validation-mlogloss:1.23682
[226]	train-mlogloss:1.22041	validation-mlogloss:1.23561
[227]	train-mlogloss:1.21922	validation-mlogloss:1.23446
[228]	train-mlogloss:1.21811	validation-mlogloss:1.23345
[229]	train-mlogloss:1.21681	validation-mlogloss:1.23228
[230]	train-mlogloss:1.21574	validation-mlogloss:1.23137
[231]	train-mlogloss:1.21462	validation-mlogloss:1.23033
[232]	train-mlogloss:1.21353	validation-mlogloss:1.22946
[233]	train-mlogloss:1.21239	validation-mlogloss:1.22823
[234]	train-mlogloss:1.21123	validation-mlogloss:1.22717
[235]	train-mlogloss:1.21001	validation-mlogloss:1.22613
[236]	train-mlogloss:1.20883	validation-mlogloss:1.22521
[237]	train-mlogloss:1.20770	validation-mlogloss:1.22409
[238]	train-mlogloss:1.20674	validation-mlogloss:1.22322
[239]	train-mlogloss:1.20558	validation-mlogloss:1.22215
[240]	train-mlogloss:1.20454	validation-mlogloss:1.22131
[241]	train-mlogloss:1.20349	validation-mlogloss:1.22042
[242]	train-mlogloss:1.20237	validation-mlogloss:1.21944
[243]	train-mlogloss:1.20144	validation-mlogloss:1.21867
[244]	train-mlogloss:1.20038	validation-mlogloss:1.21774
[245]	train-mlogloss:1.19919	validation-mlogloss:1.21679
[246]	train-mlogloss:1.19814	validation-mlogloss:1.21579
[247]	train-mlogloss:1.19719	validation-mlogloss:1.21490
[248]	train-mlogloss:1.19625	validation-mlogloss:1.21402
[249]	train-mlogloss:1.19526	validation-mlogloss:1.21308
[250]	train-mlogloss:1.19427	validation-mlogloss:1.21227
[251]	train-mlogloss:1.19318	validation-mlogloss:1.21150
[252]	train-mlogloss:1.19218	validation-mlogloss:1.21067
[253]	train-mlogloss:1.19133	validation-mlogloss:1.20984
[254]	train-mlogloss:1.19036	validation-mlogloss:1.20902
[255]	train-mlogloss:1.18936	validation-mlogloss:1.20803
[256]	train-mlogloss:1.18838	validation-mlogloss:1.20715
[257]	train-mlogloss:1.18739	validation-mlogloss:1.20624
[258]	train-mlogloss:1.18653	validation-mlogloss:1.20550
[259]	train-mlogloss:1.18556	validation-mlogloss:1.20476
[260]	train-mlogloss:1.18457	validation-mlogloss:1.20388
[261]	train-mlogloss:1.18362	validation-mlogloss:1.20307
[262]	train-mlogloss:1.18274	validation-mlogloss:1.20229
[263]	train-mlogloss:1.18191	validation-mlogloss:1.20167
[264]	train-mlogloss:1.18101	validation-mlogloss:1.20087
[265]	train-mlogloss:1.18015	validation-mlogloss:1.20005
[266]	train-mlogloss:1.17927	validation-mlogloss:1.19930
[267]	train-mlogloss:1.17837	validation-mlogloss:1.19854
[268]	train-mlogloss:1.17745	validation-mlogloss:1.19764
[269]	train-mlogloss:1.17663	validation-mlogloss:1.19690
[270]	train-mlogloss:1.17579	validation-mlogloss:1.19612
[271]	train-mlogloss:1.17505	validation-mlogloss:1.19548
[272]	train-mlogloss:1.17425	validation-mlogloss:1.19468
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[332]	train-mlogloss:1.13655	validation-mlogloss:1.16346
[333]	train-mlogloss:1.13611	validation-mlogloss:1.16312
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[338]	train-mlogloss:1.13430	validation-mlogloss:1.16163
[339]	train-mlogloss:1.13389	validation-mlogloss:1.16129
[340]	train-mlogloss:1.13341	validation-mlogloss:1.16081
[341]	train-mlogloss:1.13304	validation-mlogloss:1.16058
[342]	train-mlogloss:1.13263	validation-mlogloss:1.16020
[343]	train-mlogloss:1.13227	validation-mlogloss:1.15992
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[346]	train-mlogloss:1.13121	validation-mlogloss:1.15891
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[355]	train-mlogloss:1.12775	validation-mlogloss:1.15617
[356]	train-mlogloss:1.12743	validation-mlogloss:1.15595
[357]	train-mlogloss:1.12710	validation-mlogloss:1.15573
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[359]	train-mlogloss:1.12642	validation-mlogloss:1.15516
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[429]	train-mlogloss:1.10825	validation-mlogloss:1.14175
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[434]	train-mlogloss:1.10723	validation-mlogloss:1.14118
[435]	train-mlogloss:1.10710	validation-mlogloss:1.14105
[436]	train-mlogloss:1.10701	validation-mlogloss:1.14090
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[439]	train-mlogloss:1.10618	validation-mlogloss:1.14044
[440]	train-mlogloss:1.10598	validation-mlogloss:1.14032
[441]	train-mlogloss:1.10595	validation-mlogloss:1.14029
[442]	train-mlogloss:1.10572	validation-mlogloss:1.14010
[443]	train-mlogloss:1.10566	validation-mlogloss:1.14003
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[447]	train-mlogloss:1.10507	validation-mlogloss:1.13974
[448]	train-mlogloss:1.10502	validation-mlogloss:1.13969
[449]	train-mlogloss:1.10483	validation-mlogloss:1.13947
[450]	train-mlogloss:1.10471	validation-mlogloss:1.13939
[451]	train-mlogloss:1.10459	validation-mlogloss:1.13937
[452]	train-mlogloss:1.10452	validation-mlogloss:1.13930
[453]	train-mlogloss:1.10422	validation-mlogloss:1.13911
[454]	train-mlogloss:1.10403	validation-mlogloss:1.13897
[455]	train-mlogloss:1.10388	validation-mlogloss:1.13892
[456]	train-mlogloss:1.10374	validation-mlogloss:1.13880
[457]	train-mlogloss:1.10349	validation-mlogloss:1.13867
[458]	train-mlogloss:1.10326	validation-mlogloss:1.13850
[459]	train-mlogloss:1.10317	validation-mlogloss:1.13840
[460]	train-mlogloss:1.10293	validation-mlogloss:1.13823
[461]	train-mlogloss:1.10283	validation-mlogloss:1.13812
[462]	train-mlogloss:1.10264	validation-mlogloss:1.13796
[463]	train-mlogloss:1.10246	validation-mlogloss:1.13775
[464]	train-mlogloss:1.10236	validation-mlogloss:1.13765
[465]	train-mlogloss:1.10233	validation-mlogloss:1.13761
[466]	train-mlogloss:1.10219	validation-mlogloss:1.13751
[467]	train-mlogloss:1.10203	validation-mlogloss:1.13731
[468]	train-mlogloss:1.10185	validation-mlogloss:1.13723
[469]	train-mlogloss:1.10178	validation-mlogloss:1.13715
[470]	train-mlogloss:1.10167	validation-mlogloss:1.13710
[471]	train-mlogloss:1.10153	validation-mlogloss:1.13706
[472]	train-mlogloss:1.10144	validation-mlogloss:1.13702
[473]	train-mlogloss:1.10142	validation-mlogloss:1.13698
[474]	train-mlogloss:1.10117	validation-mlogloss:1.13676
[475]	train-mlogloss:1.10113	validation-mlogloss:1.13671
[476]	train-mlogloss:1.10108	validation-mlogloss:1.13667
[477]	train-mlogloss:1.10093	validation-mlogloss:1.13660
[478]	train-mlogloss:1.10069	validation-mlogloss:1.13639
[479]	train-mlogloss:1.10058	validation-mlogloss:1.13634
[480]	train-mlogloss:1.10045	validation-mlogloss:1.13624
[481]	train-mlogloss:1.10034	validation-mlogloss:1.13621
[482]	train-mlogloss:1.10029	validation-mlogloss:1.13614
[483]	train-mlogloss:1.10019	validation-mlogloss:1.13606
[484]	train-mlogloss:1.09996	validation-mlogloss:1.13595
[485]	train-mlogloss:1.09983	validation-mlogloss:1.13580
[486]	train-mlogloss:1.09977	validation-mlogloss:1.13576
[487]	train-mlogloss:1.09970	validation-mlogloss:1.13567
[488]	train-mlogloss:1.09960	validation-mlogloss:1.13555
[489]	train-mlogloss:1.09952	validation-mlogloss:1.13554
Saved model to disk.
2025-10-26 07:30:27.324738: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:30:27.337364: 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 00:00:1761460227.351081 2620833 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460227.355463 2620833 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460227.366264 2620833 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460227.366288 2620833 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460227.366290 2620833 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460227.366292 2620833 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:30:27.369687: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67414	validation-mlogloss:2.67537
[1]	train-mlogloss:2.64171	validation-mlogloss:2.64389
[2]	train-mlogloss:2.61019	validation-mlogloss:2.61258
[3]	train-mlogloss:2.58090	validation-mlogloss:2.58425
[4]	train-mlogloss:2.55203	validation-mlogloss:2.55571
[5]	train-mlogloss:2.52386	validation-mlogloss:2.52767
[6]	train-mlogloss:2.49781	validation-mlogloss:2.50269
[7]	train-mlogloss:2.47166	validation-mlogloss:2.47630
[8]	train-mlogloss:2.44844	validation-mlogloss:2.45384
[9]	train-mlogloss:2.42546	validation-mlogloss:2.43133
[10]	train-mlogloss:2.40276	validation-mlogloss:2.41000
[11]	train-mlogloss:2.38033	validation-mlogloss:2.38798
[12]	train-mlogloss:2.35867	validation-mlogloss:2.36697
[13]	train-mlogloss:2.33726	validation-mlogloss:2.34637
[14]	train-mlogloss:2.31662	validation-mlogloss:2.32653
[15]	train-mlogloss:2.29768	validation-mlogloss:2.30767
[16]	train-mlogloss:2.27822	validation-mlogloss:2.28874
[17]	train-mlogloss:2.25975	validation-mlogloss:2.27022
[18]	train-mlogloss:2.24242	validation-mlogloss:2.25338
[19]	train-mlogloss:2.22502	validation-mlogloss:2.23614
[20]	train-mlogloss:2.20761	validation-mlogloss:2.21954
[21]	train-mlogloss:2.19120	validation-mlogloss:2.20373
[22]	train-mlogloss:2.17468	validation-mlogloss:2.18793
[23]	train-mlogloss:2.15869	validation-mlogloss:2.17196
[24]	train-mlogloss:2.14386	validation-mlogloss:2.15724
[25]	train-mlogloss:2.12849	validation-mlogloss:2.14194
[26]	train-mlogloss:2.11371	validation-mlogloss:2.12740
[27]	train-mlogloss:2.09916	validation-mlogloss:2.11309
[28]	train-mlogloss:2.08535	validation-mlogloss:2.09958
[29]	train-mlogloss:2.07080	validation-mlogloss:2.08533
[30]	train-mlogloss:2.05700	validation-mlogloss:2.07133
[31]	train-mlogloss:2.04385	validation-mlogloss:2.05833
[32]	train-mlogloss:2.03023	validation-mlogloss:2.04468
[33]	train-mlogloss:2.01682	validation-mlogloss:2.03093
[34]	train-mlogloss:2.00464	validation-mlogloss:2.01918
[35]	train-mlogloss:1.99236	validation-mlogloss:2.00689
[36]	train-mlogloss:1.98051	validation-mlogloss:1.99497
[37]	train-mlogloss:1.96896	validation-mlogloss:1.98318
[38]	train-mlogloss:1.95756	validation-mlogloss:1.97182
[39]	train-mlogloss:1.94677	validation-mlogloss:1.96151
[40]	train-mlogloss:1.93531	validation-mlogloss:1.94987
[41]	train-mlogloss:1.92427	validation-mlogloss:1.93862
[42]	train-mlogloss:1.91395	validation-mlogloss:1.92830
[43]	train-mlogloss:1.90377	validation-mlogloss:1.91840
[44]	train-mlogloss:1.89367	validation-mlogloss:1.90868
[45]	train-mlogloss:1.88385	validation-mlogloss:1.89883
[46]	train-mlogloss:1.87389	validation-mlogloss:1.88855
[47]	train-mlogloss:1.86414	validation-mlogloss:1.87896
[48]	train-mlogloss:1.85463	validation-mlogloss:1.86965
[49]	train-mlogloss:1.84540	validation-mlogloss:1.86047
[50]	train-mlogloss:1.83594	validation-mlogloss:1.85113
[51]	train-mlogloss:1.82692	validation-mlogloss:1.84202
[52]	train-mlogloss:1.81767	validation-mlogloss:1.83266
[53]	train-mlogloss:1.80882	validation-mlogloss:1.82371
[54]	train-mlogloss:1.80012	validation-mlogloss:1.81488
[55]	train-mlogloss:1.79145	validation-mlogloss:1.80611
[56]	train-mlogloss:1.78304	validation-mlogloss:1.79781
[57]	train-mlogloss:1.77514	validation-mlogloss:1.79004
[58]	train-mlogloss:1.76713	validation-mlogloss:1.78178
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[362]	train-mlogloss:1.12536	validation-mlogloss:1.15605
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[379]	train-mlogloss:1.12028	validation-mlogloss:1.15227
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[440]	train-mlogloss:1.10627	validation-mlogloss:1.14213
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[453]	train-mlogloss:1.10437	validation-mlogloss:1.14050
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[455]	train-mlogloss:1.10405	validation-mlogloss:1.14031
[456]	train-mlogloss:1.10386	validation-mlogloss:1.14020
[457]	train-mlogloss:1.10369	validation-mlogloss:1.14011
[458]	train-mlogloss:1.10347	validation-mlogloss:1.13994
[459]	train-mlogloss:1.10335	validation-mlogloss:1.13985
[460]	train-mlogloss:1.10319	validation-mlogloss:1.13968
[461]	train-mlogloss:1.10302	validation-mlogloss:1.13956
[462]	train-mlogloss:1.10289	validation-mlogloss:1.13940
[463]	train-mlogloss:1.10280	validation-mlogloss:1.13931
[464]	train-mlogloss:1.10261	validation-mlogloss:1.13915
[465]	train-mlogloss:1.10250	validation-mlogloss:1.13908
[466]	train-mlogloss:1.10242	validation-mlogloss:1.13906
[467]	train-mlogloss:1.10218	validation-mlogloss:1.13884
[468]	train-mlogloss:1.10206	validation-mlogloss:1.13879
[469]	train-mlogloss:1.10191	validation-mlogloss:1.13867
[470]	train-mlogloss:1.10183	validation-mlogloss:1.13862
[471]	train-mlogloss:1.10162	validation-mlogloss:1.13844
[472]	train-mlogloss:1.10140	validation-mlogloss:1.13824
[473]	train-mlogloss:1.10129	validation-mlogloss:1.13818
[474]	train-mlogloss:1.10107	validation-mlogloss:1.13799
[475]	train-mlogloss:1.10096	validation-mlogloss:1.13794
[476]	train-mlogloss:1.10084	validation-mlogloss:1.13785
[477]	train-mlogloss:1.10082	validation-mlogloss:1.13781
[478]	train-mlogloss:1.10056	validation-mlogloss:1.13765
[479]	train-mlogloss:1.10039	validation-mlogloss:1.13746
[480]	train-mlogloss:1.10023	validation-mlogloss:1.13739
[481]	train-mlogloss:1.10002	validation-mlogloss:1.13723
[482]	train-mlogloss:1.09993	validation-mlogloss:1.13715
[483]	train-mlogloss:1.09980	validation-mlogloss:1.13703
[484]	train-mlogloss:1.09967	validation-mlogloss:1.13692
[485]	train-mlogloss:1.09951	validation-mlogloss:1.13677
[486]	train-mlogloss:1.09934	validation-mlogloss:1.13670
[487]	train-mlogloss:1.09929	validation-mlogloss:1.13670
[488]	train-mlogloss:1.09924	validation-mlogloss:1.13668
[489]	train-mlogloss:1.09921	validation-mlogloss:1.13665
Saved model to disk.
2025-10-26 07:31:06.900564: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:31:06.913309: 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 00:00:1761460266.927127 2621635 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460266.931540 2621635 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460266.942898 2621635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460266.942924 2621635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460266.942927 2621635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460266.942929 2621635 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:31:06.946412: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[1]	train-mlogloss:2.64265	validation-mlogloss:2.64514
[2]	train-mlogloss:2.61153	validation-mlogloss:2.61385
[3]	train-mlogloss:2.58137	validation-mlogloss:2.58365
[4]	train-mlogloss:2.55226	validation-mlogloss:2.55446
[5]	train-mlogloss:2.52457	validation-mlogloss:2.52733
[6]	train-mlogloss:2.49783	validation-mlogloss:2.50192
[7]	train-mlogloss:2.47186	validation-mlogloss:2.47687
[8]	train-mlogloss:2.44770	validation-mlogloss:2.45331
[9]	train-mlogloss:2.42409	validation-mlogloss:2.43105
[10]	train-mlogloss:2.40101	validation-mlogloss:2.40786
[11]	train-mlogloss:2.37852	validation-mlogloss:2.38524
[12]	train-mlogloss:2.35721	validation-mlogloss:2.36396
[13]	train-mlogloss:2.33656	validation-mlogloss:2.34401
[14]	train-mlogloss:2.31639	validation-mlogloss:2.32394
[15]	train-mlogloss:2.29679	validation-mlogloss:2.30497
[16]	train-mlogloss:2.27715	validation-mlogloss:2.28527
[17]	train-mlogloss:2.25875	validation-mlogloss:2.26776
[18]	train-mlogloss:2.24101	validation-mlogloss:2.25049
[19]	train-mlogloss:2.22417	validation-mlogloss:2.23419
[20]	train-mlogloss:2.20694	validation-mlogloss:2.21713
[21]	train-mlogloss:2.19021	validation-mlogloss:2.20052
[22]	train-mlogloss:2.17408	validation-mlogloss:2.18470
[23]	train-mlogloss:2.15880	validation-mlogloss:2.16957
[24]	train-mlogloss:2.14302	validation-mlogloss:2.15404
[25]	train-mlogloss:2.12695	validation-mlogloss:2.13802
[26]	train-mlogloss:2.11160	validation-mlogloss:2.12269
[27]	train-mlogloss:2.09736	validation-mlogloss:2.10869
[28]	train-mlogloss:2.08305	validation-mlogloss:2.09429
[29]	train-mlogloss:2.06931	validation-mlogloss:2.08007
[30]	train-mlogloss:2.05601	validation-mlogloss:2.06742
[31]	train-mlogloss:2.04258	validation-mlogloss:2.05423
[32]	train-mlogloss:2.02954	validation-mlogloss:2.04118
[33]	train-mlogloss:2.01660	validation-mlogloss:2.02797
[34]	train-mlogloss:2.00428	validation-mlogloss:2.01576
[35]	train-mlogloss:1.99162	validation-mlogloss:2.00296
[36]	train-mlogloss:1.97937	validation-mlogloss:1.99066
[37]	train-mlogloss:1.96720	validation-mlogloss:1.97847
[38]	train-mlogloss:1.95582	validation-mlogloss:1.96692
[39]	train-mlogloss:1.94427	validation-mlogloss:1.95523
[40]	train-mlogloss:1.93335	validation-mlogloss:1.94401
[41]	train-mlogloss:1.92275	validation-mlogloss:1.93335
[42]	train-mlogloss:1.91223	validation-mlogloss:1.92252
[43]	train-mlogloss:1.90146	validation-mlogloss:1.91185
[44]	train-mlogloss:1.89161	validation-mlogloss:1.90197
[45]	train-mlogloss:1.88177	validation-mlogloss:1.89168
[46]	train-mlogloss:1.87127	validation-mlogloss:1.88135
[47]	train-mlogloss:1.86158	validation-mlogloss:1.87178
[48]	train-mlogloss:1.85238	validation-mlogloss:1.86249
[49]	train-mlogloss:1.84318	validation-mlogloss:1.85352
[50]	train-mlogloss:1.83409	validation-mlogloss:1.84444
[51]	train-mlogloss:1.82512	validation-mlogloss:1.83596
[52]	train-mlogloss:1.81653	validation-mlogloss:1.82748
[53]	train-mlogloss:1.80801	validation-mlogloss:1.81897
[54]	train-mlogloss:1.79909	validation-mlogloss:1.81012
[55]	train-mlogloss:1.79060	validation-mlogloss:1.80142
[56]	train-mlogloss:1.78252	validation-mlogloss:1.79342
[57]	train-mlogloss:1.77459	validation-mlogloss:1.78529
[58]	train-mlogloss:1.76616	validation-mlogloss:1.77676
[59]	train-mlogloss:1.75819	validation-mlogloss:1.76883
[60]	train-mlogloss:1.75036	validation-mlogloss:1.76119
[61]	train-mlogloss:1.74277	validation-mlogloss:1.75364
[62]	train-mlogloss:1.73501	validation-mlogloss:1.74571
[63]	train-mlogloss:1.72759	validation-mlogloss:1.73815
[64]	train-mlogloss:1.72059	validation-mlogloss:1.73100
[65]	train-mlogloss:1.71354	validation-mlogloss:1.72382
[66]	train-mlogloss:1.70637	validation-mlogloss:1.71633
[67]	train-mlogloss:1.69951	validation-mlogloss:1.70921
[68]	train-mlogloss:1.69311	validation-mlogloss:1.70269
[69]	train-mlogloss:1.68625	validation-mlogloss:1.69605
[70]	train-mlogloss:1.67975	validation-mlogloss:1.68950
[71]	train-mlogloss:1.67327	validation-mlogloss:1.68285
[72]	train-mlogloss:1.66701	validation-mlogloss:1.67660
[73]	train-mlogloss:1.66053	validation-mlogloss:1.67000
[74]	train-mlogloss:1.65436	validation-mlogloss:1.66372
[75]	train-mlogloss:1.64832	validation-mlogloss:1.65753
[76]	train-mlogloss:1.64222	validation-mlogloss:1.65130
[77]	train-mlogloss:1.63629	validation-mlogloss:1.64538
[78]	train-mlogloss:1.63015	validation-mlogloss:1.63919
[79]	train-mlogloss:1.62454	validation-mlogloss:1.63338
[80]	train-mlogloss:1.61861	validation-mlogloss:1.62740
[81]	train-mlogloss:1.61293	validation-mlogloss:1.62167
[82]	train-mlogloss:1.60721	validation-mlogloss:1.61579
[83]	train-mlogloss:1.60197	validation-mlogloss:1.61053
[84]	train-mlogloss:1.59661	validation-mlogloss:1.60492
[85]	train-mlogloss:1.59092	validation-mlogloss:1.59925
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[99]	train-mlogloss:1.52301	validation-mlogloss:1.53065
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[102]	train-mlogloss:1.50973	validation-mlogloss:1.51736
[103]	train-mlogloss:1.50554	validation-mlogloss:1.51328
[104]	train-mlogloss:1.50130	validation-mlogloss:1.50897
[105]	train-mlogloss:1.49715	validation-mlogloss:1.50463
[106]	train-mlogloss:1.49293	validation-mlogloss:1.50043
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[108]	train-mlogloss:1.48495	validation-mlogloss:1.49234
[109]	train-mlogloss:1.48105	validation-mlogloss:1.48844
[110]	train-mlogloss:1.47721	validation-mlogloss:1.48446
[111]	train-mlogloss:1.47334	validation-mlogloss:1.48043
[112]	train-mlogloss:1.46972	validation-mlogloss:1.47659
[113]	train-mlogloss:1.46601	validation-mlogloss:1.47306
[114]	train-mlogloss:1.46226	validation-mlogloss:1.46919
[115]	train-mlogloss:1.45856	validation-mlogloss:1.46523
[116]	train-mlogloss:1.45489	validation-mlogloss:1.46165
[117]	train-mlogloss:1.45142	validation-mlogloss:1.45830
[118]	train-mlogloss:1.44778	validation-mlogloss:1.45458
[119]	train-mlogloss:1.44430	validation-mlogloss:1.45117
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[121]	train-mlogloss:1.43777	validation-mlogloss:1.44438
[122]	train-mlogloss:1.43449	validation-mlogloss:1.44112
[123]	train-mlogloss:1.43134	validation-mlogloss:1.43794
[124]	train-mlogloss:1.42809	validation-mlogloss:1.43479
[125]	train-mlogloss:1.42486	validation-mlogloss:1.43154
[126]	train-mlogloss:1.42176	validation-mlogloss:1.42841
[127]	train-mlogloss:1.41862	validation-mlogloss:1.42514
[128]	train-mlogloss:1.41547	validation-mlogloss:1.42181
[129]	train-mlogloss:1.41218	validation-mlogloss:1.41855
[130]	train-mlogloss:1.40920	validation-mlogloss:1.41543
[131]	train-mlogloss:1.40618	validation-mlogloss:1.41239
[132]	train-mlogloss:1.40318	validation-mlogloss:1.40926
[133]	train-mlogloss:1.40013	validation-mlogloss:1.40610
[134]	train-mlogloss:1.39713	validation-mlogloss:1.40300
[135]	train-mlogloss:1.39429	validation-mlogloss:1.40006
[136]	train-mlogloss:1.39128	validation-mlogloss:1.39710
[137]	train-mlogloss:1.38858	validation-mlogloss:1.39446
[138]	train-mlogloss:1.38582	validation-mlogloss:1.39171
[139]	train-mlogloss:1.38303	validation-mlogloss:1.38910
[140]	train-mlogloss:1.38052	validation-mlogloss:1.38664
[141]	train-mlogloss:1.37793	validation-mlogloss:1.38379
[142]	train-mlogloss:1.37530	validation-mlogloss:1.38129
[143]	train-mlogloss:1.37287	validation-mlogloss:1.37888
[144]	train-mlogloss:1.37028	validation-mlogloss:1.37640
[145]	train-mlogloss:1.36764	validation-mlogloss:1.37397
[146]	train-mlogloss:1.36500	validation-mlogloss:1.37144
[147]	train-mlogloss:1.36252	validation-mlogloss:1.36897
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[451]	train-mlogloss:1.10661	validation-mlogloss:1.13840
[452]	train-mlogloss:1.10644	validation-mlogloss:1.13829
[453]	train-mlogloss:1.10631	validation-mlogloss:1.13819
[454]	train-mlogloss:1.10622	validation-mlogloss:1.13813
[455]	train-mlogloss:1.10604	validation-mlogloss:1.13801
[456]	train-mlogloss:1.10583	validation-mlogloss:1.13783
[457]	train-mlogloss:1.10569	validation-mlogloss:1.13777
[458]	train-mlogloss:1.10551	validation-mlogloss:1.13766
[459]	train-mlogloss:1.10544	validation-mlogloss:1.13762
[460]	train-mlogloss:1.10530	validation-mlogloss:1.13749
[461]	train-mlogloss:1.10516	validation-mlogloss:1.13749
[462]	train-mlogloss:1.10496	validation-mlogloss:1.13728
[463]	train-mlogloss:1.10478	validation-mlogloss:1.13715
[464]	train-mlogloss:1.10461	validation-mlogloss:1.13709
[465]	train-mlogloss:1.10453	validation-mlogloss:1.13702
[466]	train-mlogloss:1.10445	validation-mlogloss:1.13695
[467]	train-mlogloss:1.10429	validation-mlogloss:1.13689
[468]	train-mlogloss:1.10414	validation-mlogloss:1.13686
[469]	train-mlogloss:1.10411	validation-mlogloss:1.13685
[470]	train-mlogloss:1.10397	validation-mlogloss:1.13679
[471]	train-mlogloss:1.10391	validation-mlogloss:1.13675
[472]	train-mlogloss:1.10385	validation-mlogloss:1.13669
[473]	train-mlogloss:1.10359	validation-mlogloss:1.13648
[474]	train-mlogloss:1.10348	validation-mlogloss:1.13638
[475]	train-mlogloss:1.10339	validation-mlogloss:1.13630
[476]	train-mlogloss:1.10328	validation-mlogloss:1.13627
[477]	train-mlogloss:1.10309	validation-mlogloss:1.13615
[478]	train-mlogloss:1.10293	validation-mlogloss:1.13606
[479]	train-mlogloss:1.10275	validation-mlogloss:1.13603
[480]	train-mlogloss:1.10265	validation-mlogloss:1.13593
[481]	train-mlogloss:1.10258	validation-mlogloss:1.13594
[482]	train-mlogloss:1.10242	validation-mlogloss:1.13581
[483]	train-mlogloss:1.10229	validation-mlogloss:1.13574
[484]	train-mlogloss:1.10211	validation-mlogloss:1.13561
[485]	train-mlogloss:1.10208	validation-mlogloss:1.13559
[486]	train-mlogloss:1.10194	validation-mlogloss:1.13549
[487]	train-mlogloss:1.10179	validation-mlogloss:1.13533
[488]	train-mlogloss:1.10175	validation-mlogloss:1.13530
[489]	train-mlogloss:1.10168	validation-mlogloss:1.13522
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.85 [%]
Global accuracy score (test) = 62.94 [%]
Global F1 score (train) = 66.7 [%]
Global F1 score (test) = 62.26 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.47      0.43      0.45       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.57      0.47       161
       CAMINAR USUAL SPEED       0.48      0.28      0.35       161
            CAMINAR ZIGZAG       0.73      0.58      0.65       161
          DE PIE BARRIENDO       0.74      0.62      0.67       161
   DE PIE DOBLANDO TOALLAS       0.43      0.55      0.49       161
    DE PIE MOVIENDO LIBROS       0.53      0.48      0.50       161
          DE PIE USANDO PC       0.81      0.89      0.85       161
        FASE REPOSO CON K5       0.78      0.93      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.57      0.39      0.46       161
         SENTADO USANDO PC       0.49      0.60      0.54       161
      SENTADO VIENDO LA TV       0.50      0.35      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 22: 62.94 [%]
F1-score capturado en la ejecución 22: 62.26 [%]

=== EJECUCIÓN 23 ===

--- TRAIN (ejecución 23) ---

--- TEST (ejecución 23) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  6  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.53 [%]
Global accuracy score (test) = 62.82 [%]
Global F1 score (train) = 66.36 [%]
Global F1 score (test) = 61.94 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.47      0.42      0.44       161
 CAMINAR CON MÓVIL O LIBRO       0.41      0.60      0.49       161
       CAMINAR USUAL SPEED       0.48      0.27      0.35       161
            CAMINAR ZIGZAG       0.70      0.58      0.64       161
          DE PIE BARRIENDO       0.74      0.62      0.67       161
   DE PIE DOBLANDO TOALLAS       0.44      0.55      0.49       161
    DE PIE MOVIENDO LIBROS       0.50      0.47      0.48       161
          DE PIE USANDO PC       0.80      0.91      0.85       161
        FASE REPOSO CON K5       0.77      0.93      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.59      0.36      0.45       161
         SENTADO USANDO PC       0.46      0.61      0.53       161
      SENTADO VIENDO LA TV       0.52      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 23: 62.82 [%]
F1-score capturado en la ejecución 23: 61.94 [%]

=== EJECUCIÓN 24 ===

--- TRAIN (ejecución 24) ---

--- TEST (ejecución 24) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.77 [%]
Global accuracy score (test) = 63.52 [%]
Global F1 score (train) = 66.58 [%]
Global F1 score (test) = 62.78 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.54      0.49      0.51       161
 CAMINAR CON MÓVIL O LIBRO       0.40      0.57      0.47       161
       CAMINAR USUAL SPEED       0.45      0.28      0.34       161
            CAMINAR ZIGZAG       0.75      0.59      0.66       161
          DE PIE BARRIENDO       0.72      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.44      0.54      0.49       161
    DE PIE MOVIENDO LIBROS       0.51      0.47      0.49       161
          DE PIE USANDO PC       0.82      0.93      0.87       161
        FASE REPOSO CON K5       0.78      0.92      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.58      0.35      0.44       161
         SENTADO USANDO PC       0.48      0.60      0.53       161
      SENTADO VIENDO LA TV       0.52      0.37      0.43       161
   SUBIR Y BAJAR ESCALERAS       0.65      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.64      2415
                 macro avg       0.64      0.64      0.63      2415
              weighted avg       0.64      0.64      0.63      2415


Accuracy capturado en la ejecución 24: 63.52 [%]
F1-score capturado en la ejecución 24: 62.78 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-10-26 07:31:46.117368: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:31:46.129973: 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 00:00:1761460306.144130 2622430 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460306.148383 2622430 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460306.159740 2622430 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460306.159763 2622430 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460306.159766 2622430 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460306.159767 2622430 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:31:46.163040: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67382	validation-mlogloss:2.67503
[1]	train-mlogloss:2.64182	validation-mlogloss:2.64419
[2]	train-mlogloss:2.61099	validation-mlogloss:2.61382
[3]	train-mlogloss:2.58195	validation-mlogloss:2.58508
[4]	train-mlogloss:2.55506	validation-mlogloss:2.55948
[5]	train-mlogloss:2.52811	validation-mlogloss:2.53377
[6]	train-mlogloss:2.50100	validation-mlogloss:2.50710
[7]	train-mlogloss:2.47538	validation-mlogloss:2.48140
[8]	train-mlogloss:2.45046	validation-mlogloss:2.45624
[9]	train-mlogloss:2.42650	validation-mlogloss:2.43320
[10]	train-mlogloss:2.40301	validation-mlogloss:2.40959
[11]	train-mlogloss:2.38087	validation-mlogloss:2.38819
[12]	train-mlogloss:2.35896	validation-mlogloss:2.36641
[13]	train-mlogloss:2.33903	validation-mlogloss:2.34578
[14]	train-mlogloss:2.31848	validation-mlogloss:2.32524
[15]	train-mlogloss:2.29882	validation-mlogloss:2.30572
[16]	train-mlogloss:2.27941	validation-mlogloss:2.28681
[17]	train-mlogloss:2.26064	validation-mlogloss:2.26814
[18]	train-mlogloss:2.24297	validation-mlogloss:2.25138
[19]	train-mlogloss:2.22568	validation-mlogloss:2.23441
[20]	train-mlogloss:2.20900	validation-mlogloss:2.21739
[21]	train-mlogloss:2.19243	validation-mlogloss:2.20077
[22]	train-mlogloss:2.17643	validation-mlogloss:2.18455
[23]	train-mlogloss:2.16006	validation-mlogloss:2.16762
[24]	train-mlogloss:2.14397	validation-mlogloss:2.15130
[25]	train-mlogloss:2.12938	validation-mlogloss:2.13643
[26]	train-mlogloss:2.11454	validation-mlogloss:2.12192
[27]	train-mlogloss:2.09982	validation-mlogloss:2.10707
[28]	train-mlogloss:2.08494	validation-mlogloss:2.09220
[29]	train-mlogloss:2.07115	validation-mlogloss:2.07830
[30]	train-mlogloss:2.05772	validation-mlogloss:2.06464
[31]	train-mlogloss:2.04413	validation-mlogloss:2.05120
[32]	train-mlogloss:2.03071	validation-mlogloss:2.03817
[33]	train-mlogloss:2.01783	validation-mlogloss:2.02526
[34]	train-mlogloss:2.00553	validation-mlogloss:2.01271
[35]	train-mlogloss:1.99324	validation-mlogloss:2.00079
[36]	train-mlogloss:1.98074	validation-mlogloss:1.98859
[37]	train-mlogloss:1.96898	validation-mlogloss:1.97715
[38]	train-mlogloss:1.95745	validation-mlogloss:1.96593
[39]	train-mlogloss:1.94642	validation-mlogloss:1.95488
[40]	train-mlogloss:1.93539	validation-mlogloss:1.94394
[41]	train-mlogloss:1.92456	validation-mlogloss:1.93300
[42]	train-mlogloss:1.91387	validation-mlogloss:1.92248
[43]	train-mlogloss:1.90339	validation-mlogloss:1.91195
[44]	train-mlogloss:1.89331	validation-mlogloss:1.90191
[45]	train-mlogloss:1.88329	validation-mlogloss:1.89182
[46]	train-mlogloss:1.87356	validation-mlogloss:1.88214
[47]	train-mlogloss:1.86389	validation-mlogloss:1.87232
[48]	train-mlogloss:1.85458	validation-mlogloss:1.86310
[49]	train-mlogloss:1.84569	validation-mlogloss:1.85470
[50]	train-mlogloss:1.83679	validation-mlogloss:1.84591
[51]	train-mlogloss:1.82783	validation-mlogloss:1.83641
[52]	train-mlogloss:1.81854	validation-mlogloss:1.82703
[53]	train-mlogloss:1.80984	validation-mlogloss:1.81853
[54]	train-mlogloss:1.80158	validation-mlogloss:1.81028
[55]	train-mlogloss:1.79320	validation-mlogloss:1.80147
[56]	train-mlogloss:1.78524	validation-mlogloss:1.79334
[57]	train-mlogloss:1.77726	validation-mlogloss:1.78561
[58]	train-mlogloss:1.76929	validation-mlogloss:1.77760
[59]	train-mlogloss:1.76090	validation-mlogloss:1.76912
[60]	train-mlogloss:1.75320	validation-mlogloss:1.76163
[61]	train-mlogloss:1.74538	validation-mlogloss:1.75367
[62]	train-mlogloss:1.73774	validation-mlogloss:1.74617
[63]	train-mlogloss:1.73049	validation-mlogloss:1.73878
[64]	train-mlogloss:1.72285	validation-mlogloss:1.73095
[65]	train-mlogloss:1.71565	validation-mlogloss:1.72403
[66]	train-mlogloss:1.70801	validation-mlogloss:1.71632
[67]	train-mlogloss:1.70116	validation-mlogloss:1.70949
[68]	train-mlogloss:1.69430	validation-mlogloss:1.70267
[69]	train-mlogloss:1.68748	validation-mlogloss:1.69550
[70]	train-mlogloss:1.68105	validation-mlogloss:1.68883
[71]	train-mlogloss:1.67450	validation-mlogloss:1.68205
[72]	train-mlogloss:1.66834	validation-mlogloss:1.67577
[73]	train-mlogloss:1.66190	validation-mlogloss:1.66907
[74]	train-mlogloss:1.65588	validation-mlogloss:1.66305
[75]	train-mlogloss:1.64980	validation-mlogloss:1.65725
[76]	train-mlogloss:1.64356	validation-mlogloss:1.65086
[77]	train-mlogloss:1.63767	validation-mlogloss:1.64503
[78]	train-mlogloss:1.63166	validation-mlogloss:1.63905
[79]	train-mlogloss:1.62576	validation-mlogloss:1.63309
[80]	train-mlogloss:1.62013	validation-mlogloss:1.62748
[81]	train-mlogloss:1.61452	validation-mlogloss:1.62177
[82]	train-mlogloss:1.60877	validation-mlogloss:1.61596
[83]	train-mlogloss:1.60323	validation-mlogloss:1.61063
[84]	train-mlogloss:1.59793	validation-mlogloss:1.60515
[85]	train-mlogloss:1.59254	validation-mlogloss:1.59967
[86]	train-mlogloss:1.58725	validation-mlogloss:1.59432
[87]	train-mlogloss:1.58193	validation-mlogloss:1.58903
[88]	train-mlogloss:1.57669	validation-mlogloss:1.58360
[89]	train-mlogloss:1.57156	validation-mlogloss:1.57834
[90]	train-mlogloss:1.56656	validation-mlogloss:1.57339
[91]	train-mlogloss:1.56170	validation-mlogloss:1.56829
[92]	train-mlogloss:1.55685	validation-mlogloss:1.56340
[93]	train-mlogloss:1.55217	validation-mlogloss:1.55852
[94]	train-mlogloss:1.54751	validation-mlogloss:1.55383
[95]	train-mlogloss:1.54278	validation-mlogloss:1.54891
[96]	train-mlogloss:1.53801	validation-mlogloss:1.54421
[97]	train-mlogloss:1.53344	validation-mlogloss:1.53946
[98]	train-mlogloss:1.52899	validation-mlogloss:1.53482
[99]	train-mlogloss:1.52441	validation-mlogloss:1.53038
[100]	train-mlogloss:1.52016	validation-mlogloss:1.52625
[101]	train-mlogloss:1.51570	validation-mlogloss:1.52149
[102]	train-mlogloss:1.51129	validation-mlogloss:1.51689
[103]	train-mlogloss:1.50678	validation-mlogloss:1.51214
[104]	train-mlogloss:1.50257	validation-mlogloss:1.50801
[105]	train-mlogloss:1.49842	validation-mlogloss:1.50377
[106]	train-mlogloss:1.49421	validation-mlogloss:1.49941
[107]	train-mlogloss:1.49030	validation-mlogloss:1.49553
[108]	train-mlogloss:1.48635	validation-mlogloss:1.49135
[109]	train-mlogloss:1.48240	validation-mlogloss:1.48743
[110]	train-mlogloss:1.47850	validation-mlogloss:1.48335
[111]	train-mlogloss:1.47442	validation-mlogloss:1.47916
[112]	train-mlogloss:1.47058	validation-mlogloss:1.47524
[113]	train-mlogloss:1.46673	validation-mlogloss:1.47129
[114]	train-mlogloss:1.46326	validation-mlogloss:1.46797
[115]	train-mlogloss:1.45937	validation-mlogloss:1.46382
[116]	train-mlogloss:1.45572	validation-mlogloss:1.45996
[117]	train-mlogloss:1.45220	validation-mlogloss:1.45647
[118]	train-mlogloss:1.44867	validation-mlogloss:1.45291
[119]	train-mlogloss:1.44541	validation-mlogloss:1.44961
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[423]	train-mlogloss:1.10856	validation-mlogloss:1.13686
[424]	train-mlogloss:1.10845	validation-mlogloss:1.13675
[425]	train-mlogloss:1.10823	validation-mlogloss:1.13658
[426]	train-mlogloss:1.10811	validation-mlogloss:1.13648
[427]	train-mlogloss:1.10790	validation-mlogloss:1.13634
[428]	train-mlogloss:1.10776	validation-mlogloss:1.13623
[429]	train-mlogloss:1.10754	validation-mlogloss:1.13606
[430]	train-mlogloss:1.10737	validation-mlogloss:1.13599
[431]	train-mlogloss:1.10720	validation-mlogloss:1.13584
[432]	train-mlogloss:1.10707	validation-mlogloss:1.13573
[433]	train-mlogloss:1.10686	validation-mlogloss:1.13559
[434]	train-mlogloss:1.10672	validation-mlogloss:1.13550
[435]	train-mlogloss:1.10661	validation-mlogloss:1.13542
[436]	train-mlogloss:1.10648	validation-mlogloss:1.13537
[437]	train-mlogloss:1.10636	validation-mlogloss:1.13528
[438]	train-mlogloss:1.10616	validation-mlogloss:1.13512
[439]	train-mlogloss:1.10599	validation-mlogloss:1.13503
[440]	train-mlogloss:1.10579	validation-mlogloss:1.13486
[441]	train-mlogloss:1.10556	validation-mlogloss:1.13462
[442]	train-mlogloss:1.10533	validation-mlogloss:1.13445
[443]	train-mlogloss:1.10521	validation-mlogloss:1.13435
[444]	train-mlogloss:1.10511	validation-mlogloss:1.13430
[445]	train-mlogloss:1.10494	validation-mlogloss:1.13419
[446]	train-mlogloss:1.10486	validation-mlogloss:1.13419
[447]	train-mlogloss:1.10464	validation-mlogloss:1.13403
[448]	train-mlogloss:1.10450	validation-mlogloss:1.13385
[449]	train-mlogloss:1.10438	validation-mlogloss:1.13376
[450]	train-mlogloss:1.10424	validation-mlogloss:1.13367
[451]	train-mlogloss:1.10410	validation-mlogloss:1.13360
[452]	train-mlogloss:1.10394	validation-mlogloss:1.13346
[453]	train-mlogloss:1.10384	validation-mlogloss:1.13333
[454]	train-mlogloss:1.10374	validation-mlogloss:1.13328
[455]	train-mlogloss:1.10357	validation-mlogloss:1.13316
[456]	train-mlogloss:1.10357	validation-mlogloss:1.13316
[457]	train-mlogloss:1.10335	validation-mlogloss:1.13306
[458]	train-mlogloss:1.10323	validation-mlogloss:1.13303
[459]	train-mlogloss:1.10314	validation-mlogloss:1.13293
[460]	train-mlogloss:1.10310	validation-mlogloss:1.13289
[461]	train-mlogloss:1.10303	validation-mlogloss:1.13287
[462]	train-mlogloss:1.10298	validation-mlogloss:1.13282
[463]	train-mlogloss:1.10285	validation-mlogloss:1.13272
[464]	train-mlogloss:1.10282	validation-mlogloss:1.13268
[465]	train-mlogloss:1.10269	validation-mlogloss:1.13257
[466]	train-mlogloss:1.10252	validation-mlogloss:1.13245
[467]	train-mlogloss:1.10236	validation-mlogloss:1.13239
[468]	train-mlogloss:1.10227	validation-mlogloss:1.13236
[469]	train-mlogloss:1.10209	validation-mlogloss:1.13217
[470]	train-mlogloss:1.10198	validation-mlogloss:1.13213
[471]	train-mlogloss:1.10178	validation-mlogloss:1.13192
[472]	train-mlogloss:1.10160	validation-mlogloss:1.13175
[473]	train-mlogloss:1.10152	validation-mlogloss:1.13168
[474]	train-mlogloss:1.10135	validation-mlogloss:1.13154
[475]	train-mlogloss:1.10123	validation-mlogloss:1.13142
[476]	train-mlogloss:1.10112	validation-mlogloss:1.13138
[477]	train-mlogloss:1.10101	validation-mlogloss:1.13132
[478]	train-mlogloss:1.10087	validation-mlogloss:1.13126
[479]	train-mlogloss:1.10076	validation-mlogloss:1.13118
[480]	train-mlogloss:1.10070	validation-mlogloss:1.13112
[481]	train-mlogloss:1.10057	validation-mlogloss:1.13100
[482]	train-mlogloss:1.10049	validation-mlogloss:1.13092
[483]	train-mlogloss:1.10039	validation-mlogloss:1.13082
[484]	train-mlogloss:1.10018	validation-mlogloss:1.13063
[485]	train-mlogloss:1.10002	validation-mlogloss:1.13047
[486]	train-mlogloss:1.09991	validation-mlogloss:1.13039
[487]	train-mlogloss:1.09967	validation-mlogloss:1.13022
[488]	train-mlogloss:1.09948	validation-mlogloss:1.13004
[489]	train-mlogloss:1.09935	validation-mlogloss:1.12994
Saved model to disk.
2025-10-26 07:32:24.842203: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:32:24.854785: 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 00:00:1761460344.868326 2623206 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460344.872399 2623206 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460344.883277 2623206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460344.883301 2623206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460344.883304 2623206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460344.883306 2623206 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:32:24.886670: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67328	validation-mlogloss:2.67339
[1]	train-mlogloss:2.64205	validation-mlogloss:2.64218
[2]	train-mlogloss:2.61139	validation-mlogloss:2.61265
[3]	train-mlogloss:2.58002	validation-mlogloss:2.58265
[4]	train-mlogloss:2.55232	validation-mlogloss:2.55669
[5]	train-mlogloss:2.52551	validation-mlogloss:2.53098
[6]	train-mlogloss:2.49982	validation-mlogloss:2.50487
[7]	train-mlogloss:2.47403	validation-mlogloss:2.48030
[8]	train-mlogloss:2.44969	validation-mlogloss:2.45552
[9]	train-mlogloss:2.42604	validation-mlogloss:2.43315
[10]	train-mlogloss:2.40303	validation-mlogloss:2.41038
[11]	train-mlogloss:2.38143	validation-mlogloss:2.38962
[12]	train-mlogloss:2.36050	validation-mlogloss:2.36889
[13]	train-mlogloss:2.34048	validation-mlogloss:2.34888
[14]	train-mlogloss:2.31985	validation-mlogloss:2.32939
[15]	train-mlogloss:2.30121	validation-mlogloss:2.30998
[16]	train-mlogloss:2.28212	validation-mlogloss:2.29069
[17]	train-mlogloss:2.26354	validation-mlogloss:2.27212
[18]	train-mlogloss:2.24484	validation-mlogloss:2.25323
[19]	train-mlogloss:2.22670	validation-mlogloss:2.23530
[20]	train-mlogloss:2.20934	validation-mlogloss:2.21842
[21]	train-mlogloss:2.19336	validation-mlogloss:2.20312
[22]	train-mlogloss:2.17704	validation-mlogloss:2.18686
[23]	train-mlogloss:2.16072	validation-mlogloss:2.17062
[24]	train-mlogloss:2.14536	validation-mlogloss:2.15494
[25]	train-mlogloss:2.13054	validation-mlogloss:2.13976
[26]	train-mlogloss:2.11609	validation-mlogloss:2.12558
[27]	train-mlogloss:2.10118	validation-mlogloss:2.11099
[28]	train-mlogloss:2.08646	validation-mlogloss:2.09657
[29]	train-mlogloss:2.07227	validation-mlogloss:2.08278
[30]	train-mlogloss:2.05854	validation-mlogloss:2.06897
[31]	train-mlogloss:2.04508	validation-mlogloss:2.05532
[32]	train-mlogloss:2.03186	validation-mlogloss:2.04281
[33]	train-mlogloss:2.01977	validation-mlogloss:2.03074
[34]	train-mlogloss:2.00677	validation-mlogloss:2.01783
[35]	train-mlogloss:1.99431	validation-mlogloss:2.00497
[36]	train-mlogloss:1.98207	validation-mlogloss:1.99297
[37]	train-mlogloss:1.97002	validation-mlogloss:1.98111
[38]	train-mlogloss:1.95881	validation-mlogloss:1.97018
[39]	train-mlogloss:1.94703	validation-mlogloss:1.95847
[40]	train-mlogloss:1.93550	validation-mlogloss:1.94650
[41]	train-mlogloss:1.92424	validation-mlogloss:1.93510
[42]	train-mlogloss:1.91387	validation-mlogloss:1.92504
[43]	train-mlogloss:1.90291	validation-mlogloss:1.91394
[44]	train-mlogloss:1.89270	validation-mlogloss:1.90345
[45]	train-mlogloss:1.88273	validation-mlogloss:1.89367
[46]	train-mlogloss:1.87285	validation-mlogloss:1.88409
[47]	train-mlogloss:1.86318	validation-mlogloss:1.87465
[48]	train-mlogloss:1.85378	validation-mlogloss:1.86494
[49]	train-mlogloss:1.84417	validation-mlogloss:1.85517
[50]	train-mlogloss:1.83476	validation-mlogloss:1.84556
[51]	train-mlogloss:1.82540	validation-mlogloss:1.83628
[52]	train-mlogloss:1.81677	validation-mlogloss:1.82760
[53]	train-mlogloss:1.80810	validation-mlogloss:1.81878
[54]	train-mlogloss:1.79934	validation-mlogloss:1.81033
[55]	train-mlogloss:1.79095	validation-mlogloss:1.80150
[56]	train-mlogloss:1.78245	validation-mlogloss:1.79299
[57]	train-mlogloss:1.77430	validation-mlogloss:1.78487
[58]	train-mlogloss:1.76609	validation-mlogloss:1.77654
[59]	train-mlogloss:1.75831	validation-mlogloss:1.76879
[60]	train-mlogloss:1.75024	validation-mlogloss:1.76073
[61]	train-mlogloss:1.74250	validation-mlogloss:1.75327
[62]	train-mlogloss:1.73519	validation-mlogloss:1.74570
[63]	train-mlogloss:1.72769	validation-mlogloss:1.73819
[64]	train-mlogloss:1.72067	validation-mlogloss:1.73091
[65]	train-mlogloss:1.71351	validation-mlogloss:1.72363
[66]	train-mlogloss:1.70650	validation-mlogloss:1.71673
[67]	train-mlogloss:1.69949	validation-mlogloss:1.70962
[68]	train-mlogloss:1.69245	validation-mlogloss:1.70270
[69]	train-mlogloss:1.68568	validation-mlogloss:1.69598
[70]	train-mlogloss:1.67918	validation-mlogloss:1.68920
[71]	train-mlogloss:1.67295	validation-mlogloss:1.68294
[72]	train-mlogloss:1.66653	validation-mlogloss:1.67671
[73]	train-mlogloss:1.66056	validation-mlogloss:1.67103
[74]	train-mlogloss:1.65440	validation-mlogloss:1.66497
[75]	train-mlogloss:1.64829	validation-mlogloss:1.65880
[76]	train-mlogloss:1.64187	validation-mlogloss:1.65226
[77]	train-mlogloss:1.63591	validation-mlogloss:1.64632
[78]	train-mlogloss:1.62977	validation-mlogloss:1.64020
[79]	train-mlogloss:1.62386	validation-mlogloss:1.63412
[80]	train-mlogloss:1.61796	validation-mlogloss:1.62809
[81]	train-mlogloss:1.61220	validation-mlogloss:1.62235
[82]	train-mlogloss:1.60665	validation-mlogloss:1.61676
[83]	train-mlogloss:1.60118	validation-mlogloss:1.61118
[84]	train-mlogloss:1.59581	validation-mlogloss:1.60593
[85]	train-mlogloss:1.59078	validation-mlogloss:1.60075
[86]	train-mlogloss:1.58514	validation-mlogloss:1.59496
[87]	train-mlogloss:1.57971	validation-mlogloss:1.58949
[88]	train-mlogloss:1.57474	validation-mlogloss:1.58436
[89]	train-mlogloss:1.56985	validation-mlogloss:1.57948
[90]	train-mlogloss:1.56459	validation-mlogloss:1.57402
[91]	train-mlogloss:1.55972	validation-mlogloss:1.56908
[92]	train-mlogloss:1.55487	validation-mlogloss:1.56416
[93]	train-mlogloss:1.55007	validation-mlogloss:1.55949
[94]	train-mlogloss:1.54524	validation-mlogloss:1.55444
[95]	train-mlogloss:1.54044	validation-mlogloss:1.54966
[96]	train-mlogloss:1.53596	validation-mlogloss:1.54530
[97]	train-mlogloss:1.53133	validation-mlogloss:1.54062
[98]	train-mlogloss:1.52683	validation-mlogloss:1.53602
[99]	train-mlogloss:1.52254	validation-mlogloss:1.53182
[100]	train-mlogloss:1.51778	validation-mlogloss:1.52696
[101]	train-mlogloss:1.51353	validation-mlogloss:1.52264
[102]	train-mlogloss:1.50933	validation-mlogloss:1.51837
[103]	train-mlogloss:1.50495	validation-mlogloss:1.51384
[104]	train-mlogloss:1.50078	validation-mlogloss:1.50956
[105]	train-mlogloss:1.49646	validation-mlogloss:1.50509
[106]	train-mlogloss:1.49218	validation-mlogloss:1.50068
[107]	train-mlogloss:1.48815	validation-mlogloss:1.49656
[108]	train-mlogloss:1.48394	validation-mlogloss:1.49231
[109]	train-mlogloss:1.48004	validation-mlogloss:1.48830
[110]	train-mlogloss:1.47604	validation-mlogloss:1.48413
[111]	train-mlogloss:1.47206	validation-mlogloss:1.48019
[112]	train-mlogloss:1.46806	validation-mlogloss:1.47607
[113]	train-mlogloss:1.46464	validation-mlogloss:1.47256
[114]	train-mlogloss:1.46098	validation-mlogloss:1.46886
[115]	train-mlogloss:1.45753	validation-mlogloss:1.46540
[116]	train-mlogloss:1.45382	validation-mlogloss:1.46175
[117]	train-mlogloss:1.45016	validation-mlogloss:1.45800
[118]	train-mlogloss:1.44670	validation-mlogloss:1.45452
[119]	train-mlogloss:1.44335	validation-mlogloss:1.45129
[120]	train-mlogloss:1.43971	validation-mlogloss:1.44742
[121]	train-mlogloss:1.43624	validation-mlogloss:1.44382
[122]	train-mlogloss:1.43274	validation-mlogloss:1.44023
[123]	train-mlogloss:1.42945	validation-mlogloss:1.43705
[124]	train-mlogloss:1.42629	validation-mlogloss:1.43392
[125]	train-mlogloss:1.42318	validation-mlogloss:1.43077
[126]	train-mlogloss:1.42003	validation-mlogloss:1.42763
[127]	train-mlogloss:1.41679	validation-mlogloss:1.42432
[128]	train-mlogloss:1.41369	validation-mlogloss:1.42138
[129]	train-mlogloss:1.41044	validation-mlogloss:1.41818
[130]	train-mlogloss:1.40732	validation-mlogloss:1.41518
[131]	train-mlogloss:1.40423	validation-mlogloss:1.41216
[132]	train-mlogloss:1.40132	validation-mlogloss:1.40928
[133]	train-mlogloss:1.39834	validation-mlogloss:1.40633
[134]	train-mlogloss:1.39548	validation-mlogloss:1.40348
[135]	train-mlogloss:1.39254	validation-mlogloss:1.40045
[136]	train-mlogloss:1.38974	validation-mlogloss:1.39769
[137]	train-mlogloss:1.38683	validation-mlogloss:1.39482
[138]	train-mlogloss:1.38394	validation-mlogloss:1.39182
[139]	train-mlogloss:1.38121	validation-mlogloss:1.38899
[140]	train-mlogloss:1.37849	validation-mlogloss:1.38644
[141]	train-mlogloss:1.37573	validation-mlogloss:1.38370
[142]	train-mlogloss:1.37297	validation-mlogloss:1.38112
[143]	train-mlogloss:1.37032	validation-mlogloss:1.37845
[144]	train-mlogloss:1.36771	validation-mlogloss:1.37582
[145]	train-mlogloss:1.36516	validation-mlogloss:1.37341
[146]	train-mlogloss:1.36268	validation-mlogloss:1.37092
[147]	train-mlogloss:1.36026	validation-mlogloss:1.36868
[148]	train-mlogloss:1.35770	validation-mlogloss:1.36608
[149]	train-mlogloss:1.35522	validation-mlogloss:1.36344
[150]	train-mlogloss:1.35271	validation-mlogloss:1.36084
[151]	train-mlogloss:1.35049	validation-mlogloss:1.35866
[152]	train-mlogloss:1.34816	validation-mlogloss:1.35635
[153]	train-mlogloss:1.34584	validation-mlogloss:1.35402
[154]	train-mlogloss:1.34361	validation-mlogloss:1.35155
[155]	train-mlogloss:1.34131	validation-mlogloss:1.34934
[156]	train-mlogloss:1.33903	validation-mlogloss:1.34713
[157]	train-mlogloss:1.33674	validation-mlogloss:1.34480
[158]	train-mlogloss:1.33451	validation-mlogloss:1.34278
[159]	train-mlogloss:1.33230	validation-mlogloss:1.34060
[160]	train-mlogloss:1.33000	validation-mlogloss:1.33834
[161]	train-mlogloss:1.32766	validation-mlogloss:1.33587
[162]	train-mlogloss:1.32538	validation-mlogloss:1.33356
[163]	train-mlogloss:1.32321	validation-mlogloss:1.33166
[164]	train-mlogloss:1.32105	validation-mlogloss:1.32942
[165]	train-mlogloss:1.31886	validation-mlogloss:1.32744
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[169]	train-mlogloss:1.31050	validation-mlogloss:1.31938
[170]	train-mlogloss:1.30848	validation-mlogloss:1.31744
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[172]	train-mlogloss:1.30470	validation-mlogloss:1.31385
[173]	train-mlogloss:1.30285	validation-mlogloss:1.31190
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[176]	train-mlogloss:1.29687	validation-mlogloss:1.30575
[177]	train-mlogloss:1.29504	validation-mlogloss:1.30395
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[179]	train-mlogloss:1.29122	validation-mlogloss:1.30020
[180]	train-mlogloss:1.28942	validation-mlogloss:1.29844
[181]	train-mlogloss:1.28753	validation-mlogloss:1.29657
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[183]	train-mlogloss:1.28393	validation-mlogloss:1.29316
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[185]	train-mlogloss:1.28051	validation-mlogloss:1.28988
[186]	train-mlogloss:1.27888	validation-mlogloss:1.28840
[187]	train-mlogloss:1.27706	validation-mlogloss:1.28663
[188]	train-mlogloss:1.27541	validation-mlogloss:1.28504
[189]	train-mlogloss:1.27380	validation-mlogloss:1.28356
[190]	train-mlogloss:1.27219	validation-mlogloss:1.28207
[191]	train-mlogloss:1.27041	validation-mlogloss:1.28048
[192]	train-mlogloss:1.26869	validation-mlogloss:1.27885
[193]	train-mlogloss:1.26717	validation-mlogloss:1.27752
[194]	train-mlogloss:1.26558	validation-mlogloss:1.27602
[195]	train-mlogloss:1.26409	validation-mlogloss:1.27453
[196]	train-mlogloss:1.26242	validation-mlogloss:1.27297
[197]	train-mlogloss:1.26099	validation-mlogloss:1.27166
[198]	train-mlogloss:1.25944	validation-mlogloss:1.27019
[199]	train-mlogloss:1.25789	validation-mlogloss:1.26856
[200]	train-mlogloss:1.25633	validation-mlogloss:1.26702
[201]	train-mlogloss:1.25478	validation-mlogloss:1.26568
[202]	train-mlogloss:1.25319	validation-mlogloss:1.26424
[203]	train-mlogloss:1.25172	validation-mlogloss:1.26281
[204]	train-mlogloss:1.25027	validation-mlogloss:1.26152
[205]	train-mlogloss:1.24876	validation-mlogloss:1.26012
[206]	train-mlogloss:1.24722	validation-mlogloss:1.25867
[207]	train-mlogloss:1.24562	validation-mlogloss:1.25720
[208]	train-mlogloss:1.24422	validation-mlogloss:1.25586
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[482]	train-mlogloss:1.10018	validation-mlogloss:1.13480
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[484]	train-mlogloss:1.10003	validation-mlogloss:1.13473
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[486]	train-mlogloss:1.09986	validation-mlogloss:1.13457
[487]	train-mlogloss:1.09974	validation-mlogloss:1.13446
[488]	train-mlogloss:1.09970	validation-mlogloss:1.13445
[489]	train-mlogloss:1.09961	validation-mlogloss:1.13436
Saved model to disk.
2025-10-26 07:33:03.780188: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:33:03.793276: 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 00:00:1761460383.807692 2623975 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460383.812232 2623975 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460383.823572 2623975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460383.823595 2623975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460383.823598 2623975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460383.823600 2623975 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:33:03.827099: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
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[300]	train-mlogloss:1.15561	validation-mlogloss:1.17775
[301]	train-mlogloss:1.15485	validation-mlogloss:1.17702
[302]	train-mlogloss:1.15442	validation-mlogloss:1.17660
[303]	train-mlogloss:1.15384	validation-mlogloss:1.17605
[304]	train-mlogloss:1.15343	validation-mlogloss:1.17570
[305]	train-mlogloss:1.15270	validation-mlogloss:1.17521
[306]	train-mlogloss:1.15217	validation-mlogloss:1.17477
[307]	train-mlogloss:1.15157	validation-mlogloss:1.17432
[308]	train-mlogloss:1.15103	validation-mlogloss:1.17392
[309]	train-mlogloss:1.15030	validation-mlogloss:1.17334
[310]	train-mlogloss:1.14961	validation-mlogloss:1.17278
[311]	train-mlogloss:1.14908	validation-mlogloss:1.17236
[312]	train-mlogloss:1.14859	validation-mlogloss:1.17200
[313]	train-mlogloss:1.14800	validation-mlogloss:1.17154
[314]	train-mlogloss:1.14741	validation-mlogloss:1.17099
[315]	train-mlogloss:1.14683	validation-mlogloss:1.17052
[316]	train-mlogloss:1.14629	validation-mlogloss:1.17015
[317]	train-mlogloss:1.14577	validation-mlogloss:1.16980
[318]	train-mlogloss:1.14516	validation-mlogloss:1.16933
[319]	train-mlogloss:1.14475	validation-mlogloss:1.16900
[320]	train-mlogloss:1.14421	validation-mlogloss:1.16858
[321]	train-mlogloss:1.14367	validation-mlogloss:1.16824
[322]	train-mlogloss:1.14319	validation-mlogloss:1.16786
[323]	train-mlogloss:1.14273	validation-mlogloss:1.16752
[324]	train-mlogloss:1.14227	validation-mlogloss:1.16718
[325]	train-mlogloss:1.14166	validation-mlogloss:1.16667
[326]	train-mlogloss:1.14111	validation-mlogloss:1.16619
[327]	train-mlogloss:1.14061	validation-mlogloss:1.16588
[328]	train-mlogloss:1.14025	validation-mlogloss:1.16560
[329]	train-mlogloss:1.13974	validation-mlogloss:1.16519
[330]	train-mlogloss:1.13907	validation-mlogloss:1.16473
[331]	train-mlogloss:1.13863	validation-mlogloss:1.16445
[332]	train-mlogloss:1.13816	validation-mlogloss:1.16404
[333]	train-mlogloss:1.13771	validation-mlogloss:1.16364
[334]	train-mlogloss:1.13721	validation-mlogloss:1.16328
[335]	train-mlogloss:1.13675	validation-mlogloss:1.16286
[336]	train-mlogloss:1.13615	validation-mlogloss:1.16228
[337]	train-mlogloss:1.13562	validation-mlogloss:1.16186
[338]	train-mlogloss:1.13521	validation-mlogloss:1.16143
[339]	train-mlogloss:1.13488	validation-mlogloss:1.16120
[340]	train-mlogloss:1.13450	validation-mlogloss:1.16089
[341]	train-mlogloss:1.13408	validation-mlogloss:1.16045
[342]	train-mlogloss:1.13364	validation-mlogloss:1.16009
[343]	train-mlogloss:1.13320	validation-mlogloss:1.15970
[344]	train-mlogloss:1.13279	validation-mlogloss:1.15936
[345]	train-mlogloss:1.13236	validation-mlogloss:1.15903
[346]	train-mlogloss:1.13199	validation-mlogloss:1.15875
[347]	train-mlogloss:1.13148	validation-mlogloss:1.15843
[348]	train-mlogloss:1.13108	validation-mlogloss:1.15813
[349]	train-mlogloss:1.13079	validation-mlogloss:1.15795
[350]	train-mlogloss:1.13045	validation-mlogloss:1.15775
[351]	train-mlogloss:1.13004	validation-mlogloss:1.15746
[352]	train-mlogloss:1.12985	validation-mlogloss:1.15729
[353]	train-mlogloss:1.12955	validation-mlogloss:1.15702
[354]	train-mlogloss:1.12930	validation-mlogloss:1.15685
[355]	train-mlogloss:1.12909	validation-mlogloss:1.15666
[356]	train-mlogloss:1.12877	validation-mlogloss:1.15658
[357]	train-mlogloss:1.12839	validation-mlogloss:1.15630
[358]	train-mlogloss:1.12783	validation-mlogloss:1.15595
[359]	train-mlogloss:1.12760	validation-mlogloss:1.15575
[360]	train-mlogloss:1.12726	validation-mlogloss:1.15547
[361]	train-mlogloss:1.12683	validation-mlogloss:1.15524
[362]	train-mlogloss:1.12655	validation-mlogloss:1.15504
[363]	train-mlogloss:1.12618	validation-mlogloss:1.15474
[364]	train-mlogloss:1.12588	validation-mlogloss:1.15456
[365]	train-mlogloss:1.12546	validation-mlogloss:1.15428
[366]	train-mlogloss:1.12514	validation-mlogloss:1.15400
[367]	train-mlogloss:1.12476	validation-mlogloss:1.15369
[368]	train-mlogloss:1.12439	validation-mlogloss:1.15336
[369]	train-mlogloss:1.12417	validation-mlogloss:1.15314
[370]	train-mlogloss:1.12381	validation-mlogloss:1.15284
[371]	train-mlogloss:1.12360	validation-mlogloss:1.15266
[372]	train-mlogloss:1.12339	validation-mlogloss:1.15251
[373]	train-mlogloss:1.12323	validation-mlogloss:1.15236
[374]	train-mlogloss:1.12297	validation-mlogloss:1.15217
[375]	train-mlogloss:1.12275	validation-mlogloss:1.15195
[376]	train-mlogloss:1.12245	validation-mlogloss:1.15181
[377]	train-mlogloss:1.12209	validation-mlogloss:1.15154
[378]	train-mlogloss:1.12177	validation-mlogloss:1.15135
[379]	train-mlogloss:1.12154	validation-mlogloss:1.15123
[380]	train-mlogloss:1.12144	validation-mlogloss:1.15113
[381]	train-mlogloss:1.12121	validation-mlogloss:1.15098
[382]	train-mlogloss:1.12092	validation-mlogloss:1.15073
[383]	train-mlogloss:1.12068	validation-mlogloss:1.15055
[384]	train-mlogloss:1.12051	validation-mlogloss:1.15046
[385]	train-mlogloss:1.12025	validation-mlogloss:1.15033
[386]	train-mlogloss:1.11993	validation-mlogloss:1.15012
[387]	train-mlogloss:1.11963	validation-mlogloss:1.14986
[388]	train-mlogloss:1.11943	validation-mlogloss:1.14976
[389]	train-mlogloss:1.11910	validation-mlogloss:1.14951
[390]	train-mlogloss:1.11880	validation-mlogloss:1.14934
[391]	train-mlogloss:1.11855	validation-mlogloss:1.14913
[392]	train-mlogloss:1.11815	validation-mlogloss:1.14879
[393]	train-mlogloss:1.11783	validation-mlogloss:1.14860
[394]	train-mlogloss:1.11771	validation-mlogloss:1.14851
[395]	train-mlogloss:1.11736	validation-mlogloss:1.14822
[396]	train-mlogloss:1.11718	validation-mlogloss:1.14811
[397]	train-mlogloss:1.11698	validation-mlogloss:1.14798
[398]	train-mlogloss:1.11688	validation-mlogloss:1.14790
[399]	train-mlogloss:1.11656	validation-mlogloss:1.14765
[400]	train-mlogloss:1.11636	validation-mlogloss:1.14761
[401]	train-mlogloss:1.11611	validation-mlogloss:1.14739
[402]	train-mlogloss:1.11596	validation-mlogloss:1.14728
[403]	train-mlogloss:1.11567	validation-mlogloss:1.14712
[404]	train-mlogloss:1.11528	validation-mlogloss:1.14681
[405]	train-mlogloss:1.11496	validation-mlogloss:1.14655
[406]	train-mlogloss:1.11462	validation-mlogloss:1.14632
[407]	train-mlogloss:1.11446	validation-mlogloss:1.14622
[408]	train-mlogloss:1.11417	validation-mlogloss:1.14599
[409]	train-mlogloss:1.11407	validation-mlogloss:1.14594
[410]	train-mlogloss:1.11380	validation-mlogloss:1.14573
[411]	train-mlogloss:1.11358	validation-mlogloss:1.14548
[412]	train-mlogloss:1.11330	validation-mlogloss:1.14523
[413]	train-mlogloss:1.11308	validation-mlogloss:1.14510
[414]	train-mlogloss:1.11284	validation-mlogloss:1.14484
[415]	train-mlogloss:1.11271	validation-mlogloss:1.14475
[416]	train-mlogloss:1.11238	validation-mlogloss:1.14450
[417]	train-mlogloss:1.11207	validation-mlogloss:1.14424
[418]	train-mlogloss:1.11192	validation-mlogloss:1.14409
[419]	train-mlogloss:1.11179	validation-mlogloss:1.14400
[420]	train-mlogloss:1.11157	validation-mlogloss:1.14377
[421]	train-mlogloss:1.11129	validation-mlogloss:1.14364
[422]	train-mlogloss:1.11109	validation-mlogloss:1.14352
[423]	train-mlogloss:1.11090	validation-mlogloss:1.14340
[424]	train-mlogloss:1.11058	validation-mlogloss:1.14318
[425]	train-mlogloss:1.11033	validation-mlogloss:1.14297
[426]	train-mlogloss:1.11010	validation-mlogloss:1.14287
[427]	train-mlogloss:1.10987	validation-mlogloss:1.14261
[428]	train-mlogloss:1.10957	validation-mlogloss:1.14243
[429]	train-mlogloss:1.10937	validation-mlogloss:1.14225
[430]	train-mlogloss:1.10916	validation-mlogloss:1.14205
[431]	train-mlogloss:1.10901	validation-mlogloss:1.14188
[432]	train-mlogloss:1.10896	validation-mlogloss:1.14184
[433]	train-mlogloss:1.10865	validation-mlogloss:1.14157
[434]	train-mlogloss:1.10845	validation-mlogloss:1.14149
[435]	train-mlogloss:1.10818	validation-mlogloss:1.14124
[436]	train-mlogloss:1.10795	validation-mlogloss:1.14102
[437]	train-mlogloss:1.10783	validation-mlogloss:1.14092
[438]	train-mlogloss:1.10754	validation-mlogloss:1.14068
[439]	train-mlogloss:1.10727	validation-mlogloss:1.14050
[440]	train-mlogloss:1.10711	validation-mlogloss:1.14039
[441]	train-mlogloss:1.10689	validation-mlogloss:1.14021
[442]	train-mlogloss:1.10684	validation-mlogloss:1.14020
[443]	train-mlogloss:1.10670	validation-mlogloss:1.14010
[444]	train-mlogloss:1.10662	validation-mlogloss:1.14005
[445]	train-mlogloss:1.10654	validation-mlogloss:1.14000
[446]	train-mlogloss:1.10640	validation-mlogloss:1.13991
[447]	train-mlogloss:1.10622	validation-mlogloss:1.13972
[448]	train-mlogloss:1.10612	validation-mlogloss:1.13966
[449]	train-mlogloss:1.10588	validation-mlogloss:1.13951
[450]	train-mlogloss:1.10575	validation-mlogloss:1.13945
[451]	train-mlogloss:1.10562	validation-mlogloss:1.13935
[452]	train-mlogloss:1.10540	validation-mlogloss:1.13920
[453]	train-mlogloss:1.10530	validation-mlogloss:1.13912
[454]	train-mlogloss:1.10525	validation-mlogloss:1.13910
[455]	train-mlogloss:1.10507	validation-mlogloss:1.13889
[456]	train-mlogloss:1.10498	validation-mlogloss:1.13886
[457]	train-mlogloss:1.10490	validation-mlogloss:1.13881
[458]	train-mlogloss:1.10477	validation-mlogloss:1.13869
[459]	train-mlogloss:1.10450	validation-mlogloss:1.13847
[460]	train-mlogloss:1.10439	validation-mlogloss:1.13838
[461]	train-mlogloss:1.10423	validation-mlogloss:1.13822
[462]	train-mlogloss:1.10406	validation-mlogloss:1.13811
[463]	train-mlogloss:1.10397	validation-mlogloss:1.13807
[464]	train-mlogloss:1.10382	validation-mlogloss:1.13797
[465]	train-mlogloss:1.10363	validation-mlogloss:1.13784
[466]	train-mlogloss:1.10345	validation-mlogloss:1.13774
[467]	train-mlogloss:1.10336	validation-mlogloss:1.13764
[468]	train-mlogloss:1.10321	validation-mlogloss:1.13754
[469]	train-mlogloss:1.10305	validation-mlogloss:1.13740
[470]	train-mlogloss:1.10291	validation-mlogloss:1.13736
[471]	train-mlogloss:1.10273	validation-mlogloss:1.13720
[472]	train-mlogloss:1.10256	validation-mlogloss:1.13710
[473]	train-mlogloss:1.10250	validation-mlogloss:1.13704
[474]	train-mlogloss:1.10236	validation-mlogloss:1.13694
[475]	train-mlogloss:1.10224	validation-mlogloss:1.13684
[476]	train-mlogloss:1.10208	validation-mlogloss:1.13671
[477]	train-mlogloss:1.10197	validation-mlogloss:1.13661
[478]	train-mlogloss:1.10178	validation-mlogloss:1.13646
[479]	train-mlogloss:1.10171	validation-mlogloss:1.13642
[480]	train-mlogloss:1.10164	validation-mlogloss:1.13638
[481]	train-mlogloss:1.10153	validation-mlogloss:1.13627
[482]	train-mlogloss:1.10133	validation-mlogloss:1.13616
[483]	train-mlogloss:1.10133	validation-mlogloss:1.13616
[484]	train-mlogloss:1.10125	validation-mlogloss:1.13603
[485]	train-mlogloss:1.10112	validation-mlogloss:1.13593
[486]	train-mlogloss:1.10104	validation-mlogloss:1.13587
[487]	train-mlogloss:1.10095	validation-mlogloss:1.13582
[488]	train-mlogloss:1.10078	validation-mlogloss:1.13568
[489]	train-mlogloss:1.10069	validation-mlogloss:1.13564
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.64 [%]
Global accuracy score (test) = 62.86 [%]
Global F1 score (train) = 66.46 [%]
Global F1 score (test) = 62.04 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.49      0.41      0.45       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.58      0.47       161
       CAMINAR USUAL SPEED       0.43      0.27      0.33       161
            CAMINAR ZIGZAG       0.74      0.58      0.65       161
          DE PIE BARRIENDO       0.72      0.62      0.67       161
   DE PIE DOBLANDO TOALLAS       0.44      0.55      0.49       161
    DE PIE MOVIENDO LIBROS       0.52      0.45      0.49       161
          DE PIE USANDO PC       0.80      0.93      0.86       161
        FASE REPOSO CON K5       0.77      0.92      0.84       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.61      0.40      0.49       161
         SENTADO USANDO PC       0.47      0.60      0.53       161
      SENTADO VIENDO LA TV       0.53      0.34      0.41       161
   SUBIR Y BAJAR ESCALERAS       0.62      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 25: 62.86 [%]
F1-score capturado en la ejecución 25: 62.04 [%]

=== EJECUCIÓN 26 ===

--- TRAIN (ejecución 26) ---

--- TEST (ejecución 26) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.47 [%]
Global accuracy score (test) = 62.65 [%]
Global F1 score (train) = 66.28 [%]
Global F1 score (test) = 61.79 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.48      0.44      0.46       161
 CAMINAR CON MÓVIL O LIBRO       0.40      0.53      0.46       161
       CAMINAR USUAL SPEED       0.39      0.25      0.31       161
            CAMINAR ZIGZAG       0.73      0.58      0.65       161
          DE PIE BARRIENDO       0.74      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.43      0.56      0.49       161
    DE PIE MOVIENDO LIBROS       0.53      0.46      0.49       161
          DE PIE USANDO PC       0.82      0.93      0.87       161
        FASE REPOSO CON K5       0.76      0.92      0.83       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.59      0.37      0.46       161
         SENTADO USANDO PC       0.47      0.62      0.54       161
      SENTADO VIENDO LA TV       0.50      0.32      0.39       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.63      0.63      0.62      2415
              weighted avg       0.63      0.63      0.62      2415


Accuracy capturado en la ejecución 26: 62.65 [%]
F1-score capturado en la ejecución 26: 61.79 [%]

=== EJECUCIÓN 27 ===

--- TRAIN (ejecución 27) ---

--- TEST (ejecución 27) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  6  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.66 [%]
Global accuracy score (test) = 63.23 [%]
Global F1 score (train) = 66.51 [%]
Global F1 score (test) = 62.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.51      0.45      0.48       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.53      0.45       161
       CAMINAR USUAL SPEED       0.41      0.28      0.33       161
            CAMINAR ZIGZAG       0.73      0.58      0.65       161
          DE PIE BARRIENDO       0.74      0.62      0.68       161
   DE PIE DOBLANDO TOALLAS       0.44      0.53      0.48       161
    DE PIE MOVIENDO LIBROS       0.51      0.47      0.49       161
          DE PIE USANDO PC       0.80      0.94      0.87       161
        FASE REPOSO CON K5       0.79      0.92      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.62      0.40      0.49       161
         SENTADO USANDO PC       0.49      0.61      0.54       161
      SENTADO VIENDO LA TV       0.52      0.37      0.43       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.63      2415
              weighted avg       0.64      0.63      0.63      2415


Accuracy capturado en la ejecución 27: 63.23 [%]
F1-score capturado en la ejecución 27: 62.56 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-10-26 07:33:42.865886: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:33:42.878356: 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 00:00:1761460422.891878 2624772 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460422.896243 2624772 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460422.907072 2624772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460422.907094 2624772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460422.907096 2624772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460422.907098 2624772 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:33:42.910500: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67506	validation-mlogloss:2.67663
[1]	train-mlogloss:2.64146	validation-mlogloss:2.64305
[2]	train-mlogloss:2.61285	validation-mlogloss:2.61600
[3]	train-mlogloss:2.58294	validation-mlogloss:2.58700
[4]	train-mlogloss:2.55509	validation-mlogloss:2.56051
[5]	train-mlogloss:2.53002	validation-mlogloss:2.53587
[6]	train-mlogloss:2.50457	validation-mlogloss:2.51091
[7]	train-mlogloss:2.47986	validation-mlogloss:2.48592
[8]	train-mlogloss:2.45573	validation-mlogloss:2.46230
[9]	train-mlogloss:2.43178	validation-mlogloss:2.43847
[10]	train-mlogloss:2.40956	validation-mlogloss:2.41736
[11]	train-mlogloss:2.38608	validation-mlogloss:2.39405
[12]	train-mlogloss:2.36450	validation-mlogloss:2.37317
[13]	train-mlogloss:2.34449	validation-mlogloss:2.35407
[14]	train-mlogloss:2.32355	validation-mlogloss:2.33305
[15]	train-mlogloss:2.30374	validation-mlogloss:2.31364
[16]	train-mlogloss:2.28450	validation-mlogloss:2.29416
[17]	train-mlogloss:2.26556	validation-mlogloss:2.27563
[18]	train-mlogloss:2.24726	validation-mlogloss:2.25756
[19]	train-mlogloss:2.22918	validation-mlogloss:2.24004
[20]	train-mlogloss:2.21181	validation-mlogloss:2.22307
[21]	train-mlogloss:2.19501	validation-mlogloss:2.20632
[22]	train-mlogloss:2.17866	validation-mlogloss:2.18997
[23]	train-mlogloss:2.16279	validation-mlogloss:2.17356
[24]	train-mlogloss:2.14716	validation-mlogloss:2.15824
[25]	train-mlogloss:2.13155	validation-mlogloss:2.14265
[26]	train-mlogloss:2.11630	validation-mlogloss:2.12798
[27]	train-mlogloss:2.10183	validation-mlogloss:2.11385
[28]	train-mlogloss:2.08823	validation-mlogloss:2.10076
[29]	train-mlogloss:2.07456	validation-mlogloss:2.08718
[30]	train-mlogloss:2.06039	validation-mlogloss:2.07254
[31]	train-mlogloss:2.04707	validation-mlogloss:2.05975
[32]	train-mlogloss:2.03365	validation-mlogloss:2.04625
[33]	train-mlogloss:2.02090	validation-mlogloss:2.03322
[34]	train-mlogloss:2.00888	validation-mlogloss:2.02164
[35]	train-mlogloss:1.99709	validation-mlogloss:2.00979
[36]	train-mlogloss:1.98544	validation-mlogloss:1.99815
[37]	train-mlogloss:1.97395	validation-mlogloss:1.98621
[38]	train-mlogloss:1.96217	validation-mlogloss:1.97456
[39]	train-mlogloss:1.95142	validation-mlogloss:1.96411
[40]	train-mlogloss:1.94014	validation-mlogloss:1.95284
[41]	train-mlogloss:1.92916	validation-mlogloss:1.94173
[42]	train-mlogloss:1.91850	validation-mlogloss:1.93115
[43]	train-mlogloss:1.90790	validation-mlogloss:1.92065
[44]	train-mlogloss:1.89756	validation-mlogloss:1.91031
[45]	train-mlogloss:1.88762	validation-mlogloss:1.90005
[46]	train-mlogloss:1.87749	validation-mlogloss:1.88959
[47]	train-mlogloss:1.86754	validation-mlogloss:1.87978
[48]	train-mlogloss:1.85801	validation-mlogloss:1.87021
[49]	train-mlogloss:1.84863	validation-mlogloss:1.86099
[50]	train-mlogloss:1.83948	validation-mlogloss:1.85224
[51]	train-mlogloss:1.83028	validation-mlogloss:1.84280
[52]	train-mlogloss:1.82121	validation-mlogloss:1.83371
[53]	train-mlogloss:1.81221	validation-mlogloss:1.82471
[54]	train-mlogloss:1.80421	validation-mlogloss:1.81667
[55]	train-mlogloss:1.79588	validation-mlogloss:1.80829
[56]	train-mlogloss:1.78749	validation-mlogloss:1.79998
[57]	train-mlogloss:1.77933	validation-mlogloss:1.79188
[58]	train-mlogloss:1.77120	validation-mlogloss:1.78379
[59]	train-mlogloss:1.76315	validation-mlogloss:1.77549
[60]	train-mlogloss:1.75554	validation-mlogloss:1.76764
[61]	train-mlogloss:1.74776	validation-mlogloss:1.75945
[62]	train-mlogloss:1.74003	validation-mlogloss:1.75169
[63]	train-mlogloss:1.73260	validation-mlogloss:1.74445
[64]	train-mlogloss:1.72509	validation-mlogloss:1.73697
[65]	train-mlogloss:1.71755	validation-mlogloss:1.72900
[66]	train-mlogloss:1.71045	validation-mlogloss:1.72219
[67]	train-mlogloss:1.70364	validation-mlogloss:1.71539
[68]	train-mlogloss:1.69685	validation-mlogloss:1.70857
[69]	train-mlogloss:1.69064	validation-mlogloss:1.70236
[70]	train-mlogloss:1.68398	validation-mlogloss:1.69561
[71]	train-mlogloss:1.67751	validation-mlogloss:1.68934
[72]	train-mlogloss:1.67076	validation-mlogloss:1.68257
[73]	train-mlogloss:1.66417	validation-mlogloss:1.67599
[74]	train-mlogloss:1.65782	validation-mlogloss:1.66963
[75]	train-mlogloss:1.65171	validation-mlogloss:1.66361
[76]	train-mlogloss:1.64566	validation-mlogloss:1.65764
[77]	train-mlogloss:1.63973	validation-mlogloss:1.65178
[78]	train-mlogloss:1.63362	validation-mlogloss:1.64556
[79]	train-mlogloss:1.62745	validation-mlogloss:1.63964
[80]	train-mlogloss:1.62184	validation-mlogloss:1.63375
[81]	train-mlogloss:1.61613	validation-mlogloss:1.62794
[82]	train-mlogloss:1.61014	validation-mlogloss:1.62208
[83]	train-mlogloss:1.60474	validation-mlogloss:1.61691
[84]	train-mlogloss:1.59936	validation-mlogloss:1.61138
[85]	train-mlogloss:1.59425	validation-mlogloss:1.60626
[86]	train-mlogloss:1.58911	validation-mlogloss:1.60109
[87]	train-mlogloss:1.58394	validation-mlogloss:1.59579
[88]	train-mlogloss:1.57883	validation-mlogloss:1.59062
[89]	train-mlogloss:1.57384	validation-mlogloss:1.58565
[90]	train-mlogloss:1.56855	validation-mlogloss:1.58019
[91]	train-mlogloss:1.56383	validation-mlogloss:1.57551
[92]	train-mlogloss:1.55920	validation-mlogloss:1.57087
[93]	train-mlogloss:1.55458	validation-mlogloss:1.56607
[94]	train-mlogloss:1.54957	validation-mlogloss:1.56113
[95]	train-mlogloss:1.54496	validation-mlogloss:1.55648
[96]	train-mlogloss:1.54027	validation-mlogloss:1.55181
[97]	train-mlogloss:1.53546	validation-mlogloss:1.54685
[98]	train-mlogloss:1.53085	validation-mlogloss:1.54203
[99]	train-mlogloss:1.52649	validation-mlogloss:1.53759
[100]	train-mlogloss:1.52200	validation-mlogloss:1.53302
[101]	train-mlogloss:1.51752	validation-mlogloss:1.52834
[102]	train-mlogloss:1.51314	validation-mlogloss:1.52390
[103]	train-mlogloss:1.50895	validation-mlogloss:1.51953
[104]	train-mlogloss:1.50485	validation-mlogloss:1.51531
[105]	train-mlogloss:1.50048	validation-mlogloss:1.51077
[106]	train-mlogloss:1.49635	validation-mlogloss:1.50655
[107]	train-mlogloss:1.49246	validation-mlogloss:1.50267
[108]	train-mlogloss:1.48850	validation-mlogloss:1.49866
[109]	train-mlogloss:1.48463	validation-mlogloss:1.49488
[110]	train-mlogloss:1.48061	validation-mlogloss:1.49075
[111]	train-mlogloss:1.47674	validation-mlogloss:1.48666
[112]	train-mlogloss:1.47283	validation-mlogloss:1.48284
[113]	train-mlogloss:1.46908	validation-mlogloss:1.47916
[114]	train-mlogloss:1.46528	validation-mlogloss:1.47524
[115]	train-mlogloss:1.46161	validation-mlogloss:1.47139
[116]	train-mlogloss:1.45778	validation-mlogloss:1.46738
[117]	train-mlogloss:1.45411	validation-mlogloss:1.46355
[118]	train-mlogloss:1.45047	validation-mlogloss:1.45985
[119]	train-mlogloss:1.44686	validation-mlogloss:1.45615
[120]	train-mlogloss:1.44334	validation-mlogloss:1.45258
[121]	train-mlogloss:1.44022	validation-mlogloss:1.44928
[122]	train-mlogloss:1.43680	validation-mlogloss:1.44580
[123]	train-mlogloss:1.43333	validation-mlogloss:1.44226
[124]	train-mlogloss:1.42988	validation-mlogloss:1.43884
[125]	train-mlogloss:1.42671	validation-mlogloss:1.43579
[126]	train-mlogloss:1.42363	validation-mlogloss:1.43274
[127]	train-mlogloss:1.42064	validation-mlogloss:1.42971
[128]	train-mlogloss:1.41754	validation-mlogloss:1.42664
[129]	train-mlogloss:1.41433	validation-mlogloss:1.42348
[130]	train-mlogloss:1.41140	validation-mlogloss:1.42052
[131]	train-mlogloss:1.40841	validation-mlogloss:1.41761
[132]	train-mlogloss:1.40524	validation-mlogloss:1.41431
[133]	train-mlogloss:1.40215	validation-mlogloss:1.41102
[134]	train-mlogloss:1.39932	validation-mlogloss:1.40815
[135]	train-mlogloss:1.39645	validation-mlogloss:1.40539
[136]	train-mlogloss:1.39362	validation-mlogloss:1.40255
[137]	train-mlogloss:1.39069	validation-mlogloss:1.39964
[138]	train-mlogloss:1.38801	validation-mlogloss:1.39689
[139]	train-mlogloss:1.38507	validation-mlogloss:1.39395
[140]	train-mlogloss:1.38214	validation-mlogloss:1.39097
[141]	train-mlogloss:1.37951	validation-mlogloss:1.38840
[142]	train-mlogloss:1.37686	validation-mlogloss:1.38584
[143]	train-mlogloss:1.37394	validation-mlogloss:1.38307
[144]	train-mlogloss:1.37121	validation-mlogloss:1.38036
[145]	train-mlogloss:1.36869	validation-mlogloss:1.37788
[146]	train-mlogloss:1.36611	validation-mlogloss:1.37519
[147]	train-mlogloss:1.36379	validation-mlogloss:1.37289
[148]	train-mlogloss:1.36102	validation-mlogloss:1.37020
[149]	train-mlogloss:1.35831	validation-mlogloss:1.36756
[150]	train-mlogloss:1.35593	validation-mlogloss:1.36522
[151]	train-mlogloss:1.35356	validation-mlogloss:1.36291
[152]	train-mlogloss:1.35111	validation-mlogloss:1.36045
[153]	train-mlogloss:1.34872	validation-mlogloss:1.35815
[154]	train-mlogloss:1.34646	validation-mlogloss:1.35598
[155]	train-mlogloss:1.34421	validation-mlogloss:1.35371
[156]	train-mlogloss:1.34195	validation-mlogloss:1.35138
[157]	train-mlogloss:1.33963	validation-mlogloss:1.34904
[158]	train-mlogloss:1.33740	validation-mlogloss:1.34693
[159]	train-mlogloss:1.33502	validation-mlogloss:1.34454
[160]	train-mlogloss:1.33268	validation-mlogloss:1.34238
[161]	train-mlogloss:1.33040	validation-mlogloss:1.34021
[162]	train-mlogloss:1.32816	validation-mlogloss:1.33808
[163]	train-mlogloss:1.32588	validation-mlogloss:1.33585
[164]	train-mlogloss:1.32386	validation-mlogloss:1.33404
[165]	train-mlogloss:1.32182	validation-mlogloss:1.33212
[166]	train-mlogloss:1.31975	validation-mlogloss:1.33009
[167]	train-mlogloss:1.31762	validation-mlogloss:1.32798
[168]	train-mlogloss:1.31542	validation-mlogloss:1.32576
[169]	train-mlogloss:1.31342	validation-mlogloss:1.32372
[170]	train-mlogloss:1.31142	validation-mlogloss:1.32176
[171]	train-mlogloss:1.30949	validation-mlogloss:1.31989
[172]	train-mlogloss:1.30758	validation-mlogloss:1.31799
[173]	train-mlogloss:1.30555	validation-mlogloss:1.31601
[174]	train-mlogloss:1.30351	validation-mlogloss:1.31410
[175]	train-mlogloss:1.30153	validation-mlogloss:1.31204
[176]	train-mlogloss:1.29954	validation-mlogloss:1.31008
[177]	train-mlogloss:1.29762	validation-mlogloss:1.30834
[178]	train-mlogloss:1.29564	validation-mlogloss:1.30650
[179]	train-mlogloss:1.29369	validation-mlogloss:1.30459
[180]	train-mlogloss:1.29194	validation-mlogloss:1.30302
[181]	train-mlogloss:1.29012	validation-mlogloss:1.30128
[182]	train-mlogloss:1.28826	validation-mlogloss:1.29946
[183]	train-mlogloss:1.28641	validation-mlogloss:1.29775
[184]	train-mlogloss:1.28458	validation-mlogloss:1.29594
[185]	train-mlogloss:1.28281	validation-mlogloss:1.29422
[186]	train-mlogloss:1.28105	validation-mlogloss:1.29254
[187]	train-mlogloss:1.27937	validation-mlogloss:1.29078
[188]	train-mlogloss:1.27761	validation-mlogloss:1.28926
[189]	train-mlogloss:1.27597	validation-mlogloss:1.28764
[190]	train-mlogloss:1.27431	validation-mlogloss:1.28618
[191]	train-mlogloss:1.27264	validation-mlogloss:1.28465
[192]	train-mlogloss:1.27103	validation-mlogloss:1.28323
[193]	train-mlogloss:1.26940	validation-mlogloss:1.28179
[194]	train-mlogloss:1.26780	validation-mlogloss:1.28029
[195]	train-mlogloss:1.26616	validation-mlogloss:1.27857
[196]	train-mlogloss:1.26449	validation-mlogloss:1.27703
[197]	train-mlogloss:1.26280	validation-mlogloss:1.27535
[198]	train-mlogloss:1.26127	validation-mlogloss:1.27389
[199]	train-mlogloss:1.25973	validation-mlogloss:1.27242
[200]	train-mlogloss:1.25816	validation-mlogloss:1.27093
[201]	train-mlogloss:1.25652	validation-mlogloss:1.26936
[202]	train-mlogloss:1.25501	validation-mlogloss:1.26788
[203]	train-mlogloss:1.25355	validation-mlogloss:1.26645
[204]	train-mlogloss:1.25200	validation-mlogloss:1.26489
[205]	train-mlogloss:1.25057	validation-mlogloss:1.26357
[206]	train-mlogloss:1.24925	validation-mlogloss:1.26222
[207]	train-mlogloss:1.24780	validation-mlogloss:1.26074
[208]	train-mlogloss:1.24642	validation-mlogloss:1.25939
[209]	train-mlogloss:1.24503	validation-mlogloss:1.25806
[210]	train-mlogloss:1.24358	validation-mlogloss:1.25669
[211]	train-mlogloss:1.24218	validation-mlogloss:1.25562
[212]	train-mlogloss:1.24075	validation-mlogloss:1.25436
[213]	train-mlogloss:1.23946	validation-mlogloss:1.25310
[214]	train-mlogloss:1.23818	validation-mlogloss:1.25198
[215]	train-mlogloss:1.23687	validation-mlogloss:1.25090
[216]	train-mlogloss:1.23547	validation-mlogloss:1.24958
[217]	train-mlogloss:1.23428	validation-mlogloss:1.24842
[218]	train-mlogloss:1.23296	validation-mlogloss:1.24718
[219]	train-mlogloss:1.23154	validation-mlogloss:1.24585
[220]	train-mlogloss:1.23022	validation-mlogloss:1.24457
[221]	train-mlogloss:1.22896	validation-mlogloss:1.24331
[222]	train-mlogloss:1.22754	validation-mlogloss:1.24204
[223]	train-mlogloss:1.22629	validation-mlogloss:1.24084
[224]	train-mlogloss:1.22497	validation-mlogloss:1.23973
[225]	train-mlogloss:1.22369	validation-mlogloss:1.23841
[226]	train-mlogloss:1.22252	validation-mlogloss:1.23740
[227]	train-mlogloss:1.22138	validation-mlogloss:1.23636
[228]	train-mlogloss:1.22009	validation-mlogloss:1.23515
[229]	train-mlogloss:1.21877	validation-mlogloss:1.23405
[230]	train-mlogloss:1.21755	validation-mlogloss:1.23298
[231]	train-mlogloss:1.21625	validation-mlogloss:1.23175
[232]	train-mlogloss:1.21504	validation-mlogloss:1.23062
[233]	train-mlogloss:1.21394	validation-mlogloss:1.22966
[234]	train-mlogloss:1.21278	validation-mlogloss:1.22858
[235]	train-mlogloss:1.21159	validation-mlogloss:1.22753
[236]	train-mlogloss:1.21055	validation-mlogloss:1.22670
[237]	train-mlogloss:1.20933	validation-mlogloss:1.22564
[238]	train-mlogloss:1.20831	validation-mlogloss:1.22478
[239]	train-mlogloss:1.20718	validation-mlogloss:1.22376
[240]	train-mlogloss:1.20604	validation-mlogloss:1.22276
[241]	train-mlogloss:1.20494	validation-mlogloss:1.22177
[242]	train-mlogloss:1.20380	validation-mlogloss:1.22084
[243]	train-mlogloss:1.20260	validation-mlogloss:1.21986
[244]	train-mlogloss:1.20146	validation-mlogloss:1.21883
[245]	train-mlogloss:1.20045	validation-mlogloss:1.21784
[246]	train-mlogloss:1.19937	validation-mlogloss:1.21687
[247]	train-mlogloss:1.19824	validation-mlogloss:1.21579
[248]	train-mlogloss:1.19732	validation-mlogloss:1.21497
[249]	train-mlogloss:1.19630	validation-mlogloss:1.21405
[250]	train-mlogloss:1.19523	validation-mlogloss:1.21301
[251]	train-mlogloss:1.19428	validation-mlogloss:1.21219
[252]	train-mlogloss:1.19324	validation-mlogloss:1.21124
[253]	train-mlogloss:1.19233	validation-mlogloss:1.21039
[254]	train-mlogloss:1.19134	validation-mlogloss:1.20951
[255]	train-mlogloss:1.19036	validation-mlogloss:1.20864
[256]	train-mlogloss:1.18934	validation-mlogloss:1.20773
[257]	train-mlogloss:1.18831	validation-mlogloss:1.20688
[258]	train-mlogloss:1.18727	validation-mlogloss:1.20600
[259]	train-mlogloss:1.18633	validation-mlogloss:1.20517
[260]	train-mlogloss:1.18536	validation-mlogloss:1.20437
[261]	train-mlogloss:1.18439	validation-mlogloss:1.20358
[262]	train-mlogloss:1.18342	validation-mlogloss:1.20266
[263]	train-mlogloss:1.18249	validation-mlogloss:1.20181
[264]	train-mlogloss:1.18158	validation-mlogloss:1.20105
[265]	train-mlogloss:1.18065	validation-mlogloss:1.20017
[266]	train-mlogloss:1.17962	validation-mlogloss:1.19932
[267]	train-mlogloss:1.17867	validation-mlogloss:1.19841
[268]	train-mlogloss:1.17788	validation-mlogloss:1.19771
[269]	train-mlogloss:1.17717	validation-mlogloss:1.19714
[270]	train-mlogloss:1.17636	validation-mlogloss:1.19646
[271]	train-mlogloss:1.17554	validation-mlogloss:1.19570
[272]	train-mlogloss:1.17466	validation-mlogloss:1.19487
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[337]	train-mlogloss:1.13470	validation-mlogloss:1.16163
[338]	train-mlogloss:1.13424	validation-mlogloss:1.16132
[339]	train-mlogloss:1.13374	validation-mlogloss:1.16101
[340]	train-mlogloss:1.13338	validation-mlogloss:1.16071
[341]	train-mlogloss:1.13302	validation-mlogloss:1.16039
[342]	train-mlogloss:1.13262	validation-mlogloss:1.16011
[343]	train-mlogloss:1.13214	validation-mlogloss:1.15969
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[346]	train-mlogloss:1.13095	validation-mlogloss:1.15876
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[353]	train-mlogloss:1.12815	validation-mlogloss:1.15642
[354]	train-mlogloss:1.12769	validation-mlogloss:1.15601
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[357]	train-mlogloss:1.12652	validation-mlogloss:1.15514
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[414]	train-mlogloss:1.11111	validation-mlogloss:1.14396
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[434]	train-mlogloss:1.10753	validation-mlogloss:1.14095
[435]	train-mlogloss:1.10730	validation-mlogloss:1.14085
[436]	train-mlogloss:1.10705	validation-mlogloss:1.14065
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[438]	train-mlogloss:1.10677	validation-mlogloss:1.14043
[439]	train-mlogloss:1.10660	validation-mlogloss:1.14032
[440]	train-mlogloss:1.10636	validation-mlogloss:1.14017
[441]	train-mlogloss:1.10622	validation-mlogloss:1.14005
[442]	train-mlogloss:1.10612	validation-mlogloss:1.14001
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[444]	train-mlogloss:1.10593	validation-mlogloss:1.13996
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[448]	train-mlogloss:1.10554	validation-mlogloss:1.13974
[449]	train-mlogloss:1.10544	validation-mlogloss:1.13961
[450]	train-mlogloss:1.10540	validation-mlogloss:1.13957
[451]	train-mlogloss:1.10520	validation-mlogloss:1.13947
[452]	train-mlogloss:1.10510	validation-mlogloss:1.13935
[453]	train-mlogloss:1.10496	validation-mlogloss:1.13929
[454]	train-mlogloss:1.10477	validation-mlogloss:1.13924
[455]	train-mlogloss:1.10464	validation-mlogloss:1.13911
[456]	train-mlogloss:1.10460	validation-mlogloss:1.13909
[457]	train-mlogloss:1.10447	validation-mlogloss:1.13897
[458]	train-mlogloss:1.10426	validation-mlogloss:1.13872
[459]	train-mlogloss:1.10408	validation-mlogloss:1.13859
[460]	train-mlogloss:1.10395	validation-mlogloss:1.13846
[461]	train-mlogloss:1.10377	validation-mlogloss:1.13836
[462]	train-mlogloss:1.10368	validation-mlogloss:1.13828
[463]	train-mlogloss:1.10351	validation-mlogloss:1.13822
[464]	train-mlogloss:1.10331	validation-mlogloss:1.13804
[465]	train-mlogloss:1.10299	validation-mlogloss:1.13782
[466]	train-mlogloss:1.10275	validation-mlogloss:1.13762
[467]	train-mlogloss:1.10259	validation-mlogloss:1.13754
[468]	train-mlogloss:1.10249	validation-mlogloss:1.13747
[469]	train-mlogloss:1.10238	validation-mlogloss:1.13741
[470]	train-mlogloss:1.10223	validation-mlogloss:1.13728
[471]	train-mlogloss:1.10206	validation-mlogloss:1.13714
[472]	train-mlogloss:1.10189	validation-mlogloss:1.13694
[473]	train-mlogloss:1.10177	validation-mlogloss:1.13688
[474]	train-mlogloss:1.10161	validation-mlogloss:1.13680
[475]	train-mlogloss:1.10151	validation-mlogloss:1.13675
[476]	train-mlogloss:1.10131	validation-mlogloss:1.13658
[477]	train-mlogloss:1.10113	validation-mlogloss:1.13644
[478]	train-mlogloss:1.10101	validation-mlogloss:1.13630
[479]	train-mlogloss:1.10093	validation-mlogloss:1.13620
[480]	train-mlogloss:1.10077	validation-mlogloss:1.13606
[481]	train-mlogloss:1.10067	validation-mlogloss:1.13603
[482]	train-mlogloss:1.10054	validation-mlogloss:1.13597
[483]	train-mlogloss:1.10049	validation-mlogloss:1.13591
[484]	train-mlogloss:1.10026	validation-mlogloss:1.13574
[485]	train-mlogloss:1.10004	validation-mlogloss:1.13561
[486]	train-mlogloss:1.09993	validation-mlogloss:1.13552
[487]	train-mlogloss:1.09987	validation-mlogloss:1.13552
[488]	train-mlogloss:1.09973	validation-mlogloss:1.13540
[489]	train-mlogloss:1.09959	validation-mlogloss:1.13536
Saved model to disk.
2025-10-26 07:34:21.520796: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-10-26 07:34:21.533591: 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 00:00:1761460461.547134 2625561 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1761460461.551388 2625561 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1761460461.562208 2625561 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460461.562227 2625561 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460461.562229 2625561 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1761460461.562231 2625561 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-10-26 07:34:21.565651: 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.
1 GPU(s) detected and VRAM set to crossover mode..
Training a non-convolutional model.
[0]	train-mlogloss:2.67456	validation-mlogloss:2.67444
[1]	train-mlogloss:2.64067	validation-mlogloss:2.64137
[2]	train-mlogloss:2.61061	validation-mlogloss:2.61237
[3]	train-mlogloss:2.58316	validation-mlogloss:2.58577
[4]	train-mlogloss:2.55459	validation-mlogloss:2.55841
[5]	train-mlogloss:2.52861	validation-mlogloss:2.53350
[6]	train-mlogloss:2.50143	validation-mlogloss:2.50753
[7]	train-mlogloss:2.47607	validation-mlogloss:2.48362
[8]	train-mlogloss:2.45290	validation-mlogloss:2.46089
[9]	train-mlogloss:2.42922	validation-mlogloss:2.43741
[10]	train-mlogloss:2.40603	validation-mlogloss:2.41405
[11]	train-mlogloss:2.38472	validation-mlogloss:2.39327
[12]	train-mlogloss:2.36206	validation-mlogloss:2.37191
[13]	train-mlogloss:2.34099	validation-mlogloss:2.35160
[14]	train-mlogloss:2.32119	validation-mlogloss:2.33226
[15]	train-mlogloss:2.30192	validation-mlogloss:2.31399
[16]	train-mlogloss:2.28196	validation-mlogloss:2.29396
[17]	train-mlogloss:2.26393	validation-mlogloss:2.27634
[18]	train-mlogloss:2.24545	validation-mlogloss:2.25795
[19]	train-mlogloss:2.22768	validation-mlogloss:2.24035
[20]	train-mlogloss:2.20985	validation-mlogloss:2.22193
[21]	train-mlogloss:2.19342	validation-mlogloss:2.20515
[22]	train-mlogloss:2.17650	validation-mlogloss:2.18823
[23]	train-mlogloss:2.15968	validation-mlogloss:2.17123
[24]	train-mlogloss:2.14432	validation-mlogloss:2.15618
[25]	train-mlogloss:2.12893	validation-mlogloss:2.14112
[26]	train-mlogloss:2.11397	validation-mlogloss:2.12639
[27]	train-mlogloss:2.09975	validation-mlogloss:2.11239
[28]	train-mlogloss:2.08523	validation-mlogloss:2.09865
[29]	train-mlogloss:2.07133	validation-mlogloss:2.08519
[30]	train-mlogloss:2.05734	validation-mlogloss:2.07071
[31]	train-mlogloss:2.04417	validation-mlogloss:2.05759
[32]	train-mlogloss:2.03108	validation-mlogloss:2.04431
[33]	train-mlogloss:2.01776	validation-mlogloss:2.03126
[34]	train-mlogloss:2.00531	validation-mlogloss:2.01864
[35]	train-mlogloss:1.99297	validation-mlogloss:2.00614
[36]	train-mlogloss:1.98161	validation-mlogloss:1.99490
[37]	train-mlogloss:1.96957	validation-mlogloss:1.98291
[38]	train-mlogloss:1.95738	validation-mlogloss:1.97089
[39]	train-mlogloss:1.94601	validation-mlogloss:1.95936
[40]	train-mlogloss:1.93465	validation-mlogloss:1.94811
[41]	train-mlogloss:1.92394	validation-mlogloss:1.93693
[42]	train-mlogloss:1.91388	validation-mlogloss:1.92682
[43]	train-mlogloss:1.90355	validation-mlogloss:1.91687
[44]	train-mlogloss:1.89313	validation-mlogloss:1.90631
[45]	train-mlogloss:1.88289	validation-mlogloss:1.89534
[46]	train-mlogloss:1.87303	validation-mlogloss:1.88544
[47]	train-mlogloss:1.86354	validation-mlogloss:1.87574
[48]	train-mlogloss:1.85417	validation-mlogloss:1.86661
[49]	train-mlogloss:1.84459	validation-mlogloss:1.85705
[50]	train-mlogloss:1.83541	validation-mlogloss:1.84788
[51]	train-mlogloss:1.82623	validation-mlogloss:1.83864
[52]	train-mlogloss:1.81732	validation-mlogloss:1.82951
[53]	train-mlogloss:1.80822	validation-mlogloss:1.82083
[54]	train-mlogloss:1.79939	validation-mlogloss:1.81155
[55]	train-mlogloss:1.79078	validation-mlogloss:1.80295
[56]	train-mlogloss:1.78222	validation-mlogloss:1.79403
[57]	train-mlogloss:1.77418	validation-mlogloss:1.78606
[58]	train-mlogloss:1.76596	validation-mlogloss:1.77738
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[362]	train-mlogloss:1.12567	validation-mlogloss:1.15438
[363]	train-mlogloss:1.12526	validation-mlogloss:1.15411
[364]	train-mlogloss:1.12493	validation-mlogloss:1.15388
[365]	train-mlogloss:1.12467	validation-mlogloss:1.15363
[366]	train-mlogloss:1.12434	validation-mlogloss:1.15340
[367]	train-mlogloss:1.12402	validation-mlogloss:1.15319
[368]	train-mlogloss:1.12362	validation-mlogloss:1.15293
[369]	train-mlogloss:1.12326	validation-mlogloss:1.15272
[370]	train-mlogloss:1.12286	validation-mlogloss:1.15246
[371]	train-mlogloss:1.12254	validation-mlogloss:1.15223
[372]	train-mlogloss:1.12225	validation-mlogloss:1.15203
[373]	train-mlogloss:1.12193	validation-mlogloss:1.15176
[374]	train-mlogloss:1.12163	validation-mlogloss:1.15150
[375]	train-mlogloss:1.12133	validation-mlogloss:1.15123
[376]	train-mlogloss:1.12091	validation-mlogloss:1.15091
[377]	train-mlogloss:1.12069	validation-mlogloss:1.15068
[378]	train-mlogloss:1.12041	validation-mlogloss:1.15038
[379]	train-mlogloss:1.12012	validation-mlogloss:1.15016
[380]	train-mlogloss:1.11985	validation-mlogloss:1.15000
[381]	train-mlogloss:1.11951	validation-mlogloss:1.14978
[382]	train-mlogloss:1.11912	validation-mlogloss:1.14952
[383]	train-mlogloss:1.11888	validation-mlogloss:1.14938
[384]	train-mlogloss:1.11869	validation-mlogloss:1.14924
[385]	train-mlogloss:1.11837	validation-mlogloss:1.14898
[386]	train-mlogloss:1.11802	validation-mlogloss:1.14869
[387]	train-mlogloss:1.11772	validation-mlogloss:1.14845
[388]	train-mlogloss:1.11748	validation-mlogloss:1.14823
[389]	train-mlogloss:1.11722	validation-mlogloss:1.14812
[390]	train-mlogloss:1.11700	validation-mlogloss:1.14793
[391]	train-mlogloss:1.11670	validation-mlogloss:1.14775
[392]	train-mlogloss:1.11640	validation-mlogloss:1.14755
[393]	train-mlogloss:1.11622	validation-mlogloss:1.14738
[394]	train-mlogloss:1.11600	validation-mlogloss:1.14713
[395]	train-mlogloss:1.11573	validation-mlogloss:1.14694
[396]	train-mlogloss:1.11561	validation-mlogloss:1.14681
[397]	train-mlogloss:1.11535	validation-mlogloss:1.14663
[398]	train-mlogloss:1.11520	validation-mlogloss:1.14651
[399]	train-mlogloss:1.11496	validation-mlogloss:1.14630
[400]	train-mlogloss:1.11464	validation-mlogloss:1.14598
[401]	train-mlogloss:1.11444	validation-mlogloss:1.14584
[402]	train-mlogloss:1.11433	validation-mlogloss:1.14573
[403]	train-mlogloss:1.11399	validation-mlogloss:1.14550
[404]	train-mlogloss:1.11372	validation-mlogloss:1.14526
[405]	train-mlogloss:1.11354	validation-mlogloss:1.14512
[406]	train-mlogloss:1.11336	validation-mlogloss:1.14501
[407]	train-mlogloss:1.11316	validation-mlogloss:1.14493
[408]	train-mlogloss:1.11286	validation-mlogloss:1.14475
[409]	train-mlogloss:1.11259	validation-mlogloss:1.14448
[410]	train-mlogloss:1.11225	validation-mlogloss:1.14426
[411]	train-mlogloss:1.11213	validation-mlogloss:1.14422
[412]	train-mlogloss:1.11180	validation-mlogloss:1.14396
[413]	train-mlogloss:1.11173	validation-mlogloss:1.14391
[414]	train-mlogloss:1.11162	validation-mlogloss:1.14382
[415]	train-mlogloss:1.11147	validation-mlogloss:1.14380
[416]	train-mlogloss:1.11125	validation-mlogloss:1.14364
[417]	train-mlogloss:1.11100	validation-mlogloss:1.14353
[418]	train-mlogloss:1.11075	validation-mlogloss:1.14336
[419]	train-mlogloss:1.11049	validation-mlogloss:1.14312
[420]	train-mlogloss:1.11021	validation-mlogloss:1.14291
[421]	train-mlogloss:1.11000	validation-mlogloss:1.14276
[422]	train-mlogloss:1.10984	validation-mlogloss:1.14266
[423]	train-mlogloss:1.10967	validation-mlogloss:1.14255
[424]	train-mlogloss:1.10950	validation-mlogloss:1.14244
[425]	train-mlogloss:1.10942	validation-mlogloss:1.14237
[426]	train-mlogloss:1.10915	validation-mlogloss:1.14216
[427]	train-mlogloss:1.10901	validation-mlogloss:1.14205
[428]	train-mlogloss:1.10884	validation-mlogloss:1.14193
[429]	train-mlogloss:1.10868	validation-mlogloss:1.14178
[430]	train-mlogloss:1.10841	validation-mlogloss:1.14163
[431]	train-mlogloss:1.10818	validation-mlogloss:1.14143
[432]	train-mlogloss:1.10803	validation-mlogloss:1.14131
[433]	train-mlogloss:1.10798	validation-mlogloss:1.14127
[434]	train-mlogloss:1.10772	validation-mlogloss:1.14104
[435]	train-mlogloss:1.10755	validation-mlogloss:1.14090
[436]	train-mlogloss:1.10734	validation-mlogloss:1.14071
[437]	train-mlogloss:1.10732	validation-mlogloss:1.14072
[438]	train-mlogloss:1.10725	validation-mlogloss:1.14065
[439]	train-mlogloss:1.10697	validation-mlogloss:1.14043
[440]	train-mlogloss:1.10693	validation-mlogloss:1.14040
[441]	train-mlogloss:1.10670	validation-mlogloss:1.14026
[442]	train-mlogloss:1.10651	validation-mlogloss:1.14014
[443]	train-mlogloss:1.10633	validation-mlogloss:1.14001
[444]	train-mlogloss:1.10620	validation-mlogloss:1.13994
[445]	train-mlogloss:1.10612	validation-mlogloss:1.13991
[446]	train-mlogloss:1.10601	validation-mlogloss:1.13980
[447]	train-mlogloss:1.10582	validation-mlogloss:1.13969
[448]	train-mlogloss:1.10572	validation-mlogloss:1.13963
[449]	train-mlogloss:1.10560	validation-mlogloss:1.13954
[450]	train-mlogloss:1.10549	validation-mlogloss:1.13938
[451]	train-mlogloss:1.10536	validation-mlogloss:1.13928
[452]	train-mlogloss:1.10511	validation-mlogloss:1.13906
[453]	train-mlogloss:1.10491	validation-mlogloss:1.13899
[454]	train-mlogloss:1.10480	validation-mlogloss:1.13892
[455]	train-mlogloss:1.10458	validation-mlogloss:1.13868
[456]	train-mlogloss:1.10445	validation-mlogloss:1.13866
[457]	train-mlogloss:1.10427	validation-mlogloss:1.13851
[458]	train-mlogloss:1.10411	validation-mlogloss:1.13838
[459]	train-mlogloss:1.10394	validation-mlogloss:1.13830
[460]	train-mlogloss:1.10380	validation-mlogloss:1.13817
[461]	train-mlogloss:1.10372	validation-mlogloss:1.13807
[462]	train-mlogloss:1.10355	validation-mlogloss:1.13797
[463]	train-mlogloss:1.10346	validation-mlogloss:1.13794
[464]	train-mlogloss:1.10330	validation-mlogloss:1.13787
[465]	train-mlogloss:1.10325	validation-mlogloss:1.13786
[466]	train-mlogloss:1.10312	validation-mlogloss:1.13780
[467]	train-mlogloss:1.10299	validation-mlogloss:1.13773
[468]	train-mlogloss:1.10284	validation-mlogloss:1.13764
[469]	train-mlogloss:1.10268	validation-mlogloss:1.13752
[470]	train-mlogloss:1.10250	validation-mlogloss:1.13739
[471]	train-mlogloss:1.10245	validation-mlogloss:1.13731
[472]	train-mlogloss:1.10234	validation-mlogloss:1.13726
[473]	train-mlogloss:1.10217	validation-mlogloss:1.13714
[474]	train-mlogloss:1.10198	validation-mlogloss:1.13700
[475]	train-mlogloss:1.10180	validation-mlogloss:1.13689
[476]	train-mlogloss:1.10168	validation-mlogloss:1.13682
[477]	train-mlogloss:1.10157	validation-mlogloss:1.13675
[478]	train-mlogloss:1.10151	validation-mlogloss:1.13676
[479]	train-mlogloss:1.10130	validation-mlogloss:1.13664
[480]	train-mlogloss:1.10124	validation-mlogloss:1.13663
[481]	train-mlogloss:1.10114	validation-mlogloss:1.13659
[482]	train-mlogloss:1.10098	validation-mlogloss:1.13651
[483]	train-mlogloss:1.10076	validation-mlogloss:1.13638
[484]	train-mlogloss:1.10058	validation-mlogloss:1.13615
[485]	train-mlogloss:1.10052	validation-mlogloss:1.13613
[486]	train-mlogloss:1.10047	validation-mlogloss:1.13607
[487]	train-mlogloss:1.10044	validation-mlogloss:1.13603
[488]	train-mlogloss:1.10030	validation-mlogloss:1.13594
[489]	train-mlogloss:1.10020	validation-mlogloss:1.13589
Saved model to disk.
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.44 [%]
Global accuracy score (test) = 63.44 [%]
Global F1 score (train) = 66.24 [%]
Global F1 score (test) = 62.8 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.51      0.44      0.47       161
 CAMINAR CON MÓVIL O LIBRO       0.41      0.59      0.48       161
       CAMINAR USUAL SPEED       0.47      0.30      0.36       161
            CAMINAR ZIGZAG       0.73      0.58      0.64       161
          DE PIE BARRIENDO       0.73      0.63      0.68       161
   DE PIE DOBLANDO TOALLAS       0.42      0.55      0.48       161
    DE PIE MOVIENDO LIBROS       0.55      0.48      0.51       161
          DE PIE USANDO PC       0.81      0.89      0.85       161
        FASE REPOSO CON K5       0.79      0.93      0.85       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.60      0.39      0.47       161
         SENTADO USANDO PC       0.48      0.61      0.54       161
      SENTADO VIENDO LA TV       0.53      0.36      0.43       161
   SUBIR Y BAJAR ESCALERAS       0.63      0.93      0.75       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.63      2415
              weighted avg       0.64      0.63      0.63      2415


Accuracy capturado en la ejecución 28: 63.44 [%]
F1-score capturado en la ejecución 28: 62.8 [%]

=== EJECUCIÓN 29 ===

--- TRAIN (ejecución 29) ---

--- TEST (ejecución 29) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  6  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.59 [%]
Global accuracy score (test) = 62.32 [%]
Global F1 score (train) = 66.4 [%]
Global F1 score (test) = 61.39 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.48      0.43      0.45       161
 CAMINAR CON MÓVIL O LIBRO       0.42      0.57      0.48       161
       CAMINAR USUAL SPEED       0.41      0.27      0.32       161
            CAMINAR ZIGZAG       0.70      0.58      0.64       161
          DE PIE BARRIENDO       0.73      0.61      0.66       161
   DE PIE DOBLANDO TOALLAS       0.43      0.54      0.48       161
    DE PIE MOVIENDO LIBROS       0.51      0.46      0.48       161
          DE PIE USANDO PC       0.81      0.91      0.86       161
        FASE REPOSO CON K5       0.75      0.93      0.83       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.57      0.37      0.45       161
         SENTADO USANDO PC       0.48      0.61      0.54       161
      SENTADO VIENDO LA TV       0.46      0.30      0.36       161
   SUBIR Y BAJAR ESCALERAS       0.64      0.94      0.76       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.62      2415
                 macro avg       0.62      0.62      0.61      2415
              weighted avg       0.62      0.62      0.61      2415


Accuracy capturado en la ejecución 29: 62.32 [%]
F1-score capturado en la ejecución 29: 61.39 [%]

=== EJECUCIÓN 30 ===

--- TRAIN (ejecución 30) ---

--- TEST (ejecución 30) ---
['CAMINAR CON LA COMPRA' 'CAMINAR CON MÓVIL O LIBRO' 'CAMINAR USUAL SPEED'
 'CAMINAR ZIGZAG' 'DE PIE BARRIENDO' 'DE PIE DOBLANDO TOALLAS'
 'DE PIE MOVIENDO LIBROS' 'DE PIE USANDO PC' 'FASE REPOSO CON K5'
 'INCREMENTAL CICLOERGOMETRO' 'SENTADO LEYENDO' 'SENTADO USANDO PC'
 'SENTADO VIENDO LA TV' 'SUBIR Y BAJAR ESCALERAS' 'TROTAR']
15
Mapeo de etiquetas: {'CAMINAR CON LA COMPRA': 0, 'CAMINAR CON MÓVIL O LIBRO': 1, 'CAMINAR USUAL SPEED': 2, 'CAMINAR ZIGZAG': 3, 'DE PIE BARRIENDO': 4, 'DE PIE DOBLANDO TOALLAS': 5, 'DE PIE MOVIENDO LIBROS': 6, 'DE PIE USANDO PC': 7, 'FASE REPOSO CON K5': 8, 'INCREMENTAL CICLOERGOMETRO': 9, 'SENTADO LEYENDO': 10, 'SENTADO USANDO PC': 11, 'SENTADO VIENDO LA TV': 12, 'SUBIR Y BAJAR ESCALERAS': 13, 'TROTAR': 14}
Training a non-convolutional model.
Loaded model from disk.
(2415, 91)
(17135, 91)
[[ 8.]
 [ 8.]
 [ 8.]
 ...
 [11.]
 [11.]
 [11.]]
(2415, 1)
[ 8  8  8 ... 12  5  7]
(2415,)
-------------------------------------------------

Global accuracy score (train) = 66.86 [%]
Global accuracy score (test) = 62.86 [%]
Global F1 score (train) = 66.7 [%]
Global F1 score (test) = 62.17 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.51      0.46      0.48       161
 CAMINAR CON MÓVIL O LIBRO       0.39      0.58      0.46       161
       CAMINAR USUAL SPEED       0.51      0.29      0.37       161
            CAMINAR ZIGZAG       0.73      0.57      0.64       161
          DE PIE BARRIENDO       0.73      0.61      0.67       161
   DE PIE DOBLANDO TOALLAS       0.43      0.55      0.48       161
    DE PIE MOVIENDO LIBROS       0.51      0.45      0.48       161
          DE PIE USANDO PC       0.83      0.93      0.88       161
        FASE REPOSO CON K5       0.77      0.91      0.83       161
INCREMENTAL CICLOERGOMETRO       0.99      0.98      0.98       161
           SENTADO LEYENDO       0.57      0.39      0.46       161
         SENTADO USANDO PC       0.46      0.58      0.52       161
      SENTADO VIENDO LA TV       0.50      0.33      0.40       161
   SUBIR Y BAJAR ESCALERAS       0.65      0.94      0.77       161
                    TROTAR       0.97      0.86      0.91       161

                  accuracy                           0.63      2415
                 macro avg       0.64      0.63      0.62      2415
              weighted avg       0.64      0.63      0.62      2415


Accuracy capturado en la ejecución 30: 62.86 [%]
F1-score capturado en la ejecución 30: 62.17 [%]

=== RESUMEN FINAL ===
Accuracies: [63.23, 63.31, 62.73, 62.82, 63.69, 63.6, 63.23, 62.61, 62.77, 63.15, 63.31, 62.61, 62.48, 63.11, 62.73, 63.02, 62.28, 62.77, 62.9, 62.36, 62.77, 62.94, 62.82, 63.52, 62.86, 62.65, 63.23, 63.44, 62.32, 62.86]
F1-scores: [62.52, 62.65, 61.84, 62.07, 62.94, 62.82, 62.56, 61.79, 61.86, 62.35, 62.5, 61.71, 61.57, 62.37, 61.97, 62.28, 61.56, 61.99, 62.21, 61.5, 61.91, 62.26, 61.94, 62.78, 62.04, 61.79, 62.56, 62.8, 61.39, 62.17]
Accuracy mean: 62.9373 | std: 0.3687
F1 mean: 62.1567 | std: 0.4224

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_PI/case_PI_XGB_acc_gyr_17_classes/metrics_test.npz
