2025-11-07 10:17:38.356109: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:17:38.367406: 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 00:00:1762507058.380746 2375058 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507058.384981 2375058 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507058.395416 2375058 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507058.395436 2375058 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507058.395438 2375058 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507058.395440 2375058 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:17:38.398796: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/simur/git/uniovi-simur-wearablepermed-ml/.venv/lib/python3.12/site-packages/ray/tune/impl/tuner_internal.py:144: RayDeprecationWarning: The `RunConfig` class should be imported from `ray.tune` when passing it to the Tuner. Please update your imports. See this issue for more context and migration options: https://github.com/ray-project/ray/issues/49454. Disable these warnings by setting the environment variable: RAY_TRAIN_ENABLE_V2_MIGRATION_WARNINGS=0
  _log_deprecation_warning(
2025-11-07 10:17:45,869	INFO worker.py:1927 -- Started a local Ray instance.
2025-11-07 10:17:46,559	INFO tune.py:253 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.
2025-11-07 10:17:46,624	INFO trial.py:182 -- Creating a new dirname dir_9fac0_7fc1 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,627	INFO trial.py:182 -- Creating a new dirname dir_9fac0_a5fb because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,629	INFO trial.py:182 -- Creating a new dirname dir_9fac0_fa1d because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,631	INFO trial.py:182 -- Creating a new dirname dir_9fac0_3a16 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,635	INFO trial.py:182 -- Creating a new dirname dir_9fac0_e592 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,639	INFO trial.py:182 -- Creating a new dirname dir_9fac0_3784 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,641	INFO trial.py:182 -- Creating a new dirname dir_9fac0_9a59 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,644	INFO trial.py:182 -- Creating a new dirname dir_9fac0_2236 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,648	INFO trial.py:182 -- Creating a new dirname dir_9fac0_94e5 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,651	INFO trial.py:182 -- Creating a new dirname dir_9fac0_2dff because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,654	INFO trial.py:182 -- Creating a new dirname dir_9fac0_9810 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,657	INFO trial.py:182 -- Creating a new dirname dir_9fac0_fc68 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,660	INFO trial.py:182 -- Creating a new dirname dir_9fac0_56ab because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,663	INFO trial.py:182 -- Creating a new dirname dir_9fac0_524d because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,667	INFO trial.py:182 -- Creating a new dirname dir_9fac0_2850 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,671	INFO trial.py:182 -- Creating a new dirname dir_9fac0_3e24 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,674	INFO trial.py:182 -- Creating a new dirname dir_9fac0_eb0d because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,678	INFO trial.py:182 -- Creating a new dirname dir_9fac0_cf82 because trial dirname 'dir_9fac0' already exists.
2025-11-07 10:17:46,683	INFO trial.py:182 -- Creating a new dirname dir_9fac0_0c6a because trial dirname 'dir_9fac0' 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_C/case_C_XGB_acc_17_classes/XGBoost_hyperparameters_tuning
To visualize your results with TensorBoard, run: `tensorboard --logdir /tmp/ray/session_2025-11-07_10-17-45_159722_2375058/artifacts/2025-11-07_10-17-46/XGBoost_hyperparameters_tuning/driver_artifacts`

Trial status: 20 PENDING
Current time: 2025-11-07 10:17:46. 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_9fac0    PENDING                  629             3        0.0129937       0.550764             0.826163    6.03809                  189       1.39658       47.2362              4621 │
│ trial_9fac0    PENDING                  496             3        0.011035        0.782066             0.57042     9.27748                  175       8.37826        6.3327              5415 │
│ trial_9fac0    PENDING                  367             2        0.0119098       0.822007             0.650588   16.299                    145      20.0921       538.425               1516 │
│ trial_9fac0    PENDING                  458             3        0.0130302       0.668953             0.523103    5.35156                   64      60.2809         5.22198             5380 │
│ trial_9fac0    PENDING                  356             2        0.0138383       0.824493             0.601674   25.6598                    95     465.168          7.60973             8067 │
│ trial_9fac0    PENDING                  466             2        0.00866783      0.506976             0.814022   21.9078                   170       3.50686      253.511               6833 │
│ trial_9fac0    PENDING                  665             2        0.0142689       0.700216             0.722649   22.8491                   126       9.67768      139.085               6238 │
│ trial_9fac0    PENDING                  373             3        0.00947212      0.564613             0.636997   12.8265                    76       2.40208       49.9773              7375 │
│ trial_9fac0    PENDING                  267             2        0.018387        0.590628             0.699044   16.8385                   165       7.23861      436.303               3292 │
│ trial_9fac0    PENDING                  387             3        0.0055591       0.717148             0.568672   17.6475                    81      67.7854       119.06                5871 │
│ trial_9fac0    PENDING                  507             3        0.0103495       0.821424             0.79813    12.3559                    83       6.42268        6.56943              354 │
│ trial_9fac0    PENDING                  677             3        0.00832983      0.81227              0.590491   12.3513                   179       2.83345       12.1536              7308 │
│ trial_9fac0    PENDING                  596             3        0.0133168       0.711692             0.693198    5.42144                  143      49.0868       214.24                3090 │
│ trial_9fac0    PENDING                  423             3        0.0131105       0.651097             0.668651   19.0739                   168       6.14307      683.04                 900 │
│ trial_9fac0    PENDING                  251             2        0.0151609       0.841882             0.513321   29.2414                   177       9.6365         6.78319             3251 │
│ trial_9fac0    PENDING                  209             3        0.0129513       0.629164             0.631645    9.7387                    70       2.36629        5.1895              4523 │
│ trial_9fac0    PENDING                  674             2        0.0105649       0.591425             0.549683   15.0153                   193      52.9773       522.183               3209 │
│ trial_9fac0    PENDING                  685             2        0.0140109       0.745984             0.730149   14.4615                    89     401.815         55.8077              2699 │
│ trial_9fac0    PENDING                  677             3        0.0123745       0.610346             0.800135   11.9738                   113      31.1399       397.982               5500 │
│ trial_9fac0    PENDING                  256             2        0.0194417       0.816468             0.590046    5.98402                  117     750.545        251.807                392 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.81402 │
│ gamma                        21.9078 │
│ learning_rate                0.00867 │
│ max_depth                          2 │
│ min_child_weight                 170 │
│ num_boost_round                  466 │
│ random_state                    6833 │
│ reg_alpha                    3.50686 │
│ reg_lambda                   253.511 │
│ subsample                    0.50698 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree               0.637 │
│ gamma                        12.8265 │
│ learning_rate                0.00947 │
│ max_depth                          3 │
│ min_child_weight                  76 │
│ num_boost_round                  373 │
│ random_state                    7375 │
│ reg_alpha                    2.40208 │
│ reg_lambda                   49.9773 │
│ subsample                    0.56461 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.57042 │
│ gamma                        9.27748 │
│ learning_rate                0.01103 │
│ max_depth                          3 │
│ min_child_weight                 175 │
│ num_boost_round                  496 │
│ random_state                    5415 │
│ reg_alpha                    8.37826 │
│ reg_lambda                    6.3327 │
│ subsample                    0.78207 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.73015 │
│ gamma                        14.4615 │
│ learning_rate                0.01401 │
│ max_depth                          2 │
│ min_child_weight                  89 │
│ num_boost_round                  685 │
│ random_state                    2699 │
│ reg_alpha                    401.815 │
│ reg_lambda                   55.8077 │
│ subsample                    0.74598 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.80014 │
│ gamma                        11.9738 │
│ learning_rate                0.01237 │
│ max_depth                          3 │
│ min_child_weight                 113 │
│ num_boost_round                  677 │
│ random_state                    5500 │
│ reg_alpha                    31.1399 │
│ reg_lambda                   397.982 │
│ subsample                    0.61035 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.51332 │
│ gamma                        29.2415 │
│ learning_rate                0.01516 │
│ max_depth                          2 │
│ min_child_weight                 177 │
│ num_boost_round                  251 │
│ random_state                    3251 │
│ reg_alpha                     9.6365 │
│ reg_lambda                   6.78319 │
│ subsample                    0.84188 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.82616 │
│ gamma                        6.03809 │
│ learning_rate                0.01299 │
│ max_depth                          3 │
│ min_child_weight                 189 │
│ num_boost_round                  629 │
│ random_state                    4621 │
│ reg_alpha                    1.39658 │
│ reg_lambda                   47.2362 │
│ subsample                    0.55076 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.65059 │
│ gamma                         16.299 │
│ learning_rate                0.01191 │
│ max_depth                          2 │
│ min_child_weight                 145 │
│ num_boost_round                  367 │
│ random_state                    1516 │
│ reg_alpha                    20.0921 │
│ reg_lambda                   538.425 │
│ subsample                    0.82201 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.72265 │
│ gamma                        22.8491 │
│ learning_rate                0.01427 │
│ max_depth                          2 │
│ min_child_weight                 126 │
│ num_boost_round                  665 │
│ random_state                    6238 │
│ reg_alpha                    9.67768 │
│ reg_lambda                   139.085 │
│ subsample                    0.70022 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree              0.5231 │
│ gamma                        5.35156 │
│ learning_rate                0.01303 │
│ max_depth                          3 │
│ min_child_weight                  64 │
│ num_boost_round                  458 │
│ random_state                    5380 │
│ reg_alpha                    60.2809 │
│ reg_lambda                   5.22198 │
│ subsample                    0.66895 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.59005 │
│ gamma                        5.98402 │
│ learning_rate                0.01944 │
│ max_depth                          2 │
│ min_child_weight                 117 │
│ num_boost_round                  256 │
│ random_state                     392 │
│ reg_alpha                    750.545 │
│ reg_lambda                   251.807 │
│ subsample                    0.81647 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.60167 │
│ gamma                        25.6598 │
│ learning_rate                0.01384 │
│ max_depth                          2 │
│ min_child_weight                  95 │
│ num_boost_round                  356 │
│ random_state                    8067 │
│ reg_alpha                    465.168 │
│ reg_lambda                   7.60973 │
│ subsample                    0.82449 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.59049 │
│ gamma                        12.3513 │
│ learning_rate                0.00833 │
│ max_depth                          3 │
│ min_child_weight                 179 │
│ num_boost_round                  677 │
│ random_state                    7308 │
│ reg_alpha                    2.83345 │
│ reg_lambda                   12.1536 │
│ subsample                    0.81227 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.56867 │
│ gamma                        17.6475 │
│ learning_rate                0.00556 │
│ max_depth                          3 │
│ min_child_weight                  81 │
│ num_boost_round                  387 │
│ random_state                    5871 │
│ reg_alpha                    67.7854 │
│ reg_lambda                    119.06 │
│ subsample                    0.71715 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.63165 │
│ gamma                         9.7387 │
│ learning_rate                0.01295 │
│ max_depth                          3 │
│ min_child_weight                  70 │
│ num_boost_round                  209 │
│ random_state                    4523 │
│ reg_alpha                    2.36629 │
│ reg_lambda                    5.1895 │
│ subsample                    0.62916 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.69904 │
│ gamma                        16.8385 │
│ learning_rate                0.01839 │
│ max_depth                          2 │
│ min_child_weight                 165 │
│ num_boost_round                  267 │
│ random_state                    3292 │
│ reg_alpha                    7.23861 │
│ reg_lambda                   436.303 │
│ subsample                    0.59063 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.66865 │
│ gamma                        19.0739 │
│ learning_rate                0.01311 │
│ max_depth                          3 │
│ min_child_weight                 168 │
│ num_boost_round                  423 │
│ random_state                     900 │
│ reg_alpha                    6.14307 │
│ reg_lambda                    683.04 │
│ subsample                     0.6511 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree              0.6932 │
│ gamma                        5.42144 │
│ learning_rate                0.01332 │
│ max_depth                          3 │
│ min_child_weight                 143 │
│ num_boost_round                  596 │
│ random_state                    3090 │
│ reg_alpha                    49.0868 │
│ reg_lambda                    214.24 │
│ subsample                    0.71169 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
[36m(train_xgb_ray_tune pid=2376741)[0m 2025-11-07 10:17:49.809638: 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=2376741)[0m 2025-11-07 10:17:49.832826: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
[36m(train_xgb_ray_tune pid=2376735)[0m WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
[36m(train_xgb_ray_tune pid=2376735)[0m E0000 00:00:1762507069.841522 2377864 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=2376735)[0m E0000 00:00:1762507069.849465 2377864 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=2376741)[0m W0000 00:00:1762507069.888927 2377871 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=2376741)[0m W0000 00:00:1762507069.888979 2377871 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=2376741)[0m W0000 00:00:1762507069.888981 2377871 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=2376741)[0m W0000 00:00:1762507069.889102 2377871 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=2376741)[0m 2025-11-07 10:17:49.895375: 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=2376741)[0m To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.54968 │
│ gamma                        15.0153 │
│ learning_rate                0.01056 │
│ max_depth                          2 │
│ min_child_weight                 193 │
│ num_boost_round                  674 │
│ random_state                    3209 │
│ reg_alpha                    52.9773 │
│ reg_lambda                   522.183 │
│ subsample                    0.59142 │
╰──────────────────────────────────────╯
Trial trial_9fac0 started with configuration:
╭──────────────────────────────────────╮
│ Trial trial_9fac0 config             │
├──────────────────────────────────────┤
│ colsample_bytree             0.79813 │
│ gamma                        12.3559 │
│ learning_rate                0.01035 │
│ max_depth                          3 │
│ min_child_weight                  83 │
│ num_boost_round                  507 │
│ random_state                     354 │
│ reg_alpha                    6.42268 │
│ reg_lambda                   6.56943 │
│ subsample                    0.82142 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=2376735)[0m [0]	train-mlogloss:2.70380	validation-mlogloss:2.70370
[36m(train_xgb_ray_tune pid=2376722)[0m [0]	train-mlogloss:2.69439	validation-mlogloss:2.69500
[36m(train_xgb_ray_tune pid=2376722)[0m [1]	train-mlogloss:2.68146	validation-mlogloss:2.68257
[36m(train_xgb_ray_tune pid=2376735)[0m [1]	train-mlogloss:2.69936	validation-mlogloss:2.69917
[36m(train_xgb_ray_tune pid=2376735)[0m [2]	train-mlogloss:2.69515	validation-mlogloss:2.69489
[36m(train_xgb_ray_tune pid=2376736)[0m [0]	train-mlogloss:2.70218	validation-mlogloss:2.70225
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[36m(train_xgb_ray_tune pid=2376748)[0m [146]	train-mlogloss:2.19739	validation-mlogloss:2.19172
[36m(train_xgb_ray_tune pid=2376737)[0m [143]	train-mlogloss:1.90843	validation-mlogloss:1.91597
[36m(train_xgb_ray_tune pid=2376736)[0m [160]	train-mlogloss:2.08569	validation-mlogloss:2.09215
[36m(train_xgb_ray_tune pid=2376716)[0m [166]	train-mlogloss:2.09134	validation-mlogloss:2.09078
[36m(train_xgb_ray_tune pid=2376742)[0m [211]	train-mlogloss:2.53311	validation-mlogloss:2.52686
[36m(train_xgb_ray_tune pid=2376742)[0m [212]	train-mlogloss:2.53288	validation-mlogloss:2.52662
[36m(train_xgb_ray_tune pid=2376729)[0m [143]	train-mlogloss:1.82677	validation-mlogloss:1.86385
[36m(train_xgb_ray_tune pid=2376744)[0m [208]	train-mlogloss:2.67924	validation-mlogloss:2.67801
[36m(train_xgb_ray_tune pid=2376722)[0m [128]	train-mlogloss:1.86710	validation-mlogloss:1.90666
[36m(train_xgb_ray_tune pid=2376743)[0m [154]	train-mlogloss:2.22267	validation-mlogloss:2.23383
[36m(train_xgb_ray_tune pid=2376746)[0m [126]	train-mlogloss:1.98298	validation-mlogloss:2.01553
[36m(train_xgb_ray_tune pid=2376741)[0m [135]	train-mlogloss:1.93734	validation-mlogloss:1.96941
[36m(train_xgb_ray_tune pid=2376728)[0m [135]	train-mlogloss:1.89195	validation-mlogloss:1.90474
[36m(train_xgb_ray_tune pid=2376750)[0m [119]	train-mlogloss:2.05532	validation-mlogloss:2.07564
[36m(train_xgb_ray_tune pid=2376735)[0m [156]	train-mlogloss:2.20573	validation-mlogloss:2.19480
[36m(train_xgb_ray_tune pid=2376740)[0m [202]	train-mlogloss:2.49791	validation-mlogloss:2.49200
[36m(train_xgb_ray_tune pid=2376739)[0m [126]	train-mlogloss:1.75047	validation-mlogloss:1.80499
[36m(train_xgb_ray_tune pid=2376749)[0m [156]	train-mlogloss:2.29096	validation-mlogloss:2.28058
[36m(train_xgb_ray_tune pid=2376738)[0m [149]	train-mlogloss:1.99947	validation-mlogloss:1.99422
[36m(train_xgb_ray_tune pid=2376745)[0m [153]	train-mlogloss:1.77437	validation-mlogloss:1.79154
[36m(train_xgb_ray_tune pid=2376748)[0m [147]	train-mlogloss:2.19503	validation-mlogloss:2.18946
[36m(train_xgb_ray_tune pid=2376737)[0m [144]	train-mlogloss:1.90590	validation-mlogloss:1.91351
[36m(train_xgb_ray_tune pid=2376736)[0m [161]	train-mlogloss:2.08349	validation-mlogloss:2.08995
[36m(train_xgb_ray_tune pid=2376716)[0m [167]	train-mlogloss:2.08915	validation-mlogloss:2.08860
[36m(train_xgb_ray_tune pid=2376742)[0m [213]	train-mlogloss:2.53288	validation-mlogloss:2.52662
[36m(train_xgb_ray_tune pid=2376729)[0m [144]	train-mlogloss:1.82437	validation-mlogloss:1.86146
[36m(train_xgb_ray_tune pid=2376744)[0m [209]	train-mlogloss:2.67924	validation-mlogloss:2.67801
[36m(train_xgb_ray_tune pid=2376744)[0m [210]	train-mlogloss:2.67924	validation-mlogloss:2.67801
[36m(train_xgb_ray_tune pid=2376722)[0m [129]	train-mlogloss:1.86392	validation-mlogloss:1.90387
[36m(train_xgb_ray_tune pid=2376743)[0m [155]	train-mlogloss:2.22053	validation-mlogloss:2.23180
[36m(train_xgb_ray_tune pid=2376746)[0m [127]	train-mlogloss:1.97982	validation-mlogloss:2.01253
[36m(train_xgb_ray_tune pid=2376741)[0m [136]	train-mlogloss:1.93441	validation-mlogloss:1.96675
[36m(train_xgb_ray_tune pid=2376747)[0m [110]	train-mlogloss:1.89599	validation-mlogloss:1.94146
[36m(train_xgb_ray_tune pid=2376728)[0m [136]	train-mlogloss:1.88906	validation-mlogloss:1.90203
[36m(train_xgb_ray_tune pid=2376750)[0m [120]	train-mlogloss:2.05227	validation-mlogloss:2.07268
[36m(train_xgb_ray_tune pid=2376735)[0m [157]	train-mlogloss:2.20357	validation-mlogloss:2.19262
[36m(train_xgb_ray_tune pid=2376740)[0m [203]	train-mlogloss:2.49771	validation-mlogloss:2.49180
[36m(train_xgb_ray_tune pid=2376739)[0m [127]	train-mlogloss:1.74736	validation-mlogloss:1.80198
[36m(train_xgb_ray_tune pid=2376749)[0m [157]	train-mlogloss:2.28902	validation-mlogloss:2.27855
[36m(train_xgb_ray_tune pid=2376749)[0m [158]	train-mlogloss:2.28709	validation-mlogloss:2.27661
[36m(train_xgb_ray_tune pid=2376743)[0m [156]	train-mlogloss:2.21840	validation-mlogloss:2.22970
[36m(train_xgb_ray_tune pid=2376738)[0m [150]	train-mlogloss:1.99704	validation-mlogloss:1.99189

Trial status: 20 RUNNING
Current time: 2025-11-07 10:18:16. 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_9fac0    RUNNING                  629             3        0.0129937       0.550764             0.826163    6.03809                  189       1.39658       47.2362              4621 │
│ trial_9fac0    RUNNING                  496             3        0.011035        0.782066             0.57042     9.27748                  175       8.37826        6.3327              5415 │
│ trial_9fac0    RUNNING                  367             2        0.0119098       0.822007             0.650588   16.299                    145      20.0921       538.425               1516 │
│ trial_9fac0    RUNNING                  458             3        0.0130302       0.668953             0.523103    5.35156                   64      60.2809         5.22198             5380 │
│ trial_9fac0    RUNNING                  356             2        0.0138383       0.824493             0.601674   25.6598                    95     465.168          7.60973             8067 │
│ trial_9fac0    RUNNING                  466             2        0.00866783      0.506976             0.814022   21.9078                   170       3.50686      253.511               6833 │
│ trial_9fac0    RUNNING                  665             2        0.0142689       0.700216             0.722649   22.8491                   126       9.67768      139.085               6238 │
│ trial_9fac0    RUNNING                  373             3        0.00947212      0.564613             0.636997   12.8265                    76       2.40208       49.9773              7375 │
│ trial_9fac0    RUNNING                  267             2        0.018387        0.590628             0.699044   16.8385                   165       7.23861      436.303               3292 │
│ trial_9fac0    RUNNING                  387             3        0.0055591       0.717148             0.568672   17.6475                    81      67.7854       119.06                5871 │
│ trial_9fac0    RUNNING                  507             3        0.0103495       0.821424             0.79813    12.3559                    83       6.42268        6.56943              354 │
│ trial_9fac0    RUNNING                  677             3        0.00832983      0.81227              0.590491   12.3513                   179       2.83345       12.1536              7308 │
│ trial_9fac0    RUNNING                  596             3        0.0133168       0.711692             0.693198    5.42144                  143      49.0868       214.24                3090 │
│ trial_9fac0    RUNNING                  423             3        0.0131105       0.651097             0.668651   19.0739                   168       6.14307      683.04                 900 │
│ trial_9fac0    RUNNING                  251             2        0.0151609       0.841882             0.513321   29.2414                   177       9.6365         6.78319             3251 │
│ trial_9fac0    RUNNING                  209             3        0.0129513       0.629164             0.631645    9.7387                    70       2.36629        5.1895              4523 │
│ trial_9fac0    RUNNING                  674             2        0.0105649       0.591425             0.549683   15.0153                   193      52.9773       522.183               3209 │
│ trial_9fac0    RUNNING                  685             2        0.0140109       0.745984             0.730149   14.4615                    89     401.815         55.8077              2699 │
│ trial_9fac0    RUNNING                  677             3        0.0123745       0.610346             0.800135   11.9738                   113      31.1399       397.982               5500 │
│ trial_9fac0    RUNNING                  256             2        0.0194417       0.816468             0.590046    5.98402                  117     750.545        251.807                392 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=2376745)[0m [154]	train-mlogloss:1.77231	validation-mlogloss:1.78951
[36m(train_xgb_ray_tune pid=2376748)[0m [148]	train-mlogloss:2.19281	validation-mlogloss:2.18720
[36m(train_xgb_ray_tune pid=2376737)[0m [145]	train-mlogloss:1.90337	validation-mlogloss:1.91097
[36m(train_xgb_ray_tune pid=2376736)[0m [162]	train-mlogloss:2.08116	validation-mlogloss:2.08768
[36m(train_xgb_ray_tune pid=2376716)[0m [168]	train-mlogloss:2.08690	validation-mlogloss:2.08638
[36m(train_xgb_ray_tune pid=2376742)[0m [214]	train-mlogloss:2.53265	validation-mlogloss:2.52637
[36m(train_xgb_ray_tune pid=2376744)[0m [211]	train-mlogloss:2.67924	validation-mlogloss:2.67801
[36m(train_xgb_ray_tune pid=2376722)[0m [130]	train-mlogloss:1.86095	validation-mlogloss:1.90092
[36m(train_xgb_ray_tune pid=2376746)[0m [128]	train-mlogloss:1.97675	validation-mlogloss:2.00961
[36m(train_xgb_ray_tune pid=2376747)[0m [111]	train-mlogloss:1.89247	validation-mlogloss:1.93807
[36m(train_xgb_ray_tune pid=2376750)[0m [121]	train-mlogloss:2.04921	validation-mlogloss:2.06971
[36m(train_xgb_ray_tune pid=2376735)[0m [158]	train-mlogloss:2.20141	validation-mlogloss:2.19046
[36m(train_xgb_ray_tune pid=2376740)[0m [204]	train-mlogloss:2.49721	validation-mlogloss:2.49126
[36m(train_xgb_ray_tune pid=2376739)[0m [128]	train-mlogloss:1.74422	validation-mlogloss:1.79894
[36m(train_xgb_ray_tune pid=2376749)[0m [159]	train-mlogloss:2.28517	validation-mlogloss:2.27463
[36m(train_xgb_ray_tune pid=2376745)[0m [155]	train-mlogloss:1.77013	validation-mlogloss:1.78759
[36m(train_xgb_ray_tune pid=2376748)[0m [149]	train-mlogloss:2.19047	validation-mlogloss:2.18480
[36m(train_xgb_ray_tune pid=2376737)[0m [146]	train-mlogloss:1.90089	validation-mlogloss:1.90851
[36m(train_xgb_ray_tune pid=2376736)[0m [163]	train-mlogloss:2.07888	validation-mlogloss:2.08541
[36m(train_xgb_ray_tune pid=2376716)[0m [169]	train-mlogloss:2.08472	validation-mlogloss:2.08426
[36m(train_xgb_ray_tune pid=2376742)[0m [215]	train-mlogloss:2.53265	validation-mlogloss:2.52637
[36m(train_xgb_ray_tune pid=2376729)[0m [145]	train-mlogloss:1.82196	validation-mlogloss:1.85916
[36m(train_xgb_ray_tune pid=2376744)[0m [212]	train-mlogloss:2.67907	validation-mlogloss:2.67783
[36m(train_xgb_ray_tune pid=2376743)[0m [157]	train-mlogloss:2.21627	validation-mlogloss:2.22755
[36m(train_xgb_ray_tune pid=2376741)[0m [137]	train-mlogloss:1.93147	validation-mlogloss:1.96383
[36m(train_xgb_ray_tune pid=2376738)[0m [151]	train-mlogloss:1.99446	validation-mlogloss:1.98936
[36m(train_xgb_ray_tune pid=2376728)[0m [137]	train-mlogloss:1.88612	validation-mlogloss:1.89925
[36m(train_xgb_ray_tune pid=2376735)[0m [159]	train-mlogloss:2.19931	validation-mlogloss:2.18833
[36m(train_xgb_ray_tune pid=2376740)[0m [205]	train-mlogloss:2.49696	validation-mlogloss:2.49100
[36m(train_xgb_ray_tune pid=2376745)[0m [156]	train-mlogloss:1.76807	validation-mlogloss:1.78556
[36m(train_xgb_ray_tune pid=2376748)[0m [150]	train-mlogloss:2.18830	validation-mlogloss:2.18264
[36m(train_xgb_ray_tune pid=2376737)[0m [147]	train-mlogloss:1.89843	validation-mlogloss:1.90596
[36m(train_xgb_ray_tune pid=2376736)[0m [164]	train-mlogloss:2.07661	validation-mlogloss:2.08312
[36m(train_xgb_ray_tune pid=2376716)[0m [170]	train-mlogloss:2.08259	validation-mlogloss:2.08218
[36m(train_xgb_ray_tune pid=2376742)[0m [216]	train-mlogloss:2.53265	validation-mlogloss:2.52637
[36m(train_xgb_ray_tune pid=2376742)[0m [217]	train-mlogloss:2.53242	validation-mlogloss:2.52614
[36m(train_xgb_ray_tune pid=2376729)[0m [146]	train-mlogloss:1.81955	validation-mlogloss:1.85698
[36m(train_xgb_ray_tune pid=2376744)[0m [213]	train-mlogloss:2.67907	validation-mlogloss:2.67783
[36m(train_xgb_ray_tune pid=2376722)[0m [131]	train-mlogloss:1.85800	validation-mlogloss:1.89812
[36m(train_xgb_ray_tune pid=2376743)[0m [158]	train-mlogloss:2.21412	validation-mlogloss:2.22550
[36m(train_xgb_ray_tune pid=2376746)[0m [129]	train-mlogloss:1.97367	validation-mlogloss:2.00661
[36m(train_xgb_ray_tune pid=2376741)[0m [138]	train-mlogloss:1.92856	validation-mlogloss:1.96095
[36m(train_xgb_ray_tune pid=2376747)[0m [112]	train-mlogloss:1.88889	validation-mlogloss:1.93490
[36m(train_xgb_ray_tune pid=2376738)[0m [152]	train-mlogloss:1.99205	validation-mlogloss:1.98692
[36m(train_xgb_ray_tune pid=2376728)[0m [138]	train-mlogloss:1.88318	validation-mlogloss:1.89663
[36m(train_xgb_ray_tune pid=2376750)[0m [122]	train-mlogloss:2.04607	validation-mlogloss:2.06648
[36m(train_xgb_ray_tune pid=2376735)[0m [160]	train-mlogloss:2.19707	validation-mlogloss:2.18602
[36m(train_xgb_ray_tune pid=2376740)[0m [206]	train-mlogloss:2.49662	validation-mlogloss:2.49065
[36m(train_xgb_ray_tune pid=2376739)[0m [129]	train-mlogloss:1.74108	validation-mlogloss:1.79613
[36m(train_xgb_ray_tune pid=2376749)[0m [160]	train-mlogloss:2.28335	validation-mlogloss:2.27267
[36m(train_xgb_ray_tune pid=2376743)[0m [159]	train-mlogloss:2.21194	validation-mlogloss:2.22332
[36m(train_xgb_ray_tune pid=2376740)[0m [207]	train-mlogloss:2.49628	validation-mlogloss:2.49028
[36m(train_xgb_ray_tune pid=2376749)[0m [161]	train-mlogloss:2.28137	validation-mlogloss:2.27060
[36m(train_xgb_ray_tune pid=2376744)[0m [214]	train-mlogloss:2.67893	validation-mlogloss:2.67769
[36m(train_xgb_ray_tune pid=2376745)[0m [157]	train-mlogloss:1.76600	validation-mlogloss:1.78364
[36m(train_xgb_ray_tune pid=2376748)[0m [151]	train-mlogloss:2.18605	validation-mlogloss:2.18029
[36m(train_xgb_ray_tune pid=2376737)[0m [148]	train-mlogloss:1.89604	validation-mlogloss:1.90356
[36m(train_xgb_ray_tune pid=2376736)[0m [165]	train-mlogloss:2.07438	validation-mlogloss:2.08097
[36m(train_xgb_ray_tune pid=2376716)[0m [171]	train-mlogloss:2.08030	validation-mlogloss:2.08001
[36m(train_xgb_ray_tune pid=2376742)[0m [218]	train-mlogloss:2.53242	validation-mlogloss:2.52614
[36m(train_xgb_ray_tune pid=2376729)[0m [147]	train-mlogloss:1.81720	validation-mlogloss:1.85461
[36m(train_xgb_ray_tune pid=2376744)[0m [215]	train-mlogloss:2.67880	validation-mlogloss:2.67755
[36m(train_xgb_ray_tune pid=2376722)[0m [132]	train-mlogloss:1.85498	validation-mlogloss:1.89517
[36m(train_xgb_ray_tune pid=2376743)[0m [160]	train-mlogloss:2.20998	validation-mlogloss:2.22133
[36m(train_xgb_ray_tune pid=2376746)[0m [130]	train-mlogloss:1.97065	validation-mlogloss:2.00378
[36m(train_xgb_ray_tune pid=2376741)[0m [139]	train-mlogloss:1.92566	validation-mlogloss:1.95830
[36m(train_xgb_ray_tune pid=2376747)[0m [113]	train-mlogloss:1.88536	validation-mlogloss:1.93162
[36m(train_xgb_ray_tune pid=2376738)[0m [153]	train-mlogloss:1.98959	validation-mlogloss:1.98429
[36m(train_xgb_ray_tune pid=2376728)[0m [139]	train-mlogloss:1.88020	validation-mlogloss:1.89381
[36m(train_xgb_ray_tune pid=2376750)[0m [123]	train-mlogloss:2.04295	validation-mlogloss:2.06350
[36m(train_xgb_ray_tune pid=2376735)[0m [161]	train-mlogloss:2.19493	validation-mlogloss:2.18379
[36m(train_xgb_ray_tune pid=2376740)[0m [208]	train-mlogloss:2.49606	validation-mlogloss:2.49005
[36m(train_xgb_ray_tune pid=2376739)[0m [130]	train-mlogloss:1.73794	validation-mlogloss:1.79301
[36m(train_xgb_ray_tune pid=2376749)[0m [162]	train-mlogloss:2.27938	validation-mlogloss:2.26859
[36m(train_xgb_ray_tune pid=2376745)[0m [158]	train-mlogloss:1.76400	validation-mlogloss:1.78174
[36m(train_xgb_ray_tune pid=2376748)[0m [152]	train-mlogloss:2.18366	validation-mlogloss:2.17796
[36m(train_xgb_ray_tune pid=2376737)[0m [149]	train-mlogloss:1.89344	validation-mlogloss:1.90117
[36m(train_xgb_ray_tune pid=2376736)[0m [166]	train-mlogloss:2.07210	validation-mlogloss:2.07876
[36m(train_xgb_ray_tune pid=2376716)[0m [172]	train-mlogloss:2.07811	validation-mlogloss:2.07782
[36m(train_xgb_ray_tune pid=2376742)[0m [219]	train-mlogloss:2.53242	validation-mlogloss:2.52614
[36m(train_xgb_ray_tune pid=2376742)[0m [220]	train-mlogloss:2.53202	validation-mlogloss:2.52573
[36m(train_xgb_ray_tune pid=2376744)[0m [216]	train-mlogloss:2.67880	validation-mlogloss:2.67755
[36m(train_xgb_ray_tune pid=2376743)[0m [161]	train-mlogloss:2.20784	validation-mlogloss:2.21918
[36m(train_xgb_ray_tune pid=2376746)[0m [131]	train-mlogloss:1.96765	validation-mlogloss:2.00087
[36m(train_xgb_ray_tune pid=2376741)[0m [140]	train-mlogloss:1.92281	validation-mlogloss:1.95558
[36m(train_xgb_ray_tune pid=2376738)[0m [154]	train-mlogloss:1.98717	validation-mlogloss:1.98178
[36m(train_xgb_ray_tune pid=2376728)[0m [140]	train-mlogloss:1.87711	validation-mlogloss:1.89075
[36m(train_xgb_ray_tune pid=2376750)[0m [124]	train-mlogloss:2.03985	validation-mlogloss:2.06051
[36m(train_xgb_ray_tune pid=2376735)[0m [162]	train-mlogloss:2.19280	validation-mlogloss:2.18161
[36m(train_xgb_ray_tune pid=2376740)[0m [209]	train-mlogloss:2.49575	validation-mlogloss:2.48972
[36m(train_xgb_ray_tune pid=2376739)[0m [131]	train-mlogloss:1.73493	validation-mlogloss:1.79010
[36m(train_xgb_ray_tune pid=2376749)[0m [163]	train-mlogloss:2.27758	validation-mlogloss:2.26670
[36m(train_xgb_ray_tune pid=2376722)[0m [133]	train-mlogloss:1.85200	validation-mlogloss:1.89240
[36m(train_xgb_ray_tune pid=2376745)[0m [159]	train-mlogloss:1.76191	validation-mlogloss:1.77985
[36m(train_xgb_ray_tune pid=2376748)[0m [153]	train-mlogloss:2.18142	validation-mlogloss:2.17563
[36m(train_xgb_ray_tune pid=2376737)[0m [150]	train-mlogloss:1.89111	validation-mlogloss:1.89877
[36m(train_xgb_ray_tune pid=2376736)[0m [167]	train-mlogloss:2.06991	validation-mlogloss:2.07672
[36m(train_xgb_ray_tune pid=2376716)[0m [173]	train-mlogloss:2.07588	validation-mlogloss:2.07561
[36m(train_xgb_ray_tune pid=2376742)[0m [221]	train-mlogloss:2.53202	validation-mlogloss:2.52573
[36m(train_xgb_ray_tune pid=2376729)[0m [148]	train-mlogloss:1.81482	validation-mlogloss:1.85236
[36m(train_xgb_ray_tune pid=2376744)[0m [217]	train-mlogloss:2.67880	validation-mlogloss:2.67755
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[36m(train_xgb_ray_tune pid=2376743)[0m [186]	train-mlogloss:2.15917	validation-mlogloss:2.17122
[36m(train_xgb_ray_tune pid=2376746)[0m [153]	train-mlogloss:1.90594	validation-mlogloss:1.94210
[36m(train_xgb_ray_tune pid=2376741)[0m [164]	train-mlogloss:1.85954	validation-mlogloss:1.89483
[36m(train_xgb_ray_tune pid=2376747)[0m [135]	train-mlogloss:1.81442	validation-mlogloss:1.86551
[36m(train_xgb_ray_tune pid=2376738)[0m [179]	train-mlogloss:1.93238	validation-mlogloss:1.92853
[36m(train_xgb_ray_tune pid=2376728)[0m [163]	train-mlogloss:1.81485	validation-mlogloss:1.83213
[36m(train_xgb_ray_tune pid=2376735)[0m [188]	train-mlogloss:2.14028	validation-mlogloss:2.12870
[36m(train_xgb_ray_tune pid=2376740)[0m [243]	train-mlogloss:2.48667	validation-mlogloss:2.48029
[36m(train_xgb_ray_tune pid=2376749)[0m [188]	train-mlogloss:2.23321	validation-mlogloss:2.22122
[36m(train_xgb_ray_tune pid=2376745)[0m [186]	train-mlogloss:1.71364	validation-mlogloss:1.73357
[36m(train_xgb_ray_tune pid=2376748)[0m [178]	train-mlogloss:2.12832	validation-mlogloss:2.12180
[36m(train_xgb_ray_tune pid=2376737)[0m [172]	train-mlogloss:1.84310	validation-mlogloss:1.85215
[36m(train_xgb_ray_tune pid=2376736)[0m [193]	train-mlogloss:2.01522	validation-mlogloss:2.02316
[36m(train_xgb_ray_tune pid=2376716)[0m [201]	train-mlogloss:2.01936	validation-mlogloss:2.02068
[36m(train_xgb_ray_tune pid=2376742)[0m [257]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376729)[0m [174]	train-mlogloss:1.75987	validation-mlogloss:1.80057
[36m(train_xgb_ray_tune pid=2376744)[0m [253]	train-mlogloss:2.67747	validation-mlogloss:2.67617
[36m(train_xgb_ray_tune pid=2376743)[0m [187]	train-mlogloss:2.15730	validation-mlogloss:2.16937
[36m(train_xgb_ray_tune pid=2376746)[0m [154]	train-mlogloss:1.90335	validation-mlogloss:1.93953
[36m(train_xgb_ray_tune pid=2376747)[0m [136]	train-mlogloss:1.81157	validation-mlogloss:1.86284
[36m(train_xgb_ray_tune pid=2376738)[0m [180]	train-mlogloss:1.93034	validation-mlogloss:1.92658
[36m(train_xgb_ray_tune pid=2376728)[0m [164]	train-mlogloss:1.81228	validation-mlogloss:1.82964
[36m(train_xgb_ray_tune pid=2376750)[0m [146]	train-mlogloss:1.97760	validation-mlogloss:2.00055
[36m(train_xgb_ray_tune pid=2376740)[0m [244]	train-mlogloss:2.48654	validation-mlogloss:2.48016
[36m(train_xgb_ray_tune pid=2376739)[0m [155]	train-mlogloss:1.66957	validation-mlogloss:1.73146
[36m(train_xgb_ray_tune pid=2376749)[0m [189]	train-mlogloss:2.23143	validation-mlogloss:2.21930
[36m(train_xgb_ray_tune pid=2376722)[0m [156]	train-mlogloss:1.78914	validation-mlogloss:1.83331
[36m(train_xgb_ray_tune pid=2376744)[0m [254]	train-mlogloss:2.67747	validation-mlogloss:2.67617
[36m(train_xgb_ray_tune pid=2376741)[0m [165]	train-mlogloss:1.85707	validation-mlogloss:1.89252
[36m(train_xgb_ray_tune pid=2376745)[0m [187]	train-mlogloss:1.71209	validation-mlogloss:1.73214
[36m(train_xgb_ray_tune pid=2376748)[0m [179]	train-mlogloss:2.12616	validation-mlogloss:2.11969
[36m(train_xgb_ray_tune pid=2376737)[0m [173]	train-mlogloss:1.84118	validation-mlogloss:1.85025
[36m(train_xgb_ray_tune pid=2376736)[0m [194]	train-mlogloss:2.01319	validation-mlogloss:2.02110
[36m(train_xgb_ray_tune pid=2376716)[0m [202]	train-mlogloss:2.01758	validation-mlogloss:2.01890
[36m(train_xgb_ray_tune pid=2376742)[0m [258]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376742)[0m [259]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376729)[0m [175]	train-mlogloss:1.75798	validation-mlogloss:1.79879
[36m(train_xgb_ray_tune pid=2376744)[0m [255]	train-mlogloss:2.67747	validation-mlogloss:2.67617
[36m(train_xgb_ray_tune pid=2376743)[0m [188]	train-mlogloss:2.15544	validation-mlogloss:2.16749
[36m(train_xgb_ray_tune pid=2376728)[0m [165]	train-mlogloss:1.80979	validation-mlogloss:1.82728
[36m(train_xgb_ray_tune pid=2376750)[0m [147]	train-mlogloss:1.97502	validation-mlogloss:1.99813
[36m(train_xgb_ray_tune pid=2376735)[0m [189]	train-mlogloss:2.13837	validation-mlogloss:2.12672
[36m(train_xgb_ray_tune pid=2376740)[0m [245]	train-mlogloss:2.48644	validation-mlogloss:2.48004
[36m(train_xgb_ray_tune pid=2376740)[0m [246]	train-mlogloss:2.48634	validation-mlogloss:2.47996
[36m(train_xgb_ray_tune pid=2376739)[0m [156]	train-mlogloss:1.66722	validation-mlogloss:1.72919
[36m(train_xgb_ray_tune pid=2376749)[0m [190]	train-mlogloss:2.22979	validation-mlogloss:2.21756
[36m(train_xgb_ray_tune pid=2376746)[0m [155]	train-mlogloss:1.90071	validation-mlogloss:1.93699
[36m(train_xgb_ray_tune pid=2376747)[0m [137]	train-mlogloss:1.80862	validation-mlogloss:1.85991
[36m(train_xgb_ray_tune pid=2376738)[0m [181]	train-mlogloss:1.92830	validation-mlogloss:1.92467
[36m(train_xgb_ray_tune pid=2376745)[0m [188]	train-mlogloss:1.71053	validation-mlogloss:1.73075
[36m(train_xgb_ray_tune pid=2376722)[0m [157]	train-mlogloss:1.78673	validation-mlogloss:1.83096
[36m(train_xgb_ray_tune pid=2376716)[0m [203]	train-mlogloss:2.01560	validation-mlogloss:2.01709
[36m(train_xgb_ray_tune pid=2376735)[0m [190]	train-mlogloss:2.13644	validation-mlogloss:2.12486
[36m(train_xgb_ray_tune pid=2376748)[0m [180]	train-mlogloss:2.12408	validation-mlogloss:2.11758
[36m(train_xgb_ray_tune pid=2376737)[0m [174]	train-mlogloss:1.83921	validation-mlogloss:1.84832
[36m(train_xgb_ray_tune pid=2376736)[0m [195]	train-mlogloss:2.01123	validation-mlogloss:2.01923
[36m(train_xgb_ray_tune pid=2376742)[0m [260]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376729)[0m [176]	train-mlogloss:1.75603	validation-mlogloss:1.79690
[36m(train_xgb_ray_tune pid=2376743)[0m [189]	train-mlogloss:2.15360	validation-mlogloss:2.16570
[36m(train_xgb_ray_tune pid=2376741)[0m [166]	train-mlogloss:1.85464	validation-mlogloss:1.89030
[36m(train_xgb_ray_tune pid=2376738)[0m [182]	train-mlogloss:1.92637	validation-mlogloss:1.92265
[36m(train_xgb_ray_tune pid=2376728)[0m [166]	train-mlogloss:1.80728	validation-mlogloss:1.82488
[36m(train_xgb_ray_tune pid=2376750)[0m [148]	train-mlogloss:1.97237	validation-mlogloss:1.99565
[36m(train_xgb_ray_tune pid=2376740)[0m [247]	train-mlogloss:2.48617	validation-mlogloss:2.47978
[36m(train_xgb_ray_tune pid=2376739)[0m [157]	train-mlogloss:1.66477	validation-mlogloss:1.72699
[36m(train_xgb_ray_tune pid=2376749)[0m [191]	train-mlogloss:2.22804	validation-mlogloss:2.21580
[36m(train_xgb_ray_tune pid=2376748)[0m [181]	train-mlogloss:2.12213	validation-mlogloss:2.11554
[36m(train_xgb_ray_tune pid=2376716)[0m [204]	train-mlogloss:2.01372	validation-mlogloss:2.01536
[36m(train_xgb_ray_tune pid=2376742)[0m [261]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376736)[0m [196]	train-mlogloss:2.00935	validation-mlogloss:2.01746
[36m(train_xgb_ray_tune pid=2376746)[0m [156]	train-mlogloss:1.89823	validation-mlogloss:1.93484
[36m(train_xgb_ray_tune pid=2376740)[0m [248]	train-mlogloss:2.48598	validation-mlogloss:2.47957
[36m(train_xgb_ray_tune pid=2376735)[0m [191]	train-mlogloss:2.13447	validation-mlogloss:2.12281
[36m(train_xgb_ray_tune pid=2376745)[0m [189]	train-mlogloss:1.70896	validation-mlogloss:1.72915
[36m(train_xgb_ray_tune pid=2376747)[0m [138]	train-mlogloss:1.80571	validation-mlogloss:1.85720
[36m(train_xgb_ray_tune pid=2376748)[0m [182]	train-mlogloss:2.12018	validation-mlogloss:2.11371
[36m(train_xgb_ray_tune pid=2376737)[0m [175]	train-mlogloss:1.83732	validation-mlogloss:1.84657
[36m(train_xgb_ray_tune pid=2376716)[0m [205]	train-mlogloss:2.01187	validation-mlogloss:2.01352
[36m(train_xgb_ray_tune pid=2376742)[0m [262]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376729)[0m [177]	train-mlogloss:1.75428	validation-mlogloss:1.79512
[36m(train_xgb_ray_tune pid=2376722)[0m [158]	train-mlogloss:1.78422	validation-mlogloss:1.82842
[36m(train_xgb_ray_tune pid=2376743)[0m [190]	train-mlogloss:2.15175	validation-mlogloss:2.16393
[36m(train_xgb_ray_tune pid=2376741)[0m [167]	train-mlogloss:1.85228	validation-mlogloss:1.88822
[36m(train_xgb_ray_tune pid=2376738)[0m [183]	train-mlogloss:1.92438	validation-mlogloss:1.92065
[36m(train_xgb_ray_tune pid=2376728)[0m [167]	train-mlogloss:1.80484	validation-mlogloss:1.82270
[36m(train_xgb_ray_tune pid=2376750)[0m [149]	train-mlogloss:1.96970	validation-mlogloss:1.99312
[36m(train_xgb_ray_tune pid=2376740)[0m [249]	train-mlogloss:2.48581	validation-mlogloss:2.47939
[36m(train_xgb_ray_tune pid=2376739)[0m [158]	train-mlogloss:1.66244	validation-mlogloss:1.72480
[36m(train_xgb_ray_tune pid=2376749)[0m [192]	train-mlogloss:2.22638	validation-mlogloss:2.21416
[36m(train_xgb_ray_tune pid=2376736)[0m [197]	train-mlogloss:2.00748	validation-mlogloss:2.01567
[36m(train_xgb_ray_tune pid=2376735)[0m [192]	train-mlogloss:2.13259	validation-mlogloss:2.12089
[36m(train_xgb_ray_tune pid=2376745)[0m [190]	train-mlogloss:1.70736	validation-mlogloss:1.72787
[36m(train_xgb_ray_tune pid=2376742)[0m [263]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376746)[0m [157]	train-mlogloss:1.89568	validation-mlogloss:1.93248
[36m(train_xgb_ray_tune pid=2376729)[0m [178]	train-mlogloss:1.75243	validation-mlogloss:1.79330
[36m(train_xgb_ray_tune pid=2376740)[0m [250]	train-mlogloss:2.48554	validation-mlogloss:2.47910
[36m(train_xgb_ray_tune pid=2376747)[0m [139]	train-mlogloss:1.80272	validation-mlogloss:1.85440
[36m(train_xgb_ray_tune pid=2376745)[0m [191]	train-mlogloss:1.70581	validation-mlogloss:1.72630
[36m(train_xgb_ray_tune pid=2376748)[0m [183]	train-mlogloss:2.11833	validation-mlogloss:2.11186
[36m(train_xgb_ray_tune pid=2376737)[0m [176]	train-mlogloss:1.83545	validation-mlogloss:1.84467
[36m(train_xgb_ray_tune pid=2376736)[0m [198]	train-mlogloss:2.00557	validation-mlogloss:2.01384
[36m(train_xgb_ray_tune pid=2376716)[0m [206]	train-mlogloss:2.00993	validation-mlogloss:2.01169
[36m(train_xgb_ray_tune pid=2376742)[0m [264]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376729)[0m [179]	train-mlogloss:1.75060	validation-mlogloss:1.79164
[36m(train_xgb_ray_tune pid=2376722)[0m [159]	train-mlogloss:1.78172	validation-mlogloss:1.82614
[36m(train_xgb_ray_tune pid=2376743)[0m [191]	train-mlogloss:2.14988	validation-mlogloss:2.16208
[36m(train_xgb_ray_tune pid=2376741)[0m [168]	train-mlogloss:1.84992	validation-mlogloss:1.88597
[36m(train_xgb_ray_tune pid=2376738)[0m [184]	train-mlogloss:1.92250	validation-mlogloss:1.91873
[36m(train_xgb_ray_tune pid=2376728)[0m [168]	train-mlogloss:1.80234	validation-mlogloss:1.82032
[36m(train_xgb_ray_tune pid=2376750)[0m [150]	train-mlogloss:1.96705	validation-mlogloss:1.99062
[36m(train_xgb_ray_tune pid=2376735)[0m [193]	train-mlogloss:2.13077	validation-mlogloss:2.11909
[36m(train_xgb_ray_tune pid=2376740)[0m [251]	train-mlogloss:2.48554	validation-mlogloss:2.47910
[36m(train_xgb_ray_tune pid=2376739)[0m [159]	train-mlogloss:1.66006	validation-mlogloss:1.72274
[36m(train_xgb_ray_tune pid=2376749)[0m [193]	train-mlogloss:2.22462	validation-mlogloss:2.21236
[36m(train_xgb_ray_tune pid=2376745)[0m [192]	train-mlogloss:1.70426	validation-mlogloss:1.72481
[36m(train_xgb_ray_tune pid=2376748)[0m [184]	train-mlogloss:2.11630	validation-mlogloss:2.10988
[36m(train_xgb_ray_tune pid=2376737)[0m [177]	train-mlogloss:1.83341	validation-mlogloss:1.84276
[36m(train_xgb_ray_tune pid=2376736)[0m [199]	train-mlogloss:2.00360	validation-mlogloss:2.01187
[36m(train_xgb_ray_tune pid=2376716)[0m [207]	train-mlogloss:2.00809	validation-mlogloss:2.00996
[36m(train_xgb_ray_tune pid=2376742)[0m [265]	train-mlogloss:2.53080	validation-mlogloss:2.52446
[36m(train_xgb_ray_tune pid=2376729)[0m [180]	train-mlogloss:1.74872	validation-mlogloss:1.78993
[36m(train_xgb_ray_tune pid=2376722)[0m [160]	train-mlogloss:1.77921	validation-mlogloss:1.82371
[36m(train_xgb_ray_tune pid=2376743)[0m [192]	train-mlogloss:2.14800	validation-mlogloss:2.16027
[36m(train_xgb_ray_tune pid=2376746)[0m [158]	train-mlogloss:1.89311	validation-mlogloss:1.92994
[36m(train_xgb_ray_tune pid=2376741)[0m [169]	train-mlogloss:1.84749	validation-mlogloss:1.88380
[36m(train_xgb_ray_tune pid=2376747)[0m [140]	train-mlogloss:1.79993	validation-mlogloss:1.85190
[36m(train_xgb_ray_tune pid=2376738)[0m [185]	train-mlogloss:1.92063	validation-mlogloss:1.91694
[36m(train_xgb_ray_tune pid=2376728)[0m [169]	train-mlogloss:1.79996	validation-mlogloss:1.81814
[36m(train_xgb_ray_tune pid=2376750)[0m [151]	train-mlogloss:1.96439	validation-mlogloss:1.98813
[36m(train_xgb_ray_tune pid=2376735)[0m [194]	train-mlogloss:2.12894	validation-mlogloss:2.11726
[36m(train_xgb_ray_tune pid=2376740)[0m [252]	train-mlogloss:2.48542	validation-mlogloss:2.47899
[36m(train_xgb_ray_tune pid=2376740)[0m [253]	train-mlogloss:2.48542	validation-mlogloss:2.47899
[36m(train_xgb_ray_tune pid=2376749)[0m [194]	train-mlogloss:2.22289	validation-mlogloss:2.21064
[36m(train_xgb_ray_tune pid=2376748)[0m [185]	train-mlogloss:2.11426	validation-mlogloss:2.10788
[36m(train_xgb_ray_tune pid=2376742)[0m [266]	train-mlogloss:2.53067	validation-mlogloss:2.52434
[36m(train_xgb_ray_tune pid=2376739)[0m [160]	train-mlogloss:1.65764	validation-mlogloss:1.72044
[36m(train_xgb_ray_tune pid=2376745)[0m [193]	train-mlogloss:1.70262	validation-mlogloss:1.72344
[36m(train_xgb_ray_tune pid=2376737)[0m [178]	train-mlogloss:1.83159	validation-mlogloss:1.84085
[36m(train_xgb_ray_tune pid=2376736)[0m [200]	train-mlogloss:2.00168	validation-mlogloss:2.00999
[36m(train_xgb_ray_tune pid=2376716)[0m [208]	train-mlogloss:2.00636	validation-mlogloss:2.00827
[36m(train_xgb_ray_tune pid=2376742)[0m [267]	train-mlogloss:2.53067	validation-mlogloss:2.52434
[36m(train_xgb_ray_tune pid=2376729)[0m [181]	train-mlogloss:1.74693	validation-mlogloss:1.78823
[36m(train_xgb_ray_tune pid=2376722)[0m [161]	train-mlogloss:1.77675	validation-mlogloss:1.82141
[36m(train_xgb_ray_tune pid=2376743)[0m [193]	train-mlogloss:2.14613	validation-mlogloss:2.15840
[36m(train_xgb_ray_tune pid=2376746)[0m [159]	train-mlogloss:1.89053	validation-mlogloss:1.92746
[36m(train_xgb_ray_tune pid=2376741)[0m [170]	train-mlogloss:1.84507	validation-mlogloss:1.88158
[36m(train_xgb_ray_tune pid=2376747)[0m [141]	train-mlogloss:1.79712	validation-mlogloss:1.84939
[36m(train_xgb_ray_tune pid=2376738)[0m [186]	train-mlogloss:1.91871	validation-mlogloss:1.91507
[36m(train_xgb_ray_tune pid=2376728)[0m [170]	train-mlogloss:1.79764	validation-mlogloss:1.81596
[36m(train_xgb_ray_tune pid=2376750)[0m [152]	train-mlogloss:1.96200	validation-mlogloss:1.98570
[36m(train_xgb_ray_tune pid=2376735)[0m [195]	train-mlogloss:2.12707	validation-mlogloss:2.11532
[36m(train_xgb_ray_tune pid=2376740)[0m [254]	train-mlogloss:2.48531	validation-mlogloss:2.47886
[36m(train_xgb_ray_tune pid=2376740)[0m [255]	train-mlogloss:2.48505	validation-mlogloss:2.47863
[36m(train_xgb_ray_tune pid=2376749)[0m [195]	train-mlogloss:2.22130	validation-mlogloss:2.20907
[36m(train_xgb_ray_tune pid=2376748)[0m [186]	train-mlogloss:2.11225	validation-mlogloss:2.10582
[36m(train_xgb_ray_tune pid=2376745)[0m [194]	train-mlogloss:1.70106	validation-mlogloss:1.72179
[36m(train_xgb_ray_tune pid=2376737)[0m [179]	train-mlogloss:1.82969	validation-mlogloss:1.83909
[36m(train_xgb_ray_tune pid=2376736)[0m [201]	train-mlogloss:1.99993	validation-mlogloss:2.00819
[36m(train_xgb_ray_tune pid=2376716)[0m [209]	train-mlogloss:2.00449	validation-mlogloss:2.00647
[36m(train_xgb_ray_tune pid=2376744)[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=2376744)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=2376747)[0m 2025-11-07 10:17:50.394170: 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=2376747)[0m 2025-11-07 10:17:50.414479: 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=2376747)[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=2376747)[0m E0000 00:00:1762507070.443217 2377997 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=2376747)[0m E0000 00:00:1762507070.451789 2377997 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=2376747)[0m W0000 00:00:1762507070.471829 2377997 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=2376747)[0m 2025-11-07 10:17:50.477748: 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=2376747)[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=2376742)[0m [268]	train-mlogloss:2.53067	validation-mlogloss:2.52434
[36m(train_xgb_ray_tune pid=2376729)[0m [182]	train-mlogloss:1.74519	validation-mlogloss:1.78668
[36m(train_xgb_ray_tune pid=2376743)[0m [194]	train-mlogloss:2.14434	validation-mlogloss:2.15662
[36m(train_xgb_ray_tune pid=2376746)[0m [160]	train-mlogloss:1.88802	validation-mlogloss:1.92510
[36m(train_xgb_ray_tune pid=2376741)[0m [171]	train-mlogloss:1.84275	validation-mlogloss:1.87935
[36m(train_xgb_ray_tune pid=2376747)[0m [142]	train-mlogloss:1.79436	validation-mlogloss:1.84689
[36m(train_xgb_ray_tune pid=2376738)[0m [187]	train-mlogloss:1.91689	validation-mlogloss:1.91338
[36m(train_xgb_ray_tune pid=2376728)[0m [171]	train-mlogloss:1.79527	validation-mlogloss:1.81370
[36m(train_xgb_ray_tune pid=2376735)[0m [196]	train-mlogloss:2.12523	validation-mlogloss:2.11339
[36m(train_xgb_ray_tune pid=2376740)[0m [256]	train-mlogloss:2.48494	validation-mlogloss:2.47851
[36m(train_xgb_ray_tune pid=2376739)[0m [161]	train-mlogloss:1.65533	validation-mlogloss:1.71837
[36m(train_xgb_ray_tune pid=2376748)[0m [187]	train-mlogloss:2.11029	validation-mlogloss:2.10381
[36m(train_xgb_ray_tune pid=2376742)[0m [269]	train-mlogloss:2.53067	validation-mlogloss:2.52434
[36m(train_xgb_ray_tune pid=2376722)[0m [162]	train-mlogloss:1.77427	validation-mlogloss:1.81913
[36m(train_xgb_ray_tune pid=2376740)[0m [257]	train-mlogloss:2.48494	validation-mlogloss:2.47851
[36m(train_xgb_ray_tune pid=2376749)[0m [196]	train-mlogloss:2.21965	validation-mlogloss:2.20737
[36m(train_xgb_ray_tune pid=2376750)[0m [153]	train-mlogloss:1.95938	validation-mlogloss:1.98328
[36m(train_xgb_ray_tune pid=2376716)[0m [210]	train-mlogloss:2.00258	validation-mlogloss:2.00465
[36m(train_xgb_ray_tune pid=2376738)[0m [188]	train-mlogloss:1.91508	validation-mlogloss:1.91154
[36m(train_xgb_ray_tune pid=2376745)[0m [195]	train-mlogloss:1.69972	validation-mlogloss:1.72047
[36m(train_xgb_ray_tune pid=2376737)[0m [180]	train-mlogloss:1.82770	validation-mlogloss:1.83724
[36m(train_xgb_ray_tune pid=2376736)[0m [202]	train-mlogloss:1.99802	validation-mlogloss:2.00636
[36m(train_xgb_ray_tune pid=2376742)[0m [270]	train-mlogloss:2.53067	validation-mlogloss:2.52434
[36m(train_xgb_ray_tune pid=2376729)[0m [183]	train-mlogloss:1.74332	validation-mlogloss:1.78500
[36m(train_xgb_ray_tune pid=2376743)[0m [195]	train-mlogloss:2.14260	validation-mlogloss:2.15494
[36m(train_xgb_ray_tune pid=2376746)[0m [161]	train-mlogloss:1.88561	validation-mlogloss:1.92287
[36m(train_xgb_ray_tune pid=2376741)[0m [172]	train-mlogloss:1.84044	validation-mlogloss:1.87728
[36m(train_xgb_ray_tune pid=2376728)[0m [172]	train-mlogloss:1.79290	validation-mlogloss:1.81139
[36m(train_xgb_ray_tune pid=2376735)[0m [197]	train-mlogloss:2.12322	validation-mlogloss:2.11147
[36m(train_xgb_ray_tune pid=2376740)[0m [258]	train-mlogloss:2.48482	validation-mlogloss:2.47839
[36m(train_xgb_ray_tune pid=2376739)[0m [162]	train-mlogloss:1.65297	validation-mlogloss:1.71629
[36m(train_xgb_ray_tune pid=2376748)[0m [188]	train-mlogloss:2.10841	validation-mlogloss:2.10192
[36m(train_xgb_ray_tune pid=2376738)[0m [189]	train-mlogloss:1.91318	validation-mlogloss:1.90978
[36m(train_xgb_ray_tune pid=2376749)[0m [197]	train-mlogloss:2.21799	validation-mlogloss:2.20568
[36m(train_xgb_ray_tune pid=2376716)[0m [211]	train-mlogloss:2.00079	validation-mlogloss:2.00297
[36m(train_xgb_ray_tune pid=2376747)[0m [143]	train-mlogloss:1.79153	validation-mlogloss:1.84441
[36m(train_xgb_ray_tune pid=2376740)[0m [259]	train-mlogloss:2.48474	validation-mlogloss:2.47829

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:22. Total running time: 36s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             33.3087 │
│ time_total_s                 33.3087 │
│ training_iteration                 1 │
│ validation_accuracy          0.13162 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:22. Total running time: 36s
[36m(train_xgb_ray_tune pid=2376745)[0m [196]	train-mlogloss:1.69834	validation-mlogloss:1.71911
[36m(train_xgb_ray_tune pid=2376748)[0m [189]	train-mlogloss:2.10653	validation-mlogloss:2.10000
[36m(train_xgb_ray_tune pid=2376737)[0m [181]	train-mlogloss:1.82578	validation-mlogloss:1.83542
[36m(train_xgb_ray_tune pid=2376736)[0m [203]	train-mlogloss:1.99630	validation-mlogloss:2.00472
[36m(train_xgb_ray_tune pid=2376742)[0m [271]	train-mlogloss:2.53067	validation-mlogloss:2.52434
[36m(train_xgb_ray_tune pid=2376729)[0m [184]	train-mlogloss:1.74154	validation-mlogloss:1.78329
[36m(train_xgb_ray_tune pid=2376722)[0m [163]	train-mlogloss:1.77194	validation-mlogloss:1.81690
[36m(train_xgb_ray_tune pid=2376743)[0m [196]	train-mlogloss:2.14083	validation-mlogloss:2.15319
[36m(train_xgb_ray_tune pid=2376741)[0m [173]	train-mlogloss:1.83808	validation-mlogloss:1.87501
[36m(train_xgb_ray_tune pid=2376728)[0m [173]	train-mlogloss:1.79069	validation-mlogloss:1.80919
[36m(train_xgb_ray_tune pid=2376750)[0m [154]	train-mlogloss:1.95695	validation-mlogloss:1.98079
[36m(train_xgb_ray_tune pid=2376735)[0m [198]	train-mlogloss:2.12141	validation-mlogloss:2.10958
[36m(train_xgb_ray_tune pid=2376740)[0m [260]	train-mlogloss:2.48448	validation-mlogloss:2.47801
[36m(train_xgb_ray_tune pid=2376739)[0m [163]	train-mlogloss:1.65059	validation-mlogloss:1.71438
[36m(train_xgb_ray_tune pid=2376716)[0m [212]	train-mlogloss:1.99896	validation-mlogloss:2.00121
[36m(train_xgb_ray_tune pid=2376742)[0m [272]	train-mlogloss:2.53067	validation-mlogloss:2.52434
[36m(train_xgb_ray_tune pid=2376746)[0m [162]	train-mlogloss:1.88317	validation-mlogloss:1.92057
[36m(train_xgb_ray_tune pid=2376738)[0m [190]	train-mlogloss:1.91130	validation-mlogloss:1.90791
[36m(train_xgb_ray_tune pid=2376749)[0m [198]	train-mlogloss:2.21632	validation-mlogloss:2.20396
[36m(train_xgb_ray_tune pid=2376745)[0m [197]	train-mlogloss:1.69683	validation-mlogloss:1.71766
[36m(train_xgb_ray_tune pid=2376748)[0m [190]	train-mlogloss:2.10459	validation-mlogloss:2.09815
[36m(train_xgb_ray_tune pid=2376737)[0m [182]	train-mlogloss:1.82396	validation-mlogloss:1.83366
[36m(train_xgb_ray_tune pid=2376737)[0m [183]	train-mlogloss:1.82226	validation-mlogloss:1.83190
[36m(train_xgb_ray_tune pid=2376736)[0m [204]	train-mlogloss:1.99459	validation-mlogloss:2.00298
[36m(train_xgb_ray_tune pid=2376742)[0m [273]	train-mlogloss:2.53067	validation-mlogloss:2.52434
[36m(train_xgb_ray_tune pid=2376729)[0m [185]	train-mlogloss:1.73979	validation-mlogloss:1.78176
[36m(train_xgb_ray_tune pid=2376722)[0m [164]	train-mlogloss:1.76958	validation-mlogloss:1.81471
[36m(train_xgb_ray_tune pid=2376743)[0m [197]	train-mlogloss:2.13908	validation-mlogloss:2.15149
[36m(train_xgb_ray_tune pid=2376741)[0m [174]	train-mlogloss:1.83571	validation-mlogloss:1.87265
[36m(train_xgb_ray_tune pid=2376747)[0m [144]	train-mlogloss:1.78874	validation-mlogloss:1.84189
[36m(train_xgb_ray_tune pid=2376738)[0m [191]	train-mlogloss:1.90944	validation-mlogloss:1.90620
[36m(train_xgb_ray_tune pid=2376728)[0m [174]	train-mlogloss:1.78841	validation-mlogloss:1.80706
[36m(train_xgb_ray_tune pid=2376750)[0m [155]	train-mlogloss:1.95451	validation-mlogloss:1.97840
[36m(train_xgb_ray_tune pid=2376735)[0m [199]	train-mlogloss:2.11960	validation-mlogloss:2.10773
[36m(train_xgb_ray_tune pid=2376740)[0m [261]	train-mlogloss:2.48413	validation-mlogloss:2.47767
[36m(train_xgb_ray_tune pid=2376739)[0m [164]	train-mlogloss:1.64828	validation-mlogloss:1.71238
[36m(train_xgb_ray_tune pid=2376749)[0m [199]	train-mlogloss:2.21464	validation-mlogloss:2.20218
[36m(train_xgb_ray_tune pid=2376716)[0m [213]	train-mlogloss:1.99721	validation-mlogloss:1.99950
[36m(train_xgb_ray_tune pid=2376745)[0m [198]	train-mlogloss:1.69553	validation-mlogloss:1.71653
[36m(train_xgb_ray_tune pid=2376748)[0m [191]	train-mlogloss:2.10269	validation-mlogloss:2.09616
[36m(train_xgb_ray_tune pid=2376737)[0m [184]	train-mlogloss:1.82058	validation-mlogloss:1.83031
[36m(train_xgb_ray_tune pid=2376736)[0m [205]	train-mlogloss:1.99279	validation-mlogloss:2.00116
[36m(train_xgb_ray_tune pid=2376716)[0m [214]	train-mlogloss:1.99551	validation-mlogloss:1.99784
[36m(train_xgb_ray_tune pid=2376742)[0m [274]	train-mlogloss:2.53055	validation-mlogloss:2.52422
[36m(train_xgb_ray_tune pid=2376729)[0m [186]	train-mlogloss:1.73804	validation-mlogloss:1.78007
[36m(train_xgb_ray_tune pid=2376722)[0m [165]	train-mlogloss:1.76712	validation-mlogloss:1.81243
[36m(train_xgb_ray_tune pid=2376743)[0m [198]	train-mlogloss:2.13733	validation-mlogloss:2.14970
[36m(train_xgb_ray_tune pid=2376746)[0m [163]	train-mlogloss:1.88070	validation-mlogloss:1.91818
[36m(train_xgb_ray_tune pid=2376741)[0m [175]	train-mlogloss:1.83353	validation-mlogloss:1.87051
[36m(train_xgb_ray_tune pid=2376747)[0m [145]	train-mlogloss:1.78595	validation-mlogloss:1.83917
[36m(train_xgb_ray_tune pid=2376738)[0m [192]	train-mlogloss:1.90767	validation-mlogloss:1.90436
[36m(train_xgb_ray_tune pid=2376728)[0m [175]	train-mlogloss:1.78611	validation-mlogloss:1.80500
[36m(train_xgb_ray_tune pid=2376735)[0m [200]	train-mlogloss:2.11784	validation-mlogloss:2.10591
[36m(train_xgb_ray_tune pid=2376740)[0m [262]	train-mlogloss:2.48404	validation-mlogloss:2.47759
[36m(train_xgb_ray_tune pid=2376739)[0m [165]	train-mlogloss:1.64603	validation-mlogloss:1.71052
[36m(train_xgb_ray_tune pid=2376749)[0m [200]	train-mlogloss:2.21307	validation-mlogloss:2.20053
[36m(train_xgb_ray_tune pid=2376750)[0m [156]	train-mlogloss:1.95213	validation-mlogloss:1.97625
[36m(train_xgb_ray_tune pid=2376740)[0m [263]	train-mlogloss:2.48404	validation-mlogloss:2.47759
[36m(train_xgb_ray_tune pid=2376742)[0m [275]	train-mlogloss:2.53038	validation-mlogloss:2.52406
[36m(train_xgb_ray_tune pid=2376745)[0m [199]	train-mlogloss:1.69406	validation-mlogloss:1.71535
[36m(train_xgb_ray_tune pid=2376748)[0m [192]	train-mlogloss:2.10076	validation-mlogloss:2.09431
[36m(train_xgb_ray_tune pid=2376748)[0m [193]	train-mlogloss:2.09890	validation-mlogloss:2.09237
[36m(train_xgb_ray_tune pid=2376737)[0m [185]	train-mlogloss:1.81859	validation-mlogloss:1.82844
[36m(train_xgb_ray_tune pid=2376736)[0m [206]	train-mlogloss:1.99093	validation-mlogloss:1.99931
[36m(train_xgb_ray_tune pid=2376716)[0m [215]	train-mlogloss:1.99383	validation-mlogloss:1.99620
[36m(train_xgb_ray_tune pid=2376742)[0m [276]	train-mlogloss:2.53015	validation-mlogloss:2.52382
[36m(train_xgb_ray_tune pid=2376729)[0m [187]	train-mlogloss:1.73638	validation-mlogloss:1.77845
[36m(train_xgb_ray_tune pid=2376722)[0m [166]	train-mlogloss:1.76481	validation-mlogloss:1.81003
[36m(train_xgb_ray_tune pid=2376743)[0m [199]	train-mlogloss:2.13555	validation-mlogloss:2.14791
[36m(train_xgb_ray_tune pid=2376746)[0m [164]	train-mlogloss:1.87827	validation-mlogloss:1.91593
[36m(train_xgb_ray_tune pid=2376741)[0m [176]	train-mlogloss:1.83114	validation-mlogloss:1.86837
[36m(train_xgb_ray_tune pid=2376747)[0m [146]	train-mlogloss:1.78327	validation-mlogloss:1.83659
[36m(train_xgb_ray_tune pid=2376738)[0m [193]	train-mlogloss:1.90574	validation-mlogloss:1.90248
[36m(train_xgb_ray_tune pid=2376728)[0m [176]	train-mlogloss:1.78383	validation-mlogloss:1.80289
[36m(train_xgb_ray_tune pid=2376735)[0m [201]	train-mlogloss:2.11599	validation-mlogloss:2.10411
[36m(train_xgb_ray_tune pid=2376740)[0m [264]	train-mlogloss:2.48369	validation-mlogloss:2.47722
[36m(train_xgb_ray_tune pid=2376739)[0m [166]	train-mlogloss:1.64369	validation-mlogloss:1.70853
[36m(train_xgb_ray_tune pid=2376749)[0m [201]	train-mlogloss:2.21159	validation-mlogloss:2.19910
[36m(train_xgb_ray_tune pid=2376745)[0m [200]	train-mlogloss:1.69266	validation-mlogloss:1.71392
[36m(train_xgb_ray_tune pid=2376737)[0m [186]	train-mlogloss:1.81678	validation-mlogloss:1.82673
[36m(train_xgb_ray_tune pid=2376736)[0m [207]	train-mlogloss:1.98912	validation-mlogloss:1.99757
[36m(train_xgb_ray_tune pid=2376716)[0m [216]	train-mlogloss:1.99196	validation-mlogloss:1.99441
[36m(train_xgb_ray_tune pid=2376742)[0m [277]	train-mlogloss:2.53015	validation-mlogloss:2.52382
[36m(train_xgb_ray_tune pid=2376742)[0m [278]	train-mlogloss:2.52998	validation-mlogloss:2.52366
[36m(train_xgb_ray_tune pid=2376729)[0m [188]	train-mlogloss:1.73463	validation-mlogloss:1.77682
[36m(train_xgb_ray_tune pid=2376743)[0m [200]	train-mlogloss:2.13387	validation-mlogloss:2.14626
[36m(train_xgb_ray_tune pid=2376746)[0m [165]	train-mlogloss:1.87582	validation-mlogloss:1.91359
[36m(train_xgb_ray_tune pid=2376741)[0m [177]	train-mlogloss:1.82880	validation-mlogloss:1.86635
[36m(train_xgb_ray_tune pid=2376747)[0m [147]	train-mlogloss:1.78059	validation-mlogloss:1.83398
[36m(train_xgb_ray_tune pid=2376738)[0m [194]	train-mlogloss:1.90391	validation-mlogloss:1.90061
[36m(train_xgb_ray_tune pid=2376728)[0m [177]	train-mlogloss:1.78143	validation-mlogloss:1.80063
[36m(train_xgb_ray_tune pid=2376750)[0m [157]	train-mlogloss:1.94979	validation-mlogloss:1.97394
[36m(train_xgb_ray_tune pid=2376735)[0m [202]	train-mlogloss:2.11412	validation-mlogloss:2.10232
[36m(train_xgb_ray_tune pid=2376740)[0m [265]	train-mlogloss:2.48341	validation-mlogloss:2.47693
[36m(train_xgb_ray_tune pid=2376739)[0m [167]	train-mlogloss:1.64139	validation-mlogloss:1.70675
[36m(train_xgb_ray_tune pid=2376749)[0m [202]	train-mlogloss:2.20999	validation-mlogloss:2.19747
[36m(train_xgb_ray_tune pid=2376745)[0m [201]	train-mlogloss:1.69116	validation-mlogloss:1.71264
[36m(train_xgb_ray_tune pid=2376748)[0m [194]	train-mlogloss:2.09703	validation-mlogloss:2.09040
[36m(train_xgb_ray_tune pid=2376722)[0m [167]	train-mlogloss:1.76258	validation-mlogloss:1.80773
[36m(train_xgb_ray_tune pid=2376750)[0m [158]	train-mlogloss:1.94727	validation-mlogloss:1.97158
[36m(train_xgb_ray_tune pid=2376743)[0m [201]	train-mlogloss:2.13206	validation-mlogloss:2.14445
[36m(train_xgb_ray_tune pid=2376737)[0m [187]	train-mlogloss:1.81494	validation-mlogloss:1.82510
[36m(train_xgb_ray_tune pid=2376736)[0m [208]	train-mlogloss:1.98730	validation-mlogloss:1.99572
[36m(train_xgb_ray_tune pid=2376716)[0m [217]	train-mlogloss:1.99025	validation-mlogloss:1.99266
[36m(train_xgb_ray_tune pid=2376742)[0m [279]	train-mlogloss:2.52998	validation-mlogloss:2.52366
[36m(train_xgb_ray_tune pid=2376729)[0m [189]	train-mlogloss:1.73293	validation-mlogloss:1.77514
[36m(train_xgb_ray_tune pid=2376743)[0m [202]	train-mlogloss:2.13024	validation-mlogloss:2.14268
[36m(train_xgb_ray_tune pid=2376746)[0m [166]	train-mlogloss:1.87338	validation-mlogloss:1.91132
[36m(train_xgb_ray_tune pid=2376741)[0m [178]	train-mlogloss:1.82655	validation-mlogloss:1.86417
[36m(train_xgb_ray_tune pid=2376747)[0m [148]	train-mlogloss:1.77795	validation-mlogloss:1.83148
[36m(train_xgb_ray_tune pid=2376738)[0m [195]	train-mlogloss:1.90203	validation-mlogloss:1.89879
[36m(train_xgb_ray_tune pid=2376728)[0m [178]	train-mlogloss:1.77917	validation-mlogloss:1.79857
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[36m(train_xgb_ray_tune pid=2376728)[0m [217]	train-mlogloss:1.70382	validation-mlogloss:1.72797
[36m(train_xgb_ray_tune pid=2376750)[0m [195]	train-mlogloss:1.86810	validation-mlogloss:1.89459
[36m(train_xgb_ray_tune pid=2376735)[0m [249]	train-mlogloss:2.03632	validation-mlogloss:2.02445
[36m(train_xgb_ray_tune pid=2376740)[0m [325]	train-mlogloss:2.47992	validation-mlogloss:2.47337
[36m(train_xgb_ray_tune pid=2376739)[0m [208]	train-mlogloss:1.56239	validation-mlogloss:1.63890
[36m(train_xgb_ray_tune pid=2376749)[0m [250]	train-mlogloss:2.13855	validation-mlogloss:2.12491
[36m(train_xgb_ray_tune pid=2376742)[0m [344]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376745)[0m [248]	train-mlogloss:1.63603	validation-mlogloss:1.66144
[36m(train_xgb_ray_tune pid=2376736)[0m [254]	train-mlogloss:1.91403	validation-mlogloss:1.92398
[36m(train_xgb_ray_tune pid=2376738)[0m [239]	train-mlogloss:1.83357	validation-mlogloss:1.83259
[36m(train_xgb_ray_tune pid=2376728)[0m [218]	train-mlogloss:1.70212	validation-mlogloss:1.72623
[36m(train_xgb_ray_tune pid=2376735)[0m [250]	train-mlogloss:2.03476	validation-mlogloss:2.02286
[36m(train_xgb_ray_tune pid=2376749)[0m [251]	train-mlogloss:2.13719	validation-mlogloss:2.12352
[36m(train_xgb_ray_tune pid=2376745)[0m [249]	train-mlogloss:1.63504	validation-mlogloss:1.66059
[36m(train_xgb_ray_tune pid=2376748)[0m [240]	train-mlogloss:2.01791	validation-mlogloss:2.01239
[36m(train_xgb_ray_tune pid=2376737)[0m [230]	train-mlogloss:1.74897	validation-mlogloss:1.76253
[36m(train_xgb_ray_tune pid=2376736)[0m [255]	train-mlogloss:1.91262	validation-mlogloss:1.92266
[36m(train_xgb_ray_tune pid=2376716)[0m [265]	train-mlogloss:1.91562	validation-mlogloss:1.92079
[36m(train_xgb_ray_tune pid=2376742)[0m [345]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376729)[0m [234]	train-mlogloss:1.66809	validation-mlogloss:1.71462
[36m(train_xgb_ray_tune pid=2376722)[0m [206]	train-mlogloss:1.68354	validation-mlogloss:1.73465
[36m(train_xgb_ray_tune pid=2376743)[0m [248]	train-mlogloss:2.05786	validation-mlogloss:2.07164
[36m(train_xgb_ray_tune pid=2376746)[0m [203]	train-mlogloss:1.79285	validation-mlogloss:1.83600
[36m(train_xgb_ray_tune pid=2376741)[0m [219]	train-mlogloss:1.74639	validation-mlogloss:1.78792
[36m(train_xgb_ray_tune pid=2376747)[0m [182]	train-mlogloss:1.69820	validation-mlogloss:1.75771
[36m(train_xgb_ray_tune pid=2376738)[0m [240]	train-mlogloss:1.83221	validation-mlogloss:1.83124
[36m(train_xgb_ray_tune pid=2376728)[0m [219]	train-mlogloss:1.70042	validation-mlogloss:1.72460
[36m(train_xgb_ray_tune pid=2376750)[0m [196]	train-mlogloss:1.86615	validation-mlogloss:1.89272
[36m(train_xgb_ray_tune pid=2376740)[0m [326]	train-mlogloss:2.47976	validation-mlogloss:2.47320
[36m(train_xgb_ray_tune pid=2376740)[0m [327]	train-mlogloss:2.47976	validation-mlogloss:2.47320
[36m(train_xgb_ray_tune pid=2376749)[0m [252]	train-mlogloss:2.13583	validation-mlogloss:2.12213
[36m(train_xgb_ray_tune pid=2376742)[0m [346]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376737)[0m [231]	train-mlogloss:1.74765	validation-mlogloss:1.76132
[36m(train_xgb_ray_tune pid=2376716)[0m [266]	train-mlogloss:1.91425	validation-mlogloss:1.91949
[36m(train_xgb_ray_tune pid=2376735)[0m [251]	train-mlogloss:2.03322	validation-mlogloss:2.02138
[36m(train_xgb_ray_tune pid=2376748)[0m [241]	train-mlogloss:2.01643	validation-mlogloss:2.01099
[36m(train_xgb_ray_tune pid=2376741)[0m [220]	train-mlogloss:1.74467	validation-mlogloss:1.78629
[36m(train_xgb_ray_tune pid=2376729)[0m [235]	train-mlogloss:1.66687	validation-mlogloss:1.71350
[36m(train_xgb_ray_tune pid=2376745)[0m [250]	train-mlogloss:1.63403	validation-mlogloss:1.65966
[36m(train_xgb_ray_tune pid=2376743)[0m [249]	train-mlogloss:2.05646	validation-mlogloss:2.07018
[36m(train_xgb_ray_tune pid=2376747)[0m [183]	train-mlogloss:1.69614	validation-mlogloss:1.75569
[36m(train_xgb_ray_tune pid=2376748)[0m [242]	train-mlogloss:2.01487	validation-mlogloss:2.00944
[36m(train_xgb_ray_tune pid=2376737)[0m [232]	train-mlogloss:1.74638	validation-mlogloss:1.76014
[36m(train_xgb_ray_tune pid=2376736)[0m [256]	train-mlogloss:1.91125	validation-mlogloss:1.92121
[36m(train_xgb_ray_tune pid=2376736)[0m [257]	train-mlogloss:1.90989	validation-mlogloss:1.91991
[36m(train_xgb_ray_tune pid=2376716)[0m [267]	train-mlogloss:1.91287	validation-mlogloss:1.91816
[36m(train_xgb_ray_tune pid=2376742)[0m [347]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376742)[0m [348]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376729)[0m [236]	train-mlogloss:1.66567	validation-mlogloss:1.71230
[36m(train_xgb_ray_tune pid=2376722)[0m [207]	train-mlogloss:1.68175	validation-mlogloss:1.73304
[36m(train_xgb_ray_tune pid=2376746)[0m [204]	train-mlogloss:1.79091	validation-mlogloss:1.83405
[36m(train_xgb_ray_tune pid=2376741)[0m [221]	train-mlogloss:1.74292	validation-mlogloss:1.78457
[36m(train_xgb_ray_tune pid=2376738)[0m [241]	train-mlogloss:1.83086	validation-mlogloss:1.83004
[36m(train_xgb_ray_tune pid=2376728)[0m [220]	train-mlogloss:1.69887	validation-mlogloss:1.72324
[36m(train_xgb_ray_tune pid=2376750)[0m [197]	train-mlogloss:1.86427	validation-mlogloss:1.89089
[36m(train_xgb_ray_tune pid=2376735)[0m [252]	train-mlogloss:2.03174	validation-mlogloss:2.01989
[36m(train_xgb_ray_tune pid=2376740)[0m [328]	train-mlogloss:2.47976	validation-mlogloss:2.47320
[36m(train_xgb_ray_tune pid=2376740)[0m [329]	train-mlogloss:2.47976	validation-mlogloss:2.47320
[36m(train_xgb_ray_tune pid=2376749)[0m [253]	train-mlogloss:2.13444	validation-mlogloss:2.12074
[36m(train_xgb_ray_tune pid=2376743)[0m [250]	train-mlogloss:2.05506	validation-mlogloss:2.06882
[36m(train_xgb_ray_tune pid=2376748)[0m [243]	train-mlogloss:2.01339	validation-mlogloss:2.00795
[36m(train_xgb_ray_tune pid=2376737)[0m [233]	train-mlogloss:1.74502	validation-mlogloss:1.75878
[36m(train_xgb_ray_tune pid=2376736)[0m [258]	train-mlogloss:1.90847	validation-mlogloss:1.91849
[36m(train_xgb_ray_tune pid=2376716)[0m [268]	train-mlogloss:1.91156	validation-mlogloss:1.91688
[36m(train_xgb_ray_tune pid=2376742)[0m [349]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376742)[0m [350]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376729)[0m [237]	train-mlogloss:1.66444	validation-mlogloss:1.71113
[36m(train_xgb_ray_tune pid=2376722)[0m [208]	train-mlogloss:1.68010	validation-mlogloss:1.73139
[36m(train_xgb_ray_tune pid=2376743)[0m [251]	train-mlogloss:2.05366	validation-mlogloss:2.06746
[36m(train_xgb_ray_tune pid=2376746)[0m [205]	train-mlogloss:1.78895	validation-mlogloss:1.83221
[36m(train_xgb_ray_tune pid=2376741)[0m [222]	train-mlogloss:1.74131	validation-mlogloss:1.78297
[36m(train_xgb_ray_tune pid=2376747)[0m [184]	train-mlogloss:1.69409	validation-mlogloss:1.75380
[36m(train_xgb_ray_tune pid=2376738)[0m [242]	train-mlogloss:1.82953	validation-mlogloss:1.82869
[36m(train_xgb_ray_tune pid=2376728)[0m [221]	train-mlogloss:1.69710	validation-mlogloss:1.72163
[36m(train_xgb_ray_tune pid=2376750)[0m [198]	train-mlogloss:1.86240	validation-mlogloss:1.88910
[36m(train_xgb_ray_tune pid=2376735)[0m [253]	train-mlogloss:2.03022	validation-mlogloss:2.01845
[36m(train_xgb_ray_tune pid=2376740)[0m [330]	train-mlogloss:2.47960	validation-mlogloss:2.47303
[36m(train_xgb_ray_tune pid=2376749)[0m [254]	train-mlogloss:2.13308	validation-mlogloss:2.11944
[36m(train_xgb_ray_tune pid=2376748)[0m [244]	train-mlogloss:2.01177	validation-mlogloss:2.00645
[36m(train_xgb_ray_tune pid=2376737)[0m [234]	train-mlogloss:1.74374	validation-mlogloss:1.75758
[36m(train_xgb_ray_tune pid=2376736)[0m [259]	train-mlogloss:1.90705	validation-mlogloss:1.91711
[36m(train_xgb_ray_tune pid=2376716)[0m [269]	train-mlogloss:1.91017	validation-mlogloss:1.91558
[36m(train_xgb_ray_tune pid=2376716)[0m [270]	train-mlogloss:1.90878	validation-mlogloss:1.91424
[36m(train_xgb_ray_tune pid=2376742)[0m [351]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376729)[0m [238]	train-mlogloss:1.66334	validation-mlogloss:1.71017
[36m(train_xgb_ray_tune pid=2376722)[0m [209]	train-mlogloss:1.67837	validation-mlogloss:1.72985
[36m(train_xgb_ray_tune pid=2376743)[0m [252]	train-mlogloss:2.05228	validation-mlogloss:2.06607
[36m(train_xgb_ray_tune pid=2376746)[0m [206]	train-mlogloss:1.78697	validation-mlogloss:1.83044
[36m(train_xgb_ray_tune pid=2376747)[0m [185]	train-mlogloss:1.69202	validation-mlogloss:1.75199
[36m(train_xgb_ray_tune pid=2376738)[0m [243]	train-mlogloss:1.82824	validation-mlogloss:1.82746
[36m(train_xgb_ray_tune pid=2376728)[0m [222]	train-mlogloss:1.69536	validation-mlogloss:1.72008
[36m(train_xgb_ray_tune pid=2376735)[0m [254]	train-mlogloss:2.02878	validation-mlogloss:2.01693
[36m(train_xgb_ray_tune pid=2376740)[0m [331]	train-mlogloss:2.47926	validation-mlogloss:2.47271
[36m(train_xgb_ray_tune pid=2376749)[0m [255]	train-mlogloss:2.13162	validation-mlogloss:2.11801
[36m(train_xgb_ray_tune pid=2376742)[0m [352]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376741)[0m [223]	train-mlogloss:1.73953	validation-mlogloss:1.78129
[36m(train_xgb_ray_tune pid=2376738)[0m [244]	train-mlogloss:1.82702	validation-mlogloss:1.82617
[36m(train_xgb_ray_tune pid=2376749)[0m [256]	train-mlogloss:2.13017	validation-mlogloss:2.11659
[36m(train_xgb_ray_tune pid=2376750)[0m [199]	train-mlogloss:1.86050	validation-mlogloss:1.88726
[36m(train_xgb_ray_tune pid=2376740)[0m [332]	train-mlogloss:2.47914	validation-mlogloss:2.47260
[36m(train_xgb_ray_tune pid=2376748)[0m [245]	train-mlogloss:2.01026	validation-mlogloss:2.00501
[36m(train_xgb_ray_tune pid=2376737)[0m [235]	train-mlogloss:1.74251	validation-mlogloss:1.75648
[36m(train_xgb_ray_tune pid=2376736)[0m [260]	train-mlogloss:1.90559	validation-mlogloss:1.91580
[36m(train_xgb_ray_tune pid=2376716)[0m [271]	train-mlogloss:1.90751	validation-mlogloss:1.91298
[36m(train_xgb_ray_tune pid=2376742)[0m [353]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376729)[0m [239]	train-mlogloss:1.66216	validation-mlogloss:1.70904
[36m(train_xgb_ray_tune pid=2376722)[0m [210]	train-mlogloss:1.67658	validation-mlogloss:1.72820
[36m(train_xgb_ray_tune pid=2376743)[0m [253]	train-mlogloss:2.05093	validation-mlogloss:2.06472
[36m(train_xgb_ray_tune pid=2376746)[0m [207]	train-mlogloss:1.78497	validation-mlogloss:1.82859
[36m(train_xgb_ray_tune pid=2376741)[0m [224]	train-mlogloss:1.73779	validation-mlogloss:1.77967
[36m(train_xgb_ray_tune pid=2376747)[0m [186]	train-mlogloss:1.68997	validation-mlogloss:1.75022
[36m(train_xgb_ray_tune pid=2376738)[0m [245]	train-mlogloss:1.82577	validation-mlogloss:1.82506
[36m(train_xgb_ray_tune pid=2376728)[0m [223]	train-mlogloss:1.69366	validation-mlogloss:1.71856
[36m(train_xgb_ray_tune pid=2376735)[0m [255]	train-mlogloss:2.02730	validation-mlogloss:2.01545
[36m(train_xgb_ray_tune pid=2376740)[0m [333]	train-mlogloss:2.47905	validation-mlogloss:2.47249
[36m(train_xgb_ray_tune pid=2376749)[0m [257]	train-mlogloss:2.12879	validation-mlogloss:2.11524
[36m(train_xgb_ray_tune pid=2376736)[0m [261]	train-mlogloss:1.90435	validation-mlogloss:1.91453
[36m(train_xgb_ray_tune pid=2376748)[0m [246]	train-mlogloss:2.00869	validation-mlogloss:2.00343
[36m(train_xgb_ray_tune pid=2376737)[0m [236]	train-mlogloss:1.74114	validation-mlogloss:1.75517
[36m(train_xgb_ray_tune pid=2376716)[0m [272]	train-mlogloss:1.90620	validation-mlogloss:1.91171
[36m(train_xgb_ray_tune pid=2376742)[0m [354]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376729)[0m [240]	train-mlogloss:1.66096	validation-mlogloss:1.70798
[36m(train_xgb_ray_tune pid=2376722)[0m [211]	train-mlogloss:1.67495	validation-mlogloss:1.72663
[36m(train_xgb_ray_tune pid=2376743)[0m [254]	train-mlogloss:2.04950	validation-mlogloss:2.06330
[36m(train_xgb_ray_tune pid=2376746)[0m [208]	train-mlogloss:1.78302	validation-mlogloss:1.82673
[36m(train_xgb_ray_tune pid=2376741)[0m [225]	train-mlogloss:1.73617	validation-mlogloss:1.77794
[36m(train_xgb_ray_tune pid=2376747)[0m [187]	train-mlogloss:1.68798	validation-mlogloss:1.74850
[36m(train_xgb_ray_tune pid=2376738)[0m [246]	train-mlogloss:1.82453	validation-mlogloss:1.82401
[36m(train_xgb_ray_tune pid=2376728)[0m [224]	train-mlogloss:1.69196	validation-mlogloss:1.71716
[36m(train_xgb_ray_tune pid=2376750)[0m [200]	train-mlogloss:1.85867	validation-mlogloss:1.88555
[36m(train_xgb_ray_tune pid=2376735)[0m [256]	train-mlogloss:2.02595	validation-mlogloss:2.01409
[36m(train_xgb_ray_tune pid=2376740)[0m [334]	train-mlogloss:2.47897	validation-mlogloss:2.47240
[36m(train_xgb_ray_tune pid=2376749)[0m [258]	train-mlogloss:2.12754	validation-mlogloss:2.11395
[36m(train_xgb_ray_tune pid=2376742)[0m [355]	train-mlogloss:2.52830	validation-mlogloss:2.52189
[36m(train_xgb_ray_tune pid=2376743)[0m [255]	train-mlogloss:2.04817	validation-mlogloss:2.06196
[36m(train_xgb_ray_tune pid=2376736)[0m [262]	train-mlogloss:1.90306	validation-mlogloss:1.91325
[36m(train_xgb_ray_tune pid=2376716)[0m [273]	train-mlogloss:1.90491	validation-mlogloss:1.91042
[36m(train_xgb_ray_tune pid=2376748)[0m [247]	train-mlogloss:2.00723	validation-mlogloss:2.00200
[36m(train_xgb_ray_tune pid=2376737)[0m [237]	train-mlogloss:1.73989	validation-mlogloss:1.75393
[36m(train_xgb_ray_tune pid=2376716)[0m [274]	train-mlogloss:1.90361	validation-mlogloss:1.90916
[36m(train_xgb_ray_tune pid=2376729)[0m [241]	train-mlogloss:1.65976	validation-mlogloss:1.70689
[36m(train_xgb_ray_tune pid=2376722)[0m [212]	train-mlogloss:1.67327	validation-mlogloss:1.72511
[36m(train_xgb_ray_tune pid=2376743)[0m [256]	train-mlogloss:2.04674	validation-mlogloss:2.06054
[36m(train_xgb_ray_tune pid=2376746)[0m [209]	train-mlogloss:1.78114	validation-mlogloss:1.82487
[36m(train_xgb_ray_tune pid=2376741)[0m [226]	train-mlogloss:1.73457	validation-mlogloss:1.77644
[36m(train_xgb_ray_tune pid=2376747)[0m [188]	train-mlogloss:1.68589	validation-mlogloss:1.74665
[36m(train_xgb_ray_tune pid=2376738)[0m [247]	train-mlogloss:1.82316	validation-mlogloss:1.82266
[36m(train_xgb_ray_tune pid=2376728)[0m [225]	train-mlogloss:1.69030	validation-mlogloss:1.71550
[36m(train_xgb_ray_tune pid=2376750)[0m [201]	train-mlogloss:1.85684	validation-mlogloss:1.88397
[36m(train_xgb_ray_tune pid=2376735)[0m [257]	train-mlogloss:2.02443	validation-mlogloss:2.01271
[36m(train_xgb_ray_tune pid=2376740)[0m [335]	train-mlogloss:2.47897	validation-mlogloss:2.47240
[36m(train_xgb_ray_tune pid=2376740)[0m [336]	train-mlogloss:2.47897	validation-mlogloss:2.47240
[36m(train_xgb_ray_tune pid=2376749)[0m [259]	train-mlogloss:2.12624	validation-mlogloss:2.11268
[36m(train_xgb_ray_tune pid=2376737)[0m [238]	train-mlogloss:1.73863	validation-mlogloss:1.75296
[36m(train_xgb_ray_tune pid=2376736)[0m [263]	train-mlogloss:1.90173	validation-mlogloss:1.91208

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:31. Total running time: 44s
[36m(train_xgb_ray_tune pid=2376739)[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=2376739)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=2376745)[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=2376745)[0m   _log_deprecation_warning(
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.5151 │
│ time_total_s                 41.5151 │
│ training_iteration                 1 │
│ validation_accuracy          0.46324 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=2376735)[0m [258]	train-mlogloss:2.02299	validation-mlogloss:2.01125
[36m(train_xgb_ray_tune pid=2376728)[0m [226]	train-mlogloss:1.68856	validation-mlogloss:1.71385
[36m(train_xgb_ray_tune pid=2376748)[0m [248]	train-mlogloss:2.00577	validation-mlogloss:2.00055
[36m(train_xgb_ray_tune pid=2376749)[0m [260]	train-mlogloss:2.12500	validation-mlogloss:2.11145

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:31. Total running time: 44s
[36m(train_xgb_ray_tune pid=2376738)[0m [248]	train-mlogloss:1.82187	validation-mlogloss:1.82143
[36m(train_xgb_ray_tune pid=2376743)[0m [257]	train-mlogloss:2.04527	validation-mlogloss:2.05917
[36m(train_xgb_ray_tune pid=2376746)[0m [210]	train-mlogloss:1.77916	validation-mlogloss:1.82303
[36m(train_xgb_ray_tune pid=2376722)[0m [213]	train-mlogloss:1.67163	validation-mlogloss:1.72357
[36m(train_xgb_ray_tune pid=2376750)[0m [202]	train-mlogloss:1.85497	validation-mlogloss:1.88218
[36m(train_xgb_ray_tune pid=2376740)[0m [337]	train-mlogloss:2.47886	validation-mlogloss:2.47229

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:31. Total running time: 44s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             41.8084 │
│ time_total_s                 41.8084 │
│ training_iteration                 1 │
│ validation_accuracy          0.43142 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:31. Total running time: 44s
[36m(train_xgb_ray_tune pid=2376748)[0m [249]	train-mlogloss:2.00419	validation-mlogloss:1.99917
[36m(train_xgb_ray_tune pid=2376737)[0m [239]	train-mlogloss:1.73740	validation-mlogloss:1.75173
[36m(train_xgb_ray_tune pid=2376736)[0m [264]	train-mlogloss:1.90045	validation-mlogloss:1.91081
[36m(train_xgb_ray_tune pid=2376716)[0m [275]	train-mlogloss:1.90237	validation-mlogloss:1.90801
[36m(train_xgb_ray_tune pid=2376729)[0m [243]	train-mlogloss:1.65737	validation-mlogloss:1.70457
[36m(train_xgb_ray_tune pid=2376722)[0m [214]	train-mlogloss:1.66987	validation-mlogloss:1.72193
[36m(train_xgb_ray_tune pid=2376743)[0m [258]	train-mlogloss:2.04390	validation-mlogloss:2.05787
[36m(train_xgb_ray_tune pid=2376741)[0m [227]	train-mlogloss:1.73296	validation-mlogloss:1.77488
[36m(train_xgb_ray_tune pid=2376747)[0m [189]	train-mlogloss:1.68390	validation-mlogloss:1.74486
[36m(train_xgb_ray_tune pid=2376738)[0m [249]	train-mlogloss:1.82052	validation-mlogloss:1.82011
[36m(train_xgb_ray_tune pid=2376728)[0m [227]	train-mlogloss:1.68697	validation-mlogloss:1.71232
[36m(train_xgb_ray_tune pid=2376735)[0m [259]	train-mlogloss:2.02152	validation-mlogloss:2.00975
[36m(train_xgb_ray_tune pid=2376740)[0m [338]	train-mlogloss:2.47886	validation-mlogloss:2.47229
[36m(train_xgb_ray_tune pid=2376749)[0m [261]	train-mlogloss:2.12383	validation-mlogloss:2.11031
[36m(train_xgb_ray_tune pid=2376729)[0m [244]	train-mlogloss:1.65618	validation-mlogloss:1.70350
[36m(train_xgb_ray_tune pid=2376735)[0m [260]	train-mlogloss:2.02013	validation-mlogloss:2.00822
[36m(train_xgb_ray_tune pid=2376716)[0m [276]	train-mlogloss:1.90102	validation-mlogloss:1.90674
[36m(train_xgb_ray_tune pid=2376736)[0m [265]	train-mlogloss:1.89921	validation-mlogloss:1.90964
[36m(train_xgb_ray_tune pid=2376746)[0m [211]	train-mlogloss:1.77730	validation-mlogloss:1.82138
[36m(train_xgb_ray_tune pid=2376737)[0m [240]	train-mlogloss:1.73620	validation-mlogloss:1.75070
[36m(train_xgb_ray_tune pid=2376750)[0m [203]	train-mlogloss:1.85324	validation-mlogloss:1.88054
[36m(train_xgb_ray_tune pid=2376740)[0m [339]	train-mlogloss:2.47877	validation-mlogloss:2.47220
[36m(train_xgb_ray_tune pid=2376748)[0m [250]	train-mlogloss:2.00271	validation-mlogloss:1.99776
[36m(train_xgb_ray_tune pid=2376737)[0m [241]	train-mlogloss:1.73504	validation-mlogloss:1.74958
[36m(train_xgb_ray_tune pid=2376736)[0m [266]	train-mlogloss:1.89789	validation-mlogloss:1.90837
[36m(train_xgb_ray_tune pid=2376716)[0m [277]	train-mlogloss:1.89972	validation-mlogloss:1.90548
[36m(train_xgb_ray_tune pid=2376729)[0m [245]	train-mlogloss:1.65507	validation-mlogloss:1.70243
[36m(train_xgb_ray_tune pid=2376722)[0m [215]	train-mlogloss:1.66823	validation-mlogloss:1.72049
[36m(train_xgb_ray_tune pid=2376743)[0m [259]	train-mlogloss:2.04255	validation-mlogloss:2.05658
[36m(train_xgb_ray_tune pid=2376746)[0m [212]	train-mlogloss:1.77548	validation-mlogloss:1.81973
[36m(train_xgb_ray_tune pid=2376741)[0m [228]	train-mlogloss:1.73124	validation-mlogloss:1.77332
[36m(train_xgb_ray_tune pid=2376747)[0m [190]	train-mlogloss:1.68190	validation-mlogloss:1.74310
[36m(train_xgb_ray_tune pid=2376738)[0m [250]	train-mlogloss:1.81919	validation-mlogloss:1.81888
[36m(train_xgb_ray_tune pid=2376728)[0m [228]	train-mlogloss:1.68530	validation-mlogloss:1.71078
[36m(train_xgb_ray_tune pid=2376735)[0m [261]	train-mlogloss:2.01866	validation-mlogloss:2.00677
[36m(train_xgb_ray_tune pid=2376740)[0m [340]	train-mlogloss:2.47877	validation-mlogloss:2.47220
[36m(train_xgb_ray_tune pid=2376749)[0m [262]	train-mlogloss:2.12244	validation-mlogloss:2.10894
[36m(train_xgb_ray_tune pid=2376741)[0m [229]	train-mlogloss:1.72958	validation-mlogloss:1.77183
[36m(train_xgb_ray_tune pid=2376735)[0m [262]	train-mlogloss:2.01728	validation-mlogloss:2.00533
[36m(train_xgb_ray_tune pid=2376748)[0m [251]	train-mlogloss:2.00126	validation-mlogloss:1.99642
[36m(train_xgb_ray_tune pid=2376729)[0m [246]	train-mlogloss:1.65389	validation-mlogloss:1.70134
[36m(train_xgb_ray_tune pid=2376750)[0m [204]	train-mlogloss:1.85139	validation-mlogloss:1.87873
[36m(train_xgb_ray_tune pid=2376738)[0m [251]	train-mlogloss:1.81791	validation-mlogloss:1.81769
[36m(train_xgb_ray_tune pid=2376749)[0m [263]	train-mlogloss:2.12115	validation-mlogloss:2.10767
[36m(train_xgb_ray_tune pid=2376737)[0m [242]	train-mlogloss:1.73378	validation-mlogloss:1.74846
[36m(train_xgb_ray_tune pid=2376736)[0m [267]	train-mlogloss:1.89653	validation-mlogloss:1.90710
[36m(train_xgb_ray_tune pid=2376716)[0m [278]	train-mlogloss:1.89842	validation-mlogloss:1.90431
[36m(train_xgb_ray_tune pid=2376729)[0m [247]	train-mlogloss:1.65278	validation-mlogloss:1.70026
[36m(train_xgb_ray_tune pid=2376722)[0m [216]	train-mlogloss:1.66661	validation-mlogloss:1.71906
[36m(train_xgb_ray_tune pid=2376743)[0m [260]	train-mlogloss:2.04119	validation-mlogloss:2.05524
[36m(train_xgb_ray_tune pid=2376743)[0m [261]	train-mlogloss:2.03979	validation-mlogloss:2.05387
[36m(train_xgb_ray_tune pid=2376746)[0m [213]	train-mlogloss:1.77363	validation-mlogloss:1.81803
[36m(train_xgb_ray_tune pid=2376747)[0m [191]	train-mlogloss:1.67990	validation-mlogloss:1.74135
[36m(train_xgb_ray_tune pid=2376738)[0m [252]	train-mlogloss:1.81665	validation-mlogloss:1.81646
[36m(train_xgb_ray_tune pid=2376728)[0m [229]	train-mlogloss:1.68373	validation-mlogloss:1.70955
[36m(train_xgb_ray_tune pid=2376750)[0m [205]	train-mlogloss:1.84956	validation-mlogloss:1.87705
[36m(train_xgb_ray_tune pid=2376735)[0m [263]	train-mlogloss:2.01580	validation-mlogloss:2.00390
[36m(train_xgb_ray_tune pid=2376740)[0m [341]	train-mlogloss:2.47877	validation-mlogloss:2.47220
[36m(train_xgb_ray_tune pid=2376740)[0m [342]	train-mlogloss:2.47868	validation-mlogloss:2.47209
[36m(train_xgb_ray_tune pid=2376749)[0m [264]	train-mlogloss:2.11989	validation-mlogloss:2.10641
[36m(train_xgb_ray_tune pid=2376748)[0m [252]	train-mlogloss:1.99981	validation-mlogloss:1.99498
[36m(train_xgb_ray_tune pid=2376741)[0m [230]	train-mlogloss:1.72786	validation-mlogloss:1.77020
[36m(train_xgb_ray_tune pid=2376737)[0m [243]	train-mlogloss:1.73246	validation-mlogloss:1.74726
[36m(train_xgb_ray_tune pid=2376728)[0m [230]	train-mlogloss:1.68214	validation-mlogloss:1.70806
[36m(train_xgb_ray_tune pid=2376716)[0m [279]	train-mlogloss:1.89710	validation-mlogloss:1.90304
[36m(train_xgb_ray_tune pid=2376722)[0m [217]	train-mlogloss:1.66497	validation-mlogloss:1.71751
[36m(train_xgb_ray_tune pid=2376749)[0m [265]	train-mlogloss:2.11858	validation-mlogloss:2.10519
[36m(train_xgb_ray_tune pid=2376748)[0m [253]	train-mlogloss:1.99847	validation-mlogloss:1.99360
[36m(train_xgb_ray_tune pid=2376737)[0m [244]	train-mlogloss:1.73114	validation-mlogloss:1.74616
[36m(train_xgb_ray_tune pid=2376736)[0m [268]	train-mlogloss:1.89530	validation-mlogloss:1.90585
[36m(train_xgb_ray_tune pid=2376716)[0m [280]	train-mlogloss:1.89579	validation-mlogloss:1.90180
[36m(train_xgb_ray_tune pid=2376729)[0m [248]	train-mlogloss:1.65162	validation-mlogloss:1.69921
[36m(train_xgb_ray_tune pid=2376729)[0m [249]	train-mlogloss:1.65055	validation-mlogloss:1.69817
[36m(train_xgb_ray_tune pid=2376743)[0m [262]	train-mlogloss:2.03843	validation-mlogloss:2.05249
[36m(train_xgb_ray_tune pid=2376746)[0m [214]	train-mlogloss:1.77179	validation-mlogloss:1.81627
[36m(train_xgb_ray_tune pid=2376741)[0m [231]	train-mlogloss:1.72628	validation-mlogloss:1.76869
[36m(train_xgb_ray_tune pid=2376747)[0m [192]	train-mlogloss:1.67791	validation-mlogloss:1.73959
[36m(train_xgb_ray_tune pid=2376738)[0m [253]	train-mlogloss:1.81536	validation-mlogloss:1.81520
[36m(train_xgb_ray_tune pid=2376728)[0m [231]	train-mlogloss:1.68060	validation-mlogloss:1.70670
[36m(train_xgb_ray_tune pid=2376750)[0m [206]	train-mlogloss:1.84773	validation-mlogloss:1.87527
[36m(train_xgb_ray_tune pid=2376735)[0m [264]	train-mlogloss:2.01450	validation-mlogloss:2.00259
[36m(train_xgb_ray_tune pid=2376740)[0m [343]	train-mlogloss:2.47868	validation-mlogloss:2.47209
[36m(train_xgb_ray_tune pid=2376740)[0m [344]	train-mlogloss:2.47868	validation-mlogloss:2.47209
[36m(train_xgb_ray_tune pid=2376749)[0m [266]	train-mlogloss:2.11727	validation-mlogloss:2.10382
[36m(train_xgb_ray_tune pid=2376736)[0m [269]	train-mlogloss:1.89400	validation-mlogloss:1.90462
[36m(train_xgb_ray_tune pid=2376722)[0m [218]	train-mlogloss:1.66329	validation-mlogloss:1.71606
[36m(train_xgb_ray_tune pid=2376743)[0m [263]	train-mlogloss:2.03704	validation-mlogloss:2.05108

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:32. Total running time: 45s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             42.3991 │
│ time_total_s                 42.3991 │
│ training_iteration                 1 │
│ validation_accuracy          0.22747 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:32. Total running time: 45s
[36m(train_xgb_ray_tune pid=2376748)[0m [254]	train-mlogloss:1.99702	validation-mlogloss:1.99210
[36m(train_xgb_ray_tune pid=2376737)[0m [245]	train-mlogloss:1.72995	validation-mlogloss:1.74501
[36m(train_xgb_ray_tune pid=2376736)[0m [270]	train-mlogloss:1.89284	validation-mlogloss:1.90343
[36m(train_xgb_ray_tune pid=2376716)[0m [281]	train-mlogloss:1.89454	validation-mlogloss:1.90061
[36m(train_xgb_ray_tune pid=2376716)[0m [282]	train-mlogloss:1.89326	validation-mlogloss:1.89932
[36m(train_xgb_ray_tune pid=2376729)[0m [250]	train-mlogloss:1.64949	validation-mlogloss:1.69720
[36m(train_xgb_ray_tune pid=2376743)[0m [264]	train-mlogloss:2.03575	validation-mlogloss:2.04973
[36m(train_xgb_ray_tune pid=2376746)[0m [215]	train-mlogloss:1.76994	validation-mlogloss:1.81469
[36m(train_xgb_ray_tune pid=2376741)[0m [232]	train-mlogloss:1.72471	validation-mlogloss:1.76729
[36m(train_xgb_ray_tune pid=2376747)[0m [193]	train-mlogloss:1.67603	validation-mlogloss:1.73786
[36m(train_xgb_ray_tune pid=2376738)[0m [254]	train-mlogloss:1.81407	validation-mlogloss:1.81391
[36m(train_xgb_ray_tune pid=2376728)[0m [232]	train-mlogloss:1.67897	validation-mlogloss:1.70511
[36m(train_xgb_ray_tune pid=2376750)[0m [207]	train-mlogloss:1.84592	validation-mlogloss:1.87365
[36m(train_xgb_ray_tune pid=2376735)[0m [265]	train-mlogloss:2.01308	validation-mlogloss:2.00122
[36m(train_xgb_ray_tune pid=2376735)[0m [266]	train-mlogloss:2.01164	validation-mlogloss:1.99982
[36m(train_xgb_ray_tune pid=2376740)[0m [345]	train-mlogloss:2.47868	validation-mlogloss:2.47209
[36m(train_xgb_ray_tune pid=2376749)[0m [267]	train-mlogloss:2.11598	validation-mlogloss:2.10245
[36m(train_xgb_ray_tune pid=2376729)[0m [251]	train-mlogloss:1.64834	validation-mlogloss:1.69614
[36m(train_xgb_ray_tune pid=2376722)[0m [219]	train-mlogloss:1.66160	validation-mlogloss:1.71457
[36m(train_xgb_ray_tune pid=2376740)[0m [346]	train-mlogloss:2.47852	validation-mlogloss:2.47192
[36m(train_xgb_ray_tune pid=2376748)[0m [255]	train-mlogloss:1.99554	validation-mlogloss:1.99058
[36m(train_xgb_ray_tune pid=2376736)[0m [271]	train-mlogloss:1.89153	validation-mlogloss:1.90213
[36m(train_xgb_ray_tune pid=2376738)[0m [255]	train-mlogloss:1.81280	validation-mlogloss:1.81285
[36m(train_xgb_ray_tune pid=2376748)[0m [256]	train-mlogloss:1.99403	validation-mlogloss:1.98907
[36m(train_xgb_ray_tune pid=2376737)[0m [246]	train-mlogloss:1.72874	validation-mlogloss:1.74388
[36m(train_xgb_ray_tune pid=2376736)[0m [272]	train-mlogloss:1.89028	validation-mlogloss:1.90092
[36m(train_xgb_ray_tune pid=2376716)[0m [283]	train-mlogloss:1.89195	validation-mlogloss:1.89815
[36m(train_xgb_ray_tune pid=2376729)[0m [252]	train-mlogloss:1.64726	validation-mlogloss:1.69521
[36m(train_xgb_ray_tune pid=2376722)[0m [220]	train-mlogloss:1.66004	validation-mlogloss:1.71317
[36m(train_xgb_ray_tune pid=2376743)[0m [265]	train-mlogloss:2.03444	validation-mlogloss:2.04845
[36m(train_xgb_ray_tune pid=2376746)[0m [216]	train-mlogloss:1.76807	validation-mlogloss:1.81298
[36m(train_xgb_ray_tune pid=2376741)[0m [233]	train-mlogloss:1.72312	validation-mlogloss:1.76581
[36m(train_xgb_ray_tune pid=2376741)[0m [234]	train-mlogloss:1.72147	validation-mlogloss:1.76417
[36m(train_xgb_ray_tune pid=2376747)[0m [194]	train-mlogloss:1.67410	validation-mlogloss:1.73622
[36m(train_xgb_ray_tune pid=2376738)[0m [256]	train-mlogloss:1.81155	validation-mlogloss:1.81163
[36m(train_xgb_ray_tune pid=2376728)[0m [233]	train-mlogloss:1.67732	validation-mlogloss:1.70370
[36m(train_xgb_ray_tune pid=2376750)[0m [208]	train-mlogloss:1.84414	validation-mlogloss:1.87196
[36m(train_xgb_ray_tune pid=2376735)[0m [267]	train-mlogloss:2.01018	validation-mlogloss:1.99843
[36m(train_xgb_ray_tune pid=2376740)[0m [347]	train-mlogloss:2.47832	validation-mlogloss:2.47171
[36m(train_xgb_ray_tune pid=2376749)[0m [268]	train-mlogloss:2.11468	validation-mlogloss:2.10116
[36m(train_xgb_ray_tune pid=2376740)[0m [348]	train-mlogloss:2.47832	validation-mlogloss:2.47171
[36m(train_xgb_ray_tune pid=2376748)[0m [257]	train-mlogloss:1.99255	validation-mlogloss:1.98760
[36m(train_xgb_ray_tune pid=2376737)[0m [247]	train-mlogloss:1.72748	validation-mlogloss:1.74271
[36m(train_xgb_ray_tune pid=2376736)[0m [273]	train-mlogloss:1.88907	validation-mlogloss:1.89978
[36m(train_xgb_ray_tune pid=2376736)[0m [274]	train-mlogloss:1.88790	validation-mlogloss:1.89864
[36m(train_xgb_ray_tune pid=2376716)[0m [284]	train-mlogloss:1.89071	validation-mlogloss:1.89707
[36m(train_xgb_ray_tune pid=2376729)[0m [253]	train-mlogloss:1.64621	validation-mlogloss:1.69421
[36m(train_xgb_ray_tune pid=2376729)[0m [254]	train-mlogloss:1.64516	validation-mlogloss:1.69329
[36m(train_xgb_ray_tune pid=2376722)[0m [221]	train-mlogloss:1.65837	validation-mlogloss:1.71174
[36m(train_xgb_ray_tune pid=2376743)[0m [266]	train-mlogloss:2.03309	validation-mlogloss:2.04706
[36m(train_xgb_ray_tune pid=2376746)[0m [217]	train-mlogloss:1.76631	validation-mlogloss:1.81130
[36m(train_xgb_ray_tune pid=2376741)[0m [235]	train-mlogloss:1.71991	validation-mlogloss:1.76269
[36m(train_xgb_ray_tune pid=2376741)[0m [236]	train-mlogloss:1.71827	validation-mlogloss:1.76124
[36m(train_xgb_ray_tune pid=2376747)[0m [195]	train-mlogloss:1.67214	validation-mlogloss:1.73455
[36m(train_xgb_ray_tune pid=2376738)[0m [257]	train-mlogloss:1.81040	validation-mlogloss:1.81056
[36m(train_xgb_ray_tune pid=2376728)[0m [234]	train-mlogloss:1.67576	validation-mlogloss:1.70225
[36m(train_xgb_ray_tune pid=2376728)[0m [235]	train-mlogloss:1.67424	validation-mlogloss:1.70117
[36m(train_xgb_ray_tune pid=2376750)[0m [209]	train-mlogloss:1.84239	validation-mlogloss:1.87038
[36m(train_xgb_ray_tune pid=2376735)[0m [268]	train-mlogloss:2.00891	validation-mlogloss:1.99715
[36m(train_xgb_ray_tune pid=2376735)[0m [269]	train-mlogloss:2.00757	validation-mlogloss:1.99575
[36m(train_xgb_ray_tune pid=2376740)[0m [349]	train-mlogloss:2.47832	validation-mlogloss:2.47171
[36m(train_xgb_ray_tune pid=2376749)[0m [269]	train-mlogloss:2.11343	validation-mlogloss:2.09986
[36m(train_xgb_ray_tune pid=2376749)[0m [270]	train-mlogloss:2.11220	validation-mlogloss:2.09865
[36m(train_xgb_ray_tune pid=2376737)[0m [248]	train-mlogloss:1.72627	validation-mlogloss:1.74150
[36m(train_xgb_ray_tune pid=2376716)[0m [285]	train-mlogloss:1.88948	validation-mlogloss:1.89596
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[36m(train_xgb_ray_tune pid=2376728)[0m [244]	train-mlogloss:1.66122	validation-mlogloss:1.68917
[36m(train_xgb_ray_tune pid=2376750)[0m [218]	train-mlogloss:1.82714	validation-mlogloss:1.85621
[36m(train_xgb_ray_tune pid=2376735)[0m [280]	train-mlogloss:1.99260	validation-mlogloss:1.98092
[36m(train_xgb_ray_tune pid=2376740)[0m [364]	train-mlogloss:2.47783	validation-mlogloss:2.47121
[36m(train_xgb_ray_tune pid=2376740)[0m [365]	train-mlogloss:2.47783	validation-mlogloss:2.47121
[36m(train_xgb_ray_tune pid=2376749)[0m [281]	train-mlogloss:2.09882	validation-mlogloss:2.08531
[36m(train_xgb_ray_tune pid=2376736)[0m [287]	train-mlogloss:1.87207	validation-mlogloss:1.88331
[36m(train_xgb_ray_tune pid=2376716)[0m [298]	train-mlogloss:1.87372	validation-mlogloss:1.88069
[36m(train_xgb_ray_tune pid=2376722)[0m [232]	train-mlogloss:1.64104	validation-mlogloss:1.69636
[36m(train_xgb_ray_tune pid=2376743)[0m [278]	train-mlogloss:2.01751	validation-mlogloss:2.03150
[36m(train_xgb_ray_tune pid=2376746)[0m [228]	train-mlogloss:1.74688	validation-mlogloss:1.79315
[36m(train_xgb_ray_tune pid=2376741)[0m [248]	train-mlogloss:1.69999	validation-mlogloss:1.74459
[36m(train_xgb_ray_tune pid=2376728)[0m [245]	train-mlogloss:1.65972	validation-mlogloss:1.68772
[36m(train_xgb_ray_tune pid=2376749)[0m [282]	train-mlogloss:2.09756	validation-mlogloss:2.08408
[36m(train_xgb_ray_tune pid=2376737)[0m [259]	train-mlogloss:1.71404	validation-mlogloss:1.72984
[36m(train_xgb_ray_tune pid=2376729)[0m [266]	train-mlogloss:1.63344	validation-mlogloss:1.68239
[36m(train_xgb_ray_tune pid=2376735)[0m [281]	train-mlogloss:1.99138	validation-mlogloss:1.97971
[36m(train_xgb_ray_tune pid=2376748)[0m [269]	train-mlogloss:1.97578	validation-mlogloss:1.97124
[36m(train_xgb_ray_tune pid=2376737)[0m [260]	train-mlogloss:1.71294	validation-mlogloss:1.72885
[36m(train_xgb_ray_tune pid=2376736)[0m [288]	train-mlogloss:1.87092	validation-mlogloss:1.88220
[36m(train_xgb_ray_tune pid=2376716)[0m [299]	train-mlogloss:1.87256	validation-mlogloss:1.87956
[36m(train_xgb_ray_tune pid=2376729)[0m [267]	train-mlogloss:1.63243	validation-mlogloss:1.68148
[36m(train_xgb_ray_tune pid=2376722)[0m [233]	train-mlogloss:1.63962	validation-mlogloss:1.69507
[36m(train_xgb_ray_tune pid=2376743)[0m [279]	train-mlogloss:2.01622	validation-mlogloss:2.03025
[36m(train_xgb_ray_tune pid=2376746)[0m [229]	train-mlogloss:1.74532	validation-mlogloss:1.79177
[36m(train_xgb_ray_tune pid=2376741)[0m [249]	train-mlogloss:1.69858	validation-mlogloss:1.74329
[36m(train_xgb_ray_tune pid=2376747)[0m [206]	train-mlogloss:1.65184	validation-mlogloss:1.71738
[36m(train_xgb_ray_tune pid=2376728)[0m [246]	train-mlogloss:1.65833	validation-mlogloss:1.68648
[36m(train_xgb_ray_tune pid=2376750)[0m [219]	train-mlogloss:1.82546	validation-mlogloss:1.85467
[36m(train_xgb_ray_tune pid=2376735)[0m [282]	train-mlogloss:1.99009	validation-mlogloss:1.97840
[36m(train_xgb_ray_tune pid=2376740)[0m [366]	train-mlogloss:2.47783	validation-mlogloss:2.47121
[36m(train_xgb_ray_tune pid=2376740)[0m [367]	train-mlogloss:2.47783	validation-mlogloss:2.47121
[36m(train_xgb_ray_tune pid=2376749)[0m [283]	train-mlogloss:2.09634	validation-mlogloss:2.08288
[36m(train_xgb_ray_tune pid=2376748)[0m [270]	train-mlogloss:1.97448	validation-mlogloss:1.96999
[36m(train_xgb_ray_tune pid=2376736)[0m [289]	train-mlogloss:1.86978	validation-mlogloss:1.88111
[36m(train_xgb_ray_tune pid=2376750)[0m [220]	train-mlogloss:1.82391	validation-mlogloss:1.85319
[36m(train_xgb_ray_tune pid=2376747)[0m [207]	train-mlogloss:1.65007	validation-mlogloss:1.71577
[36m(train_xgb_ray_tune pid=2376748)[0m [271]	train-mlogloss:1.97323	validation-mlogloss:1.96867
[36m(train_xgb_ray_tune pid=2376737)[0m [261]	train-mlogloss:1.71180	validation-mlogloss:1.72790
[36m(train_xgb_ray_tune pid=2376736)[0m [290]	train-mlogloss:1.86853	validation-mlogloss:1.87989
[36m(train_xgb_ray_tune pid=2376716)[0m [300]	train-mlogloss:1.87139	validation-mlogloss:1.87840
[36m(train_xgb_ray_tune pid=2376729)[0m [268]	train-mlogloss:1.63154	validation-mlogloss:1.68066
[36m(train_xgb_ray_tune pid=2376722)[0m [234]	train-mlogloss:1.63817	validation-mlogloss:1.69355
[36m(train_xgb_ray_tune pid=2376743)[0m [280]	train-mlogloss:2.01494	validation-mlogloss:2.02893
[36m(train_xgb_ray_tune pid=2376746)[0m [230]	train-mlogloss:1.74360	validation-mlogloss:1.79023
[36m(train_xgb_ray_tune pid=2376741)[0m [250]	train-mlogloss:1.69718	validation-mlogloss:1.74195
[36m(train_xgb_ray_tune pid=2376747)[0m [208]	train-mlogloss:1.64826	validation-mlogloss:1.71418
[36m(train_xgb_ray_tune pid=2376728)[0m [247]	train-mlogloss:1.65695	validation-mlogloss:1.68535
[36m(train_xgb_ray_tune pid=2376735)[0m [283]	train-mlogloss:1.98875	validation-mlogloss:1.97702
[36m(train_xgb_ray_tune pid=2376740)[0m [368]	train-mlogloss:2.47773	validation-mlogloss:2.47111
[36m(train_xgb_ray_tune pid=2376749)[0m [284]	train-mlogloss:2.09514	validation-mlogloss:2.08172
[36m(train_xgb_ray_tune pid=2376749)[0m [285]	train-mlogloss:2.09391	validation-mlogloss:2.08043
[36m(train_xgb_ray_tune pid=2376729)[0m [269]	train-mlogloss:1.63058	validation-mlogloss:1.67974
[36m(train_xgb_ray_tune pid=2376743)[0m [281]	train-mlogloss:2.01366	validation-mlogloss:2.02765
[36m(train_xgb_ray_tune pid=2376746)[0m [231]	train-mlogloss:1.74185	validation-mlogloss:1.78860
[36m(train_xgb_ray_tune pid=2376750)[0m [221]	train-mlogloss:1.82233	validation-mlogloss:1.85166
[36m(train_xgb_ray_tune pid=2376740)[0m [369]	train-mlogloss:2.47773	validation-mlogloss:2.47111
[36m(train_xgb_ray_tune pid=2376748)[0m [272]	train-mlogloss:1.97192	validation-mlogloss:1.96733
[36m(train_xgb_ray_tune pid=2376716)[0m [301]	train-mlogloss:1.87028	validation-mlogloss:1.87733
[36m(train_xgb_ray_tune pid=2376748)[0m [273]	train-mlogloss:1.97055	validation-mlogloss:1.96606
[36m(train_xgb_ray_tune pid=2376737)[0m [262]	train-mlogloss:1.71072	validation-mlogloss:1.72676
[36m(train_xgb_ray_tune pid=2376736)[0m [291]	train-mlogloss:1.86743	validation-mlogloss:1.87886
[36m(train_xgb_ray_tune pid=2376716)[0m [302]	train-mlogloss:1.86909	validation-mlogloss:1.87630
[36m(train_xgb_ray_tune pid=2376729)[0m [270]	train-mlogloss:1.62980	validation-mlogloss:1.67902
[36m(train_xgb_ray_tune pid=2376722)[0m [235]	train-mlogloss:1.63667	validation-mlogloss:1.69232
[36m(train_xgb_ray_tune pid=2376743)[0m [282]	train-mlogloss:2.01239	validation-mlogloss:2.02637
[36m(train_xgb_ray_tune pid=2376746)[0m [232]	train-mlogloss:1.74020	validation-mlogloss:1.78700
[36m(train_xgb_ray_tune pid=2376741)[0m [251]	train-mlogloss:1.69577	validation-mlogloss:1.74065
[36m(train_xgb_ray_tune pid=2376747)[0m [209]	train-mlogloss:1.64653	validation-mlogloss:1.71249
[36m(train_xgb_ray_tune pid=2376728)[0m [248]	train-mlogloss:1.65561	validation-mlogloss:1.68408
[36m(train_xgb_ray_tune pid=2376728)[0m [249]	train-mlogloss:1.65427	validation-mlogloss:1.68286
[36m(train_xgb_ray_tune pid=2376750)[0m [222]	train-mlogloss:1.82073	validation-mlogloss:1.85013
[36m(train_xgb_ray_tune pid=2376735)[0m [284]	train-mlogloss:1.98752	validation-mlogloss:1.97574
[36m(train_xgb_ray_tune pid=2376735)[0m [285]	train-mlogloss:1.98624	validation-mlogloss:1.97449
[36m(train_xgb_ray_tune pid=2376740)[0m [370]	train-mlogloss:2.47773	validation-mlogloss:2.47111
[36m(train_xgb_ray_tune pid=2376749)[0m [286]	train-mlogloss:2.09273	validation-mlogloss:2.07923
[36m(train_xgb_ray_tune pid=2376749)[0m [287]	train-mlogloss:2.09147	validation-mlogloss:2.07800
[36m(train_xgb_ray_tune pid=2376748)[0m [274]	train-mlogloss:1.96921	validation-mlogloss:1.96469
[36m(train_xgb_ray_tune pid=2376737)[0m [263]	train-mlogloss:1.70955	validation-mlogloss:1.72565
[36m(train_xgb_ray_tune pid=2376736)[0m [292]	train-mlogloss:1.86629	validation-mlogloss:1.87774
[36m(train_xgb_ray_tune pid=2376716)[0m [303]	train-mlogloss:1.86788	validation-mlogloss:1.87517
[36m(train_xgb_ray_tune pid=2376729)[0m [271]	train-mlogloss:1.62890	validation-mlogloss:1.67820
[36m(train_xgb_ray_tune pid=2376722)[0m [236]	train-mlogloss:1.63527	validation-mlogloss:1.69110
[36m(train_xgb_ray_tune pid=2376722)[0m [237]	train-mlogloss:1.63373	validation-mlogloss:1.68966
[36m(train_xgb_ray_tune pid=2376743)[0m [283]	train-mlogloss:2.01116	validation-mlogloss:2.02523
[36m(train_xgb_ray_tune pid=2376746)[0m [233]	train-mlogloss:1.73850	validation-mlogloss:1.78546
[36m(train_xgb_ray_tune pid=2376741)[0m [252]	train-mlogloss:1.69446	validation-mlogloss:1.73944
[36m(train_xgb_ray_tune pid=2376747)[0m [210]	train-mlogloss:1.64470	validation-mlogloss:1.71104
[36m(train_xgb_ray_tune pid=2376728)[0m [250]	train-mlogloss:1.65287	validation-mlogloss:1.68145
[36m(train_xgb_ray_tune pid=2376750)[0m [223]	train-mlogloss:1.81909	validation-mlogloss:1.84851
[36m(train_xgb_ray_tune pid=2376735)[0m [286]	train-mlogloss:1.98491	validation-mlogloss:1.97320
[36m(train_xgb_ray_tune pid=2376740)[0m [371]	train-mlogloss:2.47773	validation-mlogloss:2.47111
[36m(train_xgb_ray_tune pid=2376740)[0m [372]	train-mlogloss:2.47773	validation-mlogloss:2.47111
[36m(train_xgb_ray_tune pid=2376749)[0m [288]	train-mlogloss:2.09026	validation-mlogloss:2.07679
[36m(train_xgb_ray_tune pid=2376728)[0m [251]	train-mlogloss:1.65156	validation-mlogloss:1.68022
[36m(train_xgb_ray_tune pid=2376737)[0m [264]	train-mlogloss:1.70848	validation-mlogloss:1.72455
[36m(train_xgb_ray_tune pid=2376746)[0m [234]	train-mlogloss:1.73683	validation-mlogloss:1.78378
[36m(train_xgb_ray_tune pid=2376748)[0m [275]	train-mlogloss:1.96795	validation-mlogloss:1.96339
[36m(train_xgb_ray_tune pid=2376736)[0m [293]	train-mlogloss:1.86514	validation-mlogloss:1.87659
[36m(train_xgb_ray_tune pid=2376716)[0m [304]	train-mlogloss:1.86676	validation-mlogloss:1.87406
[36m(train_xgb_ray_tune pid=2376729)[0m [272]	train-mlogloss:1.62796	validation-mlogloss:1.67743
[36m(train_xgb_ray_tune pid=2376722)[0m [238]	train-mlogloss:1.63221	validation-mlogloss:1.68842
[36m(train_xgb_ray_tune pid=2376743)[0m [284]	train-mlogloss:2.00987	validation-mlogloss:2.02396
[36m(train_xgb_ray_tune pid=2376746)[0m [235]	train-mlogloss:1.73527	validation-mlogloss:1.78240
[36m(train_xgb_ray_tune pid=2376741)[0m [253]	train-mlogloss:1.69313	validation-mlogloss:1.73816
[36m(train_xgb_ray_tune pid=2376747)[0m [211]	train-mlogloss:1.64289	validation-mlogloss:1.70949
[36m(train_xgb_ray_tune pid=2376728)[0m [252]	train-mlogloss:1.65023	validation-mlogloss:1.67894
[36m(train_xgb_ray_tune pid=2376750)[0m [224]	train-mlogloss:1.81754	validation-mlogloss:1.84696
[36m(train_xgb_ray_tune pid=2376750)[0m [225]	train-mlogloss:1.81599	validation-mlogloss:1.84557
[36m(train_xgb_ray_tune pid=2376735)[0m [287]	train-mlogloss:1.98369	validation-mlogloss:1.97190
[36m(train_xgb_ray_tune pid=2376740)[0m [373]	train-mlogloss:2.47773	validation-mlogloss:2.47111
[36m(train_xgb_ray_tune pid=2376749)[0m [289]	train-mlogloss:2.08904	validation-mlogloss:2.07555
[36m(train_xgb_ray_tune pid=2376737)[0m [265]	train-mlogloss:1.70739	validation-mlogloss:1.72355
[36m(train_xgb_ray_tune pid=2376736)[0m [294]	train-mlogloss:1.86398	validation-mlogloss:1.87551
[36m(train_xgb_ray_tune pid=2376716)[0m [305]	train-mlogloss:1.86561	validation-mlogloss:1.87293
[36m(train_xgb_ray_tune pid=2376729)[0m [273]	train-mlogloss:1.62713	validation-mlogloss:1.67662
[36m(train_xgb_ray_tune pid=2376749)[0m [290]	train-mlogloss:2.08789	validation-mlogloss:2.07447

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:34. Total running time: 47s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             44.8149 │
│ time_total_s                 44.8149 │
│ training_iteration                 1 │
│ validation_accuracy          0.41462 │
╰──────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=2376748)[0m [276]	train-mlogloss:1.96662	validation-mlogloss:1.96202
[36m(train_xgb_ray_tune pid=2376743)[0m [285]	train-mlogloss:2.00860	validation-mlogloss:2.02273
[36m(train_xgb_ray_tune pid=2376735)[0m [288]	train-mlogloss:1.98246	validation-mlogloss:1.97065
[36m(train_xgb_ray_tune pid=2376740)[0m [374]	train-mlogloss:2.47773	validation-mlogloss:2.47111
[36m(train_xgb_ray_tune pid=2376741)[0m [254]	train-mlogloss:1.69162	validation-mlogloss:1.73680

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:34. Total running time: 47s
[36m(train_xgb_ray_tune pid=2376748)[0m [277]	train-mlogloss:1.96530	validation-mlogloss:1.96075
[36m(train_xgb_ray_tune pid=2376737)[0m [266]	train-mlogloss:1.70636	validation-mlogloss:1.72251
[36m(train_xgb_ray_tune pid=2376736)[0m [295]	train-mlogloss:1.86289	validation-mlogloss:1.87442
[36m(train_xgb_ray_tune pid=2376716)[0m [306]	train-mlogloss:1.86447	validation-mlogloss:1.87180
[36m(train_xgb_ray_tune pid=2376729)[0m [274]	train-mlogloss:1.62622	validation-mlogloss:1.67575
[36m(train_xgb_ray_tune pid=2376722)[0m [239]	train-mlogloss:1.63075	validation-mlogloss:1.68715
[36m(train_xgb_ray_tune pid=2376743)[0m [286]	train-mlogloss:2.00736	validation-mlogloss:2.02152
[36m(train_xgb_ray_tune pid=2376746)[0m [236]	train-mlogloss:1.73363	validation-mlogloss:1.78087
[36m(train_xgb_ray_tune pid=2376741)[0m [255]	train-mlogloss:1.69029	validation-mlogloss:1.73552
[36m(train_xgb_ray_tune pid=2376747)[0m [212]	train-mlogloss:1.64110	validation-mlogloss:1.70800
[36m(train_xgb_ray_tune pid=2376728)[0m [253]	train-mlogloss:1.64886	validation-mlogloss:1.67761
[36m(train_xgb_ray_tune pid=2376750)[0m [226]	train-mlogloss:1.81438	validation-mlogloss:1.84403
[36m(train_xgb_ray_tune pid=2376735)[0m [289]	train-mlogloss:1.98112	validation-mlogloss:1.96934
[36m(train_xgb_ray_tune pid=2376740)[0m [375]	train-mlogloss:2.47763	validation-mlogloss:2.47100
[36m(train_xgb_ray_tune pid=2376740)[0m [376]	train-mlogloss:2.47763	validation-mlogloss:2.47100
[36m(train_xgb_ray_tune pid=2376749)[0m [291]	train-mlogloss:2.08659	validation-mlogloss:2.07323
[36m(train_xgb_ray_tune pid=2376746)[0m [237]	train-mlogloss:1.73196	validation-mlogloss:1.77934
[36m(train_xgb_ray_tune pid=2376728)[0m [254]	train-mlogloss:1.64764	validation-mlogloss:1.67651
[36m(train_xgb_ray_tune pid=2376749)[0m [292]	train-mlogloss:2.08534	validation-mlogloss:2.07194
[36m(train_xgb_ray_tune pid=2376716)[0m [307]	train-mlogloss:1.86326	validation-mlogloss:1.87052
[36m(train_xgb_ray_tune pid=2376748)[0m [278]	train-mlogloss:1.96400	validation-mlogloss:1.95943
[36m(train_xgb_ray_tune pid=2376737)[0m [267]	train-mlogloss:1.70535	validation-mlogloss:1.72160
[36m(train_xgb_ray_tune pid=2376736)[0m [296]	train-mlogloss:1.86182	validation-mlogloss:1.87342
[36m(train_xgb_ray_tune pid=2376716)[0m [308]	train-mlogloss:1.86214	validation-mlogloss:1.86940
[36m(train_xgb_ray_tune pid=2376729)[0m [275]	train-mlogloss:1.62524	validation-mlogloss:1.67482
[36m(train_xgb_ray_tune pid=2376722)[0m [240]	train-mlogloss:1.62929	validation-mlogloss:1.68590
[36m(train_xgb_ray_tune pid=2376743)[0m [287]	train-mlogloss:2.00611	validation-mlogloss:2.02031
[36m(train_xgb_ray_tune pid=2376746)[0m [238]	train-mlogloss:1.73037	validation-mlogloss:1.77794
[36m(train_xgb_ray_tune pid=2376741)[0m [256]	train-mlogloss:1.68887	validation-mlogloss:1.73436
[36m(train_xgb_ray_tune pid=2376747)[0m [213]	train-mlogloss:1.63935	validation-mlogloss:1.70660
[36m(train_xgb_ray_tune pid=2376728)[0m [255]	train-mlogloss:1.64636	validation-mlogloss:1.67535
[36m(train_xgb_ray_tune pid=2376750)[0m [227]	train-mlogloss:1.81279	validation-mlogloss:1.84250
[36m(train_xgb_ray_tune pid=2376735)[0m [290]	train-mlogloss:1.97975	validation-mlogloss:1.96801
[36m(train_xgb_ray_tune pid=2376735)[0m [291]	train-mlogloss:1.97850	validation-mlogloss:1.96679
[36m(train_xgb_ray_tune pid=2376740)[0m [377]	train-mlogloss:2.47755	validation-mlogloss:2.47091
[36m(train_xgb_ray_tune pid=2376740)[0m [378]	train-mlogloss:2.47755	validation-mlogloss:2.47091
[36m(train_xgb_ray_tune pid=2376749)[0m [293]	train-mlogloss:2.08416	validation-mlogloss:2.07075
[36m(train_xgb_ray_tune pid=2376748)[0m [279]	train-mlogloss:1.96274	validation-mlogloss:1.95819
[36m(train_xgb_ray_tune pid=2376737)[0m [268]	train-mlogloss:1.70423	validation-mlogloss:1.72055
[36m(train_xgb_ray_tune pid=2376736)[0m [297]	train-mlogloss:1.86065	validation-mlogloss:1.87239
[36m(train_xgb_ray_tune pid=2376716)[0m [309]	train-mlogloss:1.86097	validation-mlogloss:1.86832
[36m(train_xgb_ray_tune pid=2376729)[0m [276]	train-mlogloss:1.62438	validation-mlogloss:1.67406
[36m(train_xgb_ray_tune pid=2376722)[0m [241]	train-mlogloss:1.62789	validation-mlogloss:1.68472
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[36m(train_xgb_ray_tune pid=2376740)[0m [447]	train-mlogloss:2.47452	validation-mlogloss:2.46790
[36m(train_xgb_ray_tune pid=2376749)[0m [344]	train-mlogloss:2.02953	validation-mlogloss:2.01644
[36m(train_xgb_ray_tune pid=2376749)[0m [345]	train-mlogloss:2.02859	validation-mlogloss:2.01552
[36m(train_xgb_ray_tune pid=2376729)[0m [343]	train-mlogloss:1.58071	validation-mlogloss:1.63482
[36m(train_xgb_ray_tune pid=2376746)[0m [286]	train-mlogloss:1.66111	validation-mlogloss:1.71432
[36m(train_xgb_ray_tune pid=2376728)[0m [315]	train-mlogloss:1.57790	validation-mlogloss:1.61356
[36m(train_xgb_ray_tune pid=2376740)[0m [448]	train-mlogloss:2.47452	validation-mlogloss:2.46790
[36m(train_xgb_ray_tune pid=2376748)[0m [341]	train-mlogloss:1.89045	validation-mlogloss:1.88875
[36m(train_xgb_ray_tune pid=2376748)[0m [342]	train-mlogloss:1.88951	validation-mlogloss:1.88778
[36m(train_xgb_ray_tune pid=2376737)[0m [333]	train-mlogloss:1.64901	validation-mlogloss:1.66947
[36m(train_xgb_ray_tune pid=2376736)[0m [352]	train-mlogloss:1.80585	validation-mlogloss:1.81994
[36m(train_xgb_ray_tune pid=2376729)[0m [344]	train-mlogloss:1.58025	validation-mlogloss:1.63434
[36m(train_xgb_ray_tune pid=2376729)[0m [345]	train-mlogloss:1.57982	validation-mlogloss:1.63391
[36m(train_xgb_ray_tune pid=2376722)[0m [283]	train-mlogloss:1.57486	validation-mlogloss:1.63839
[36m(train_xgb_ray_tune pid=2376743)[0m [341]	train-mlogloss:1.94727	validation-mlogloss:1.96178
[36m(train_xgb_ray_tune pid=2376746)[0m [287]	train-mlogloss:1.65987	validation-mlogloss:1.71323
[36m(train_xgb_ray_tune pid=2376741)[0m [308]	train-mlogloss:1.62656	validation-mlogloss:1.67751
[36m(train_xgb_ray_tune pid=2376741)[0m [309]	train-mlogloss:1.62548	validation-mlogloss:1.67652
[36m(train_xgb_ray_tune pid=2376747)[0m [269]	train-mlogloss:1.55860	validation-mlogloss:1.63810
[36m(train_xgb_ray_tune pid=2376747)[0m [270]	train-mlogloss:1.55737	validation-mlogloss:1.63716
[36m(train_xgb_ray_tune pid=2376728)[0m [316]	train-mlogloss:1.57697	validation-mlogloss:1.61279
[36m(train_xgb_ray_tune pid=2376750)[0m [277]	train-mlogloss:1.74428	validation-mlogloss:1.77774
[36m(train_xgb_ray_tune pid=2376735)[0m [352]	train-mlogloss:1.90961	validation-mlogloss:1.89932
[36m(train_xgb_ray_tune pid=2376735)[0m [353]	train-mlogloss:1.90866	validation-mlogloss:1.89837
[36m(train_xgb_ray_tune pid=2376740)[0m [449]	train-mlogloss:2.47452	validation-mlogloss:2.46790
[36m(train_xgb_ray_tune pid=2376749)[0m [346]	train-mlogloss:2.02769	validation-mlogloss:2.01457
[36m(train_xgb_ray_tune pid=2376748)[0m [343]	train-mlogloss:1.88848	validation-mlogloss:1.88678
[36m(train_xgb_ray_tune pid=2376737)[0m [334]	train-mlogloss:1.64829	validation-mlogloss:1.66890
[36m(train_xgb_ray_tune pid=2376736)[0m [353]	train-mlogloss:1.80494	validation-mlogloss:1.81907
[36m(train_xgb_ray_tune pid=2376729)[0m [346]	train-mlogloss:1.57934	validation-mlogloss:1.63347
[36m(train_xgb_ray_tune pid=2376729)[0m [347]	train-mlogloss:1.57879	validation-mlogloss:1.63302
[36m(train_xgb_ray_tune pid=2376722)[0m [284]	train-mlogloss:1.57375	validation-mlogloss:1.63735
[36m(train_xgb_ray_tune pid=2376743)[0m [342]	train-mlogloss:1.94628	validation-mlogloss:1.96079
[36m(train_xgb_ray_tune pid=2376716)[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=2376716)[0m   _log_deprecation_warning([32m [repeated 3x across cluster][0m
[36m(train_xgb_ray_tune pid=2376741)[0m [310]	train-mlogloss:1.62443	validation-mlogloss:1.67553
[36m(train_xgb_ray_tune pid=2376747)[0m [271]	train-mlogloss:1.55612	validation-mlogloss:1.63613
[36m(train_xgb_ray_tune pid=2376728)[0m [317]	train-mlogloss:1.57609	validation-mlogloss:1.61216
[36m(train_xgb_ray_tune pid=2376728)[0m [318]	train-mlogloss:1.57510	validation-mlogloss:1.61127
[36m(train_xgb_ray_tune pid=2376750)[0m [278]	train-mlogloss:1.74315	validation-mlogloss:1.77666
[36m(train_xgb_ray_tune pid=2376735)[0m [354]	train-mlogloss:1.90762	validation-mlogloss:1.89734
[36m(train_xgb_ray_tune pid=2376735)[0m [355]	train-mlogloss:1.90662	validation-mlogloss:1.89637
[36m(train_xgb_ray_tune pid=2376740)[0m [450]	train-mlogloss:2.47452	validation-mlogloss:2.46790
[36m(train_xgb_ray_tune pid=2376749)[0m [347]	train-mlogloss:2.02679	validation-mlogloss:2.01371
[36m(train_xgb_ray_tune pid=2376748)[0m [344]	train-mlogloss:1.88742	validation-mlogloss:1.88577
[36m(train_xgb_ray_tune pid=2376737)[0m [335]	train-mlogloss:1.64775	validation-mlogloss:1.66838
[36m(train_xgb_ray_tune pid=2376736)[0m [354]	train-mlogloss:1.80406	validation-mlogloss:1.81817
[36m(train_xgb_ray_tune pid=2376746)[0m [288]	train-mlogloss:1.65856	validation-mlogloss:1.71197
[36m(train_xgb_ray_tune pid=2376740)[0m [451]	train-mlogloss:2.47427	validation-mlogloss:2.46764
[36m(train_xgb_ray_tune pid=2376747)[0m [272]	train-mlogloss:1.55488	validation-mlogloss:1.63523
[36m(train_xgb_ray_tune pid=2376748)[0m [345]	train-mlogloss:1.88638	validation-mlogloss:1.88484
[36m(train_xgb_ray_tune pid=2376737)[0m [336]	train-mlogloss:1.64709	validation-mlogloss:1.66793
[36m(train_xgb_ray_tune pid=2376736)[0m [355]	train-mlogloss:1.80323	validation-mlogloss:1.81739
[36m(train_xgb_ray_tune pid=2376729)[0m [348]	train-mlogloss:1.57841	validation-mlogloss:1.63272
[36m(train_xgb_ray_tune pid=2376729)[0m [349]	train-mlogloss:1.57794	validation-mlogloss:1.63219
[36m(train_xgb_ray_tune pid=2376722)[0m [285]	train-mlogloss:1.57268	validation-mlogloss:1.63639
[36m(train_xgb_ray_tune pid=2376743)[0m [343]	train-mlogloss:1.94527	validation-mlogloss:1.95978
[36m(train_xgb_ray_tune pid=2376746)[0m [289]	train-mlogloss:1.65733	validation-mlogloss:1.71085
[36m(train_xgb_ray_tune pid=2376741)[0m [311]	train-mlogloss:1.62338	validation-mlogloss:1.67463
[36m(train_xgb_ray_tune pid=2376747)[0m [273]	train-mlogloss:1.55363	validation-mlogloss:1.63421
[36m(train_xgb_ray_tune pid=2376728)[0m [319]	train-mlogloss:1.57420	validation-mlogloss:1.61055
[36m(train_xgb_ray_tune pid=2376728)[0m [320]	train-mlogloss:1.57323	validation-mlogloss:1.60973
[36m(train_xgb_ray_tune pid=2376750)[0m [279]	train-mlogloss:1.74195	validation-mlogloss:1.77558
[36m(train_xgb_ray_tune pid=2376735)[0m [356]	train-mlogloss:1.90563	validation-mlogloss:1.89538
[36m(train_xgb_ray_tune pid=2376735)[0m [357]	train-mlogloss:1.90462	validation-mlogloss:1.89438
[36m(train_xgb_ray_tune pid=2376740)[0m [452]	train-mlogloss:2.47418	validation-mlogloss:2.46755
[36m(train_xgb_ray_tune pid=2376749)[0m [348]	train-mlogloss:2.02582	validation-mlogloss:2.01272
[36m(train_xgb_ray_tune pid=2376748)[0m [346]	train-mlogloss:1.88531	validation-mlogloss:1.88380
[36m(train_xgb_ray_tune pid=2376729)[0m [350]	train-mlogloss:1.57755	validation-mlogloss:1.63185
[36m(train_xgb_ray_tune pid=2376741)[0m [312]	train-mlogloss:1.62235	validation-mlogloss:1.67373
[36m(train_xgb_ray_tune pid=2376743)[0m [344]	train-mlogloss:1.94430	validation-mlogloss:1.95878
[36m(train_xgb_ray_tune pid=2376740)[0m [453]	train-mlogloss:2.47418	validation-mlogloss:2.46755
[36m(train_xgb_ray_tune pid=2376749)[0m [349]	train-mlogloss:2.02488	validation-mlogloss:2.01181
[36m(train_xgb_ray_tune pid=2376748)[0m [347]	train-mlogloss:1.88429	validation-mlogloss:1.88286
[36m(train_xgb_ray_tune pid=2376748)[0m [348]	train-mlogloss:1.88325	validation-mlogloss:1.88185
[36m(train_xgb_ray_tune pid=2376737)[0m [337]	train-mlogloss:1.64657	validation-mlogloss:1.66749
[36m(train_xgb_ray_tune pid=2376736)[0m [356]	train-mlogloss:1.80235	validation-mlogloss:1.81665
[36m(train_xgb_ray_tune pid=2376729)[0m [351]	train-mlogloss:1.57721	validation-mlogloss:1.63153
[36m(train_xgb_ray_tune pid=2376729)[0m [352]	train-mlogloss:1.57678	validation-mlogloss:1.63119
[36m(train_xgb_ray_tune pid=2376722)[0m [286]	train-mlogloss:1.57161	validation-mlogloss:1.63557
[36m(train_xgb_ray_tune pid=2376743)[0m [345]	train-mlogloss:1.94335	validation-mlogloss:1.95785
[36m(train_xgb_ray_tune pid=2376746)[0m [290]	train-mlogloss:1.65610	validation-mlogloss:1.70978
[36m(train_xgb_ray_tune pid=2376741)[0m [313]	train-mlogloss:1.62134	validation-mlogloss:1.67279
[36m(train_xgb_ray_tune pid=2376747)[0m [274]	train-mlogloss:1.55250	validation-mlogloss:1.63307
[36m(train_xgb_ray_tune pid=2376728)[0m [321]	train-mlogloss:1.57233	validation-mlogloss:1.60895
[36m(train_xgb_ray_tune pid=2376728)[0m [322]	train-mlogloss:1.57138	validation-mlogloss:1.60811
[36m(train_xgb_ray_tune pid=2376750)[0m [280]	train-mlogloss:1.74084	validation-mlogloss:1.77453
[36m(train_xgb_ray_tune pid=2376735)[0m [358]	train-mlogloss:1.90363	validation-mlogloss:1.89343
[36m(train_xgb_ray_tune pid=2376735)[0m [359]	train-mlogloss:1.90277	validation-mlogloss:1.89256
[36m(train_xgb_ray_tune pid=2376740)[0m [454]	train-mlogloss:2.47408	validation-mlogloss:2.46744
[36m(train_xgb_ray_tune pid=2376749)[0m [350]	train-mlogloss:2.02398	validation-mlogloss:2.01088

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:41. Total running time: 54s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             51.6578 │
│ time_total_s                 51.6578 │
│ training_iteration                 1 │
│ validation_accuracy           0.4083 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:41. Total running time: 54s
[36m(train_xgb_ray_tune pid=2376748)[0m [349]	train-mlogloss:1.88229	validation-mlogloss:1.88093
[36m(train_xgb_ray_tune pid=2376748)[0m [350]	train-mlogloss:1.88129	validation-mlogloss:1.87997
[36m(train_xgb_ray_tune pid=2376737)[0m [338]	train-mlogloss:1.64589	validation-mlogloss:1.66679
[36m(train_xgb_ray_tune pid=2376737)[0m [339]	train-mlogloss:1.64532	validation-mlogloss:1.66629
[36m(train_xgb_ray_tune pid=2376736)[0m [357]	train-mlogloss:1.80144	validation-mlogloss:1.81585
[36m(train_xgb_ray_tune pid=2376729)[0m [353]	train-mlogloss:1.57639	validation-mlogloss:1.63082
[36m(train_xgb_ray_tune pid=2376729)[0m [354]	train-mlogloss:1.57589	validation-mlogloss:1.63051
[36m(train_xgb_ray_tune pid=2376722)[0m [287]	train-mlogloss:1.57050	validation-mlogloss:1.63467
[36m(train_xgb_ray_tune pid=2376743)[0m [346]	train-mlogloss:1.94243	validation-mlogloss:1.95693
[36m(train_xgb_ray_tune pid=2376746)[0m [291]	train-mlogloss:1.65484	validation-mlogloss:1.70863
[36m(train_xgb_ray_tune pid=2376741)[0m [314]	train-mlogloss:1.62033	validation-mlogloss:1.67192
[36m(train_xgb_ray_tune pid=2376747)[0m [275]	train-mlogloss:1.55130	validation-mlogloss:1.63212
[36m(train_xgb_ray_tune pid=2376728)[0m [323]	train-mlogloss:1.57043	validation-mlogloss:1.60735
[36m(train_xgb_ray_tune pid=2376728)[0m [324]	train-mlogloss:1.56960	validation-mlogloss:1.60652
[36m(train_xgb_ray_tune pid=2376750)[0m [281]	train-mlogloss:1.73958	validation-mlogloss:1.77343
[36m(train_xgb_ray_tune pid=2376735)[0m [360]	train-mlogloss:1.90177	validation-mlogloss:1.89158
[36m(train_xgb_ray_tune pid=2376735)[0m [361]	train-mlogloss:1.90073	validation-mlogloss:1.89045
[36m(train_xgb_ray_tune pid=2376740)[0m [455]	train-mlogloss:2.47408	validation-mlogloss:2.46744
[36m(train_xgb_ray_tune pid=2376740)[0m [456]	train-mlogloss:2.47408	validation-mlogloss:2.46744
[36m(train_xgb_ray_tune pid=2376749)[0m [351]	train-mlogloss:2.02305	validation-mlogloss:2.00997
[36m(train_xgb_ray_tune pid=2376736)[0m [358]	train-mlogloss:1.80066	validation-mlogloss:1.81509
[36m(train_xgb_ray_tune pid=2376748)[0m [351]	train-mlogloss:1.88030	validation-mlogloss:1.87895
[36m(train_xgb_ray_tune pid=2376748)[0m [352]	train-mlogloss:1.87936	validation-mlogloss:1.87801
[36m(train_xgb_ray_tune pid=2376737)[0m [340]	train-mlogloss:1.64471	validation-mlogloss:1.66580
[36m(train_xgb_ray_tune pid=2376737)[0m [341]	train-mlogloss:1.64414	validation-mlogloss:1.66515
[36m(train_xgb_ray_tune pid=2376736)[0m [359]	train-mlogloss:1.79983	validation-mlogloss:1.81430
[36m(train_xgb_ray_tune pid=2376729)[0m [355]	train-mlogloss:1.57533	validation-mlogloss:1.63000
[36m(train_xgb_ray_tune pid=2376729)[0m [356]	train-mlogloss:1.57484	validation-mlogloss:1.62966
[36m(train_xgb_ray_tune pid=2376722)[0m [288]	train-mlogloss:1.56935	validation-mlogloss:1.63369
[36m(train_xgb_ray_tune pid=2376722)[0m [289]	train-mlogloss:1.56825	validation-mlogloss:1.63281
[36m(train_xgb_ray_tune pid=2376743)[0m [347]	train-mlogloss:1.94152	validation-mlogloss:1.95605
[36m(train_xgb_ray_tune pid=2376746)[0m [292]	train-mlogloss:1.65362	validation-mlogloss:1.70740
[36m(train_xgb_ray_tune pid=2376741)[0m [315]	train-mlogloss:1.61940	validation-mlogloss:1.67103
[36m(train_xgb_ray_tune pid=2376747)[0m [276]	train-mlogloss:1.55006	validation-mlogloss:1.63112
[36m(train_xgb_ray_tune pid=2376728)[0m [325]	train-mlogloss:1.56871	validation-mlogloss:1.60580
[36m(train_xgb_ray_tune pid=2376728)[0m [326]	train-mlogloss:1.56784	validation-mlogloss:1.60500
[36m(train_xgb_ray_tune pid=2376750)[0m [282]	train-mlogloss:1.73846	validation-mlogloss:1.77228
[36m(train_xgb_ray_tune pid=2376735)[0m [362]	train-mlogloss:1.89982	validation-mlogloss:1.88954
[36m(train_xgb_ray_tune pid=2376735)[0m [363]	train-mlogloss:1.89893	validation-mlogloss:1.88869
[36m(train_xgb_ray_tune pid=2376740)[0m [457]	train-mlogloss:2.47408	validation-mlogloss:2.46744
[36m(train_xgb_ray_tune pid=2376749)[0m [352]	train-mlogloss:2.02208	validation-mlogloss:2.00900
[36m(train_xgb_ray_tune pid=2376748)[0m [353]	train-mlogloss:1.87841	validation-mlogloss:1.87706
[36m(train_xgb_ray_tune pid=2376737)[0m [342]	train-mlogloss:1.64355	validation-mlogloss:1.66458
[36m(train_xgb_ray_tune pid=2376736)[0m [360]	train-mlogloss:1.79897	validation-mlogloss:1.81347
[36m(train_xgb_ray_tune pid=2376729)[0m [357]	train-mlogloss:1.57441	validation-mlogloss:1.62936
[36m(train_xgb_ray_tune pid=2376729)[0m [358]	train-mlogloss:1.57399	validation-mlogloss:1.62906
[36m(train_xgb_ray_tune pid=2376722)[0m [290]	train-mlogloss:1.56719	validation-mlogloss:1.63193
[36m(train_xgb_ray_tune pid=2376743)[0m [348]	train-mlogloss:1.94055	validation-mlogloss:1.95507
[36m(train_xgb_ray_tune pid=2376746)[0m [293]	train-mlogloss:1.65241	validation-mlogloss:1.70636
[36m(train_xgb_ray_tune pid=2376741)[0m [316]	train-mlogloss:1.61840	validation-mlogloss:1.67009
[36m(train_xgb_ray_tune pid=2376747)[0m [277]	train-mlogloss:1.54893	validation-mlogloss:1.63021
[36m(train_xgb_ray_tune pid=2376728)[0m [327]	train-mlogloss:1.56707	validation-mlogloss:1.60430
[36m(train_xgb_ray_tune pid=2376750)[0m [283]	train-mlogloss:1.73730	validation-mlogloss:1.77122
[36m(train_xgb_ray_tune pid=2376735)[0m [364]	train-mlogloss:1.89793	validation-mlogloss:1.88766
[36m(train_xgb_ray_tune pid=2376735)[0m [365]	train-mlogloss:1.89695	validation-mlogloss:1.88673
[36m(train_xgb_ray_tune pid=2376740)[0m [458]	train-mlogloss:2.47408	validation-mlogloss:2.46744
[36m(train_xgb_ray_tune pid=2376740)[0m [459]	train-mlogloss:2.47388	validation-mlogloss:2.46725
[36m(train_xgb_ray_tune pid=2376749)[0m [353]	train-mlogloss:2.02113	validation-mlogloss:2.00808
[36m(train_xgb_ray_tune pid=2376748)[0m [354]	train-mlogloss:1.87746	validation-mlogloss:1.87614
[36m(train_xgb_ray_tune pid=2376737)[0m [343]	train-mlogloss:1.64293	validation-mlogloss:1.66409
[36m(train_xgb_ray_tune pid=2376729)[0m [359]	train-mlogloss:1.57363	validation-mlogloss:1.62872
[36m(train_xgb_ray_tune pid=2376722)[0m [291]	train-mlogloss:1.56615	validation-mlogloss:1.63106
[36m(train_xgb_ray_tune pid=2376728)[0m [328]	train-mlogloss:1.56610	validation-mlogloss:1.60358
[36m(train_xgb_ray_tune pid=2376743)[0m [349]	train-mlogloss:1.93962	validation-mlogloss:1.95414
[36m(train_xgb_ray_tune pid=2376750)[0m [284]	train-mlogloss:1.73616	validation-mlogloss:1.77011
[36m(train_xgb_ray_tune pid=2376749)[0m [354]	train-mlogloss:2.02017	validation-mlogloss:2.00713
[36m(train_xgb_ray_tune pid=2376748)[0m [355]	train-mlogloss:1.87644	validation-mlogloss:1.87513
[36m(train_xgb_ray_tune pid=2376748)[0m [356]	train-mlogloss:1.87554	validation-mlogloss:1.87426
[36m(train_xgb_ray_tune pid=2376737)[0m [344]	train-mlogloss:1.64230	validation-mlogloss:1.66346
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[36m(train_xgb_ray_tune pid=2376750)[0m [326]	train-mlogloss:1.69294	validation-mlogloss:1.72926
[36m(train_xgb_ray_tune pid=2376748)[0m [399]	train-mlogloss:1.83798	validation-mlogloss:1.83842
[36m(train_xgb_ray_tune pid=2376737)[0m [401]	train-mlogloss:1.61758	validation-mlogloss:1.64195
[36m(train_xgb_ray_tune pid=2376737)[0m [402]	train-mlogloss:1.61722	validation-mlogloss:1.64171
[36m(train_xgb_ray_tune pid=2376736)[0m [402]	train-mlogloss:1.76608	validation-mlogloss:1.78236
[36m(train_xgb_ray_tune pid=2376729)[0m [424]	train-mlogloss:1.55299	validation-mlogloss:1.61068
[36m(train_xgb_ray_tune pid=2376722)[0m [348]	train-mlogloss:1.51323	validation-mlogloss:1.58620
[36m(train_xgb_ray_tune pid=2376746)[0m [329]	train-mlogloss:1.61207	validation-mlogloss:1.67081
[36m(train_xgb_ray_tune pid=2376741)[0m [355]	train-mlogloss:1.58397	validation-mlogloss:1.63910
[36m(train_xgb_ray_tune pid=2376747)[0m [330]	train-mlogloss:1.49480	validation-mlogloss:1.58777
[36m(train_xgb_ray_tune pid=2376747)[0m [331]	train-mlogloss:1.49393	validation-mlogloss:1.58727
[36m(train_xgb_ray_tune pid=2376728)[0m [389]	train-mlogloss:1.51942	validation-mlogloss:1.56420
[36m(train_xgb_ray_tune pid=2376750)[0m [327]	train-mlogloss:1.69207	validation-mlogloss:1.72836
[36m(train_xgb_ray_tune pid=2376735)[0m [420]	train-mlogloss:1.84972	validation-mlogloss:1.84062
[36m(train_xgb_ray_tune pid=2376735)[0m [421]	train-mlogloss:1.84893	validation-mlogloss:1.83982
[36m(train_xgb_ray_tune pid=2376740)[0m [513]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376740)[0m [514]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376743)[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=2376743)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=2376749)[0m [396]	train-mlogloss:1.98512	validation-mlogloss:1.97274
[36m(train_xgb_ray_tune pid=2376736)[0m [403]	train-mlogloss:1.76539	validation-mlogloss:1.78171
[36m(train_xgb_ray_tune pid=2376741)[0m [356]	train-mlogloss:1.58317	validation-mlogloss:1.63841
[36m(train_xgb_ray_tune pid=2376748)[0m [400]	train-mlogloss:1.83712	validation-mlogloss:1.83760
[36m(train_xgb_ray_tune pid=2376737)[0m [403]	train-mlogloss:1.61686	validation-mlogloss:1.64138
[36m(train_xgb_ray_tune pid=2376737)[0m [404]	train-mlogloss:1.61661	validation-mlogloss:1.64117
[36m(train_xgb_ray_tune pid=2376736)[0m [404]	train-mlogloss:1.76468	validation-mlogloss:1.78103
[36m(train_xgb_ray_tune pid=2376729)[0m [425]	train-mlogloss:1.55281	validation-mlogloss:1.61058
[36m(train_xgb_ray_tune pid=2376729)[0m [426]	train-mlogloss:1.55261	validation-mlogloss:1.61043
[36m(train_xgb_ray_tune pid=2376722)[0m [349]	train-mlogloss:1.51244	validation-mlogloss:1.58548
[36m(train_xgb_ray_tune pid=2376722)[0m [350]	train-mlogloss:1.51170	validation-mlogloss:1.58480
[36m(train_xgb_ray_tune pid=2376746)[0m [330]	train-mlogloss:1.61101	validation-mlogloss:1.66989
[36m(train_xgb_ray_tune pid=2376741)[0m [357]	train-mlogloss:1.58236	validation-mlogloss:1.63769
[36m(train_xgb_ray_tune pid=2376747)[0m [332]	train-mlogloss:1.49296	validation-mlogloss:1.58657
[36m(train_xgb_ray_tune pid=2376728)[0m [390]	train-mlogloss:1.51883	validation-mlogloss:1.56368
[36m(train_xgb_ray_tune pid=2376750)[0m [328]	train-mlogloss:1.69118	validation-mlogloss:1.72754
[36m(train_xgb_ray_tune pid=2376735)[0m [422]	train-mlogloss:1.84815	validation-mlogloss:1.83903
[36m(train_xgb_ray_tune pid=2376735)[0m [423]	train-mlogloss:1.84738	validation-mlogloss:1.83825
[36m(train_xgb_ray_tune pid=2376740)[0m [515]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [397]	train-mlogloss:1.98446	validation-mlogloss:1.97211
[36m(train_xgb_ray_tune pid=2376748)[0m [401]	train-mlogloss:1.83633	validation-mlogloss:1.83681
[36m(train_xgb_ray_tune pid=2376747)[0m [333]	train-mlogloss:1.49214	validation-mlogloss:1.58580
[36m(train_xgb_ray_tune pid=2376740)[0m [516]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376728)[0m [391]	train-mlogloss:1.51820	validation-mlogloss:1.56313
[36m(train_xgb_ray_tune pid=2376737)[0m [405]	train-mlogloss:1.61639	validation-mlogloss:1.64101
[36m(train_xgb_ray_tune pid=2376749)[0m [398]	train-mlogloss:1.98372	validation-mlogloss:1.97142
[36m(train_xgb_ray_tune pid=2376748)[0m [402]	train-mlogloss:1.83556	validation-mlogloss:1.83608
[36m(train_xgb_ray_tune pid=2376737)[0m [406]	train-mlogloss:1.61615	validation-mlogloss:1.64073
[36m(train_xgb_ray_tune pid=2376736)[0m [405]	train-mlogloss:1.76397	validation-mlogloss:1.78042
[36m(train_xgb_ray_tune pid=2376729)[0m [427]	train-mlogloss:1.55245	validation-mlogloss:1.61028
[36m(train_xgb_ray_tune pid=2376722)[0m [351]	train-mlogloss:1.51088	validation-mlogloss:1.58410
[36m(train_xgb_ray_tune pid=2376746)[0m [331]	train-mlogloss:1.61002	validation-mlogloss:1.66900
[36m(train_xgb_ray_tune pid=2376741)[0m [358]	train-mlogloss:1.58155	validation-mlogloss:1.63694
[36m(train_xgb_ray_tune pid=2376747)[0m [334]	train-mlogloss:1.49127	validation-mlogloss:1.58504
[36m(train_xgb_ray_tune pid=2376728)[0m [392]	train-mlogloss:1.51758	validation-mlogloss:1.56260
[36m(train_xgb_ray_tune pid=2376750)[0m [329]	train-mlogloss:1.69020	validation-mlogloss:1.72659
[36m(train_xgb_ray_tune pid=2376750)[0m [330]	train-mlogloss:1.68929	validation-mlogloss:1.72571
[36m(train_xgb_ray_tune pid=2376735)[0m [424]	train-mlogloss:1.84661	validation-mlogloss:1.83753
[36m(train_xgb_ray_tune pid=2376735)[0m [425]	train-mlogloss:1.84594	validation-mlogloss:1.83688
[36m(train_xgb_ray_tune pid=2376740)[0m [517]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [399]	train-mlogloss:1.98295	validation-mlogloss:1.97064
[36m(train_xgb_ray_tune pid=2376737)[0m [407]	train-mlogloss:1.61576	validation-mlogloss:1.64046
[36m(train_xgb_ray_tune pid=2376722)[0m [352]	train-mlogloss:1.51013	validation-mlogloss:1.58343

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:46. Total running time: 59s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             56.7127 │
│ time_total_s                 56.7127 │
│ training_iteration                 1 │
│ validation_accuracy          0.42648 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:46. Total running time: 59s
[36m(train_xgb_ray_tune pid=2376748)[0m [403]	train-mlogloss:1.83481	validation-mlogloss:1.83539
[36m(train_xgb_ray_tune pid=2376737)[0m [408]	train-mlogloss:1.61538	validation-mlogloss:1.64012
[36m(train_xgb_ray_tune pid=2376737)[0m [409]	train-mlogloss:1.61520	validation-mlogloss:1.63991
[36m(train_xgb_ray_tune pid=2376736)[0m [406]	train-mlogloss:1.76337	validation-mlogloss:1.77980
[36m(train_xgb_ray_tune pid=2376736)[0m [407]	train-mlogloss:1.76271	validation-mlogloss:1.77919
[36m(train_xgb_ray_tune pid=2376729)[0m [428]	train-mlogloss:1.55222	validation-mlogloss:1.61011
[36m(train_xgb_ray_tune pid=2376729)[0m [429]	train-mlogloss:1.55204	validation-mlogloss:1.60999
[36m(train_xgb_ray_tune pid=2376722)[0m [353]	train-mlogloss:1.50944	validation-mlogloss:1.58284
[36m(train_xgb_ray_tune pid=2376746)[0m [332]	train-mlogloss:1.60903	validation-mlogloss:1.66809
[36m(train_xgb_ray_tune pid=2376741)[0m [359]	train-mlogloss:1.58074	validation-mlogloss:1.63632
[36m(train_xgb_ray_tune pid=2376747)[0m [335]	train-mlogloss:1.49040	validation-mlogloss:1.58442
[36m(train_xgb_ray_tune pid=2376747)[0m [336]	train-mlogloss:1.48948	validation-mlogloss:1.58385
[36m(train_xgb_ray_tune pid=2376728)[0m [393]	train-mlogloss:1.51696	validation-mlogloss:1.56206
[36m(train_xgb_ray_tune pid=2376750)[0m [331]	train-mlogloss:1.68839	validation-mlogloss:1.72487
[36m(train_xgb_ray_tune pid=2376735)[0m [426]	train-mlogloss:1.84522	validation-mlogloss:1.83624
[36m(train_xgb_ray_tune pid=2376735)[0m [427]	train-mlogloss:1.84451	validation-mlogloss:1.83557
[36m(train_xgb_ray_tune pid=2376740)[0m [518]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376740)[0m [519]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [400]	train-mlogloss:1.98221	validation-mlogloss:1.96987
[36m(train_xgb_ray_tune pid=2376750)[0m [332]	train-mlogloss:1.68743	validation-mlogloss:1.72393
[36m(train_xgb_ray_tune pid=2376722)[0m [354]	train-mlogloss:1.50863	validation-mlogloss:1.58226
[36m(train_xgb_ray_tune pid=2376749)[0m [401]	train-mlogloss:1.98142	validation-mlogloss:1.96914
[36m(train_xgb_ray_tune pid=2376748)[0m [404]	train-mlogloss:1.83401	validation-mlogloss:1.83457
[36m(train_xgb_ray_tune pid=2376737)[0m [410]	train-mlogloss:1.61491	validation-mlogloss:1.63971
[36m(train_xgb_ray_tune pid=2376737)[0m [411]	train-mlogloss:1.61462	validation-mlogloss:1.63950
[36m(train_xgb_ray_tune pid=2376736)[0m [408]	train-mlogloss:1.76208	validation-mlogloss:1.77856
[36m(train_xgb_ray_tune pid=2376729)[0m [430]	train-mlogloss:1.55187	validation-mlogloss:1.60990
[36m(train_xgb_ray_tune pid=2376729)[0m [431]	train-mlogloss:1.55172	validation-mlogloss:1.60978
[36m(train_xgb_ray_tune pid=2376722)[0m [355]	train-mlogloss:1.50780	validation-mlogloss:1.58153
[36m(train_xgb_ray_tune pid=2376746)[0m [333]	train-mlogloss:1.60795	validation-mlogloss:1.66720
[36m(train_xgb_ray_tune pid=2376741)[0m [360]	train-mlogloss:1.57996	validation-mlogloss:1.63574
[36m(train_xgb_ray_tune pid=2376747)[0m [337]	train-mlogloss:1.48864	validation-mlogloss:1.58318
[36m(train_xgb_ray_tune pid=2376728)[0m [394]	train-mlogloss:1.51630	validation-mlogloss:1.56160
[36m(train_xgb_ray_tune pid=2376728)[0m [395]	train-mlogloss:1.51564	validation-mlogloss:1.56112
[36m(train_xgb_ray_tune pid=2376750)[0m [333]	train-mlogloss:1.68652	validation-mlogloss:1.72304
[36m(train_xgb_ray_tune pid=2376735)[0m [428]	train-mlogloss:1.84370	validation-mlogloss:1.83479
[36m(train_xgb_ray_tune pid=2376735)[0m [429]	train-mlogloss:1.84293	validation-mlogloss:1.83410
[36m(train_xgb_ray_tune pid=2376740)[0m [520]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [402]	train-mlogloss:1.98052	validation-mlogloss:1.96829
[36m(train_xgb_ray_tune pid=2376740)[0m [521]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [403]	train-mlogloss:1.97982	validation-mlogloss:1.96756
[36m(train_xgb_ray_tune pid=2376747)[0m [338]	train-mlogloss:1.48775	validation-mlogloss:1.58252
[36m(train_xgb_ray_tune pid=2376735)[0m [430]	train-mlogloss:1.84221	validation-mlogloss:1.83341
[36m(train_xgb_ray_tune pid=2376748)[0m [405]	train-mlogloss:1.83320	validation-mlogloss:1.83385
[36m(train_xgb_ray_tune pid=2376741)[0m [361]	train-mlogloss:1.57912	validation-mlogloss:1.63509
[36m(train_xgb_ray_tune pid=2376737)[0m [412]	train-mlogloss:1.61433	validation-mlogloss:1.63930
[36m(train_xgb_ray_tune pid=2376750)[0m [334]	train-mlogloss:1.68565	validation-mlogloss:1.72222
[36m(train_xgb_ray_tune pid=2376728)[0m [396]	train-mlogloss:1.51495	validation-mlogloss:1.56072
[36m(train_xgb_ray_tune pid=2376748)[0m [406]	train-mlogloss:1.83244	validation-mlogloss:1.83310
[36m(train_xgb_ray_tune pid=2376737)[0m [413]	train-mlogloss:1.61394	validation-mlogloss:1.63904
[36m(train_xgb_ray_tune pid=2376736)[0m [409]	train-mlogloss:1.76140	validation-mlogloss:1.77788
[36m(train_xgb_ray_tune pid=2376729)[0m [432]	train-mlogloss:1.55152	validation-mlogloss:1.60958
[36m(train_xgb_ray_tune pid=2376722)[0m [356]	train-mlogloss:1.50702	validation-mlogloss:1.58089
[36m(train_xgb_ray_tune pid=2376722)[0m [357]	train-mlogloss:1.50625	validation-mlogloss:1.58016
[36m(train_xgb_ray_tune pid=2376746)[0m [334]	train-mlogloss:1.60697	validation-mlogloss:1.66652
[36m(train_xgb_ray_tune pid=2376741)[0m [362]	train-mlogloss:1.57830	validation-mlogloss:1.63441
[36m(train_xgb_ray_tune pid=2376747)[0m [339]	train-mlogloss:1.48687	validation-mlogloss:1.58180
[36m(train_xgb_ray_tune pid=2376728)[0m [397]	train-mlogloss:1.51435	validation-mlogloss:1.56022
[36m(train_xgb_ray_tune pid=2376750)[0m [335]	train-mlogloss:1.68480	validation-mlogloss:1.72138
[36m(train_xgb_ray_tune pid=2376735)[0m [431]	train-mlogloss:1.84149	validation-mlogloss:1.83267
[36m(train_xgb_ray_tune pid=2376735)[0m [432]	train-mlogloss:1.84076	validation-mlogloss:1.83191
[36m(train_xgb_ray_tune pid=2376740)[0m [522]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [404]	train-mlogloss:1.97910	validation-mlogloss:1.96680
[36m(train_xgb_ray_tune pid=2376749)[0m [405]	train-mlogloss:1.97822	validation-mlogloss:1.96593
[36m(train_xgb_ray_tune pid=2376737)[0m [414]	train-mlogloss:1.61366	validation-mlogloss:1.63878
[36m(train_xgb_ray_tune pid=2376736)[0m [410]	train-mlogloss:1.76072	validation-mlogloss:1.77720
[36m(train_xgb_ray_tune pid=2376729)[0m [433]	train-mlogloss:1.55135	validation-mlogloss:1.60944
[36m(train_xgb_ray_tune pid=2376740)[0m [523]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376747)[0m [340]	train-mlogloss:1.48600	validation-mlogloss:1.58112
[36m(train_xgb_ray_tune pid=2376746)[0m [335]	train-mlogloss:1.60599	validation-mlogloss:1.66562
[36m(train_xgb_ray_tune pid=2376728)[0m [398]	train-mlogloss:1.51374	validation-mlogloss:1.55968
[36m(train_xgb_ray_tune pid=2376748)[0m [407]	train-mlogloss:1.83163	validation-mlogloss:1.83224
[36m(train_xgb_ray_tune pid=2376737)[0m [415]	train-mlogloss:1.61339	validation-mlogloss:1.63859
[36m(train_xgb_ray_tune pid=2376737)[0m [416]	train-mlogloss:1.61313	validation-mlogloss:1.63840
[36m(train_xgb_ray_tune pid=2376736)[0m [411]	train-mlogloss:1.75999	validation-mlogloss:1.77657
[36m(train_xgb_ray_tune pid=2376729)[0m [434]	train-mlogloss:1.55113	validation-mlogloss:1.60924
[36m(train_xgb_ray_tune pid=2376722)[0m [358]	train-mlogloss:1.50543	validation-mlogloss:1.57943
[36m(train_xgb_ray_tune pid=2376722)[0m [359]	train-mlogloss:1.50473	validation-mlogloss:1.57882
[36m(train_xgb_ray_tune pid=2376746)[0m [336]	train-mlogloss:1.60496	validation-mlogloss:1.66481
[36m(train_xgb_ray_tune pid=2376741)[0m [363]	train-mlogloss:1.57750	validation-mlogloss:1.63371
[36m(train_xgb_ray_tune pid=2376747)[0m [341]	train-mlogloss:1.48501	validation-mlogloss:1.58043
[36m(train_xgb_ray_tune pid=2376728)[0m [399]	train-mlogloss:1.51305	validation-mlogloss:1.55932
[36m(train_xgb_ray_tune pid=2376750)[0m [336]	train-mlogloss:1.68394	validation-mlogloss:1.72058
[36m(train_xgb_ray_tune pid=2376750)[0m [337]	train-mlogloss:1.68302	validation-mlogloss:1.71967
[36m(train_xgb_ray_tune pid=2376735)[0m [433]	train-mlogloss:1.84005	validation-mlogloss:1.83121
[36m(train_xgb_ray_tune pid=2376735)[0m [434]	train-mlogloss:1.83928	validation-mlogloss:1.83043
[36m(train_xgb_ray_tune pid=2376740)[0m [524]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [406]	train-mlogloss:1.97757	validation-mlogloss:1.96534
[36m(train_xgb_ray_tune pid=2376749)[0m [407]	train-mlogloss:1.97683	validation-mlogloss:1.96462

Trial status: 13 RUNNING | 7 TERMINATED
Current time: 2025-11-07 10:18:46. Total running time: 1min 0s
Logical resource usage: 13.0/20 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_9fac0    RUNNING                    629             3        0.0129937       0.550764             0.826163    6.03809                  189       1.39658       47.2362              4621                                                     │
│ trial_9fac0    RUNNING                    496             3        0.011035        0.782066             0.57042     9.27748                  175       8.37826        6.3327              5415                                                     │
│ trial_9fac0    RUNNING                    458             3        0.0130302       0.668953             0.523103    5.35156                   64      60.2809         5.22198             5380                                                     │
│ trial_9fac0    RUNNING                    466             2        0.00866783      0.506976             0.814022   21.9078                   170       3.50686      253.511               6833                                                     │
│ trial_9fac0    RUNNING                    665             2        0.0142689       0.700216             0.722649   22.8491                   126       9.67768      139.085               6238                                                     │
│ trial_9fac0    RUNNING                    373             3        0.00947212      0.564613             0.636997   12.8265                    76       2.40208       49.9773              7375                                                     │
│ trial_9fac0    RUNNING                    507             3        0.0103495       0.821424             0.79813    12.3559                    83       6.42268        6.56943              354                                                     │
│ trial_9fac0    RUNNING                    677             3        0.00832983      0.81227              0.590491   12.3513                   179       2.83345       12.1536              7308                                                     │
│ trial_9fac0    RUNNING                    596             3        0.0133168       0.711692             0.693198    5.42144                  143      49.0868       214.24                3090                                                     │
│ trial_9fac0    RUNNING                    423             3        0.0131105       0.651097             0.668651   19.0739                   168       6.14307      683.04                 900                                                     │
│ trial_9fac0    RUNNING                    674             2        0.0105649       0.591425             0.549683   15.0153                   193      52.9773       522.183               3209                                                     │
│ trial_9fac0    RUNNING                    685             2        0.0140109       0.745984             0.730149   14.4615                    89     401.815         55.8077              2699                                                     │
│ trial_9fac0    RUNNING                    677             3        0.0123745       0.610346             0.800135   11.9738                   113      31.1399       397.982               5500                                                     │
│ trial_9fac0    TERMINATED                 367             2        0.0119098       0.822007             0.650588   16.299                    145      20.0921       538.425               1516        1            51.6578                0.4083   │
│ trial_9fac0    TERMINATED                 356             2        0.0138383       0.824493             0.601674   25.6598                    95     465.168          7.60973             8067        1            42.3991                0.22747  │
│ trial_9fac0    TERMINATED                 267             2        0.018387        0.590628             0.699044   16.8385                   165       7.23861      436.303               3292        1            44.8149                0.414625 │
│ trial_9fac0    TERMINATED                 387             3        0.0055591       0.717148             0.568672   17.6475                    81      67.7854       119.06                5871        1            56.7127                0.426482 │
│ trial_9fac0    TERMINATED                 251             2        0.0151609       0.841882             0.513321   29.2414                   177       9.6365         6.78319             3251        1            41.8084                0.431423 │
│ trial_9fac0    TERMINATED                 209             3        0.0129513       0.629164             0.631645    9.7387                    70       2.36629        5.1895              4523        1            41.5151                0.463241 │
│ trial_9fac0    TERMINATED                 256             2        0.0194417       0.816468             0.590046    5.98402                  117     750.545        251.807                392        1            33.3086                0.131621 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[36m(train_xgb_ray_tune pid=2376748)[0m [408]	train-mlogloss:1.83089	validation-mlogloss:1.83148
[36m(train_xgb_ray_tune pid=2376737)[0m [417]	train-mlogloss:1.61284	validation-mlogloss:1.63823
[36m(train_xgb_ray_tune pid=2376737)[0m [418]	train-mlogloss:1.61254	validation-mlogloss:1.63794
[36m(train_xgb_ray_tune pid=2376736)[0m [412]	train-mlogloss:1.75930	validation-mlogloss:1.77595
[36m(train_xgb_ray_tune pid=2376729)[0m [435]	train-mlogloss:1.55098	validation-mlogloss:1.60912
[36m(train_xgb_ray_tune pid=2376729)[0m [436]	train-mlogloss:1.55075	validation-mlogloss:1.60893
[36m(train_xgb_ray_tune pid=2376722)[0m [360]	train-mlogloss:1.50390	validation-mlogloss:1.57812
[36m(train_xgb_ray_tune pid=2376746)[0m [337]	train-mlogloss:1.60396	validation-mlogloss:1.66380
[36m(train_xgb_ray_tune pid=2376741)[0m [364]	train-mlogloss:1.57673	validation-mlogloss:1.63302
[36m(train_xgb_ray_tune pid=2376747)[0m [342]	train-mlogloss:1.48423	validation-mlogloss:1.57975
[36m(train_xgb_ray_tune pid=2376747)[0m [343]	train-mlogloss:1.48340	validation-mlogloss:1.57919
[36m(train_xgb_ray_tune pid=2376728)[0m [400]	train-mlogloss:1.51239	validation-mlogloss:1.55873
[36m(train_xgb_ray_tune pid=2376728)[0m [401]	train-mlogloss:1.51172	validation-mlogloss:1.55838
[36m(train_xgb_ray_tune pid=2376750)[0m [338]	train-mlogloss:1.68219	validation-mlogloss:1.71892
[36m(train_xgb_ray_tune pid=2376735)[0m [435]	train-mlogloss:1.83866	validation-mlogloss:1.82973
[36m(train_xgb_ray_tune pid=2376735)[0m [436]	train-mlogloss:1.83786	validation-mlogloss:1.82894
[36m(train_xgb_ray_tune pid=2376740)[0m [525]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376740)[0m [526]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [408]	train-mlogloss:1.97610	validation-mlogloss:1.96391
[36m(train_xgb_ray_tune pid=2376749)[0m [409]	train-mlogloss:1.97524	validation-mlogloss:1.96306
[36m(train_xgb_ray_tune pid=2376722)[0m [361]	train-mlogloss:1.50319	validation-mlogloss:1.57750
[36m(train_xgb_ray_tune pid=2376750)[0m [339]	train-mlogloss:1.68126	validation-mlogloss:1.71797
[36m(train_xgb_ray_tune pid=2376748)[0m [409]	train-mlogloss:1.83008	validation-mlogloss:1.83063
[36m(train_xgb_ray_tune pid=2376737)[0m [419]	train-mlogloss:1.61236	validation-mlogloss:1.63774
[36m(train_xgb_ray_tune pid=2376737)[0m [420]	train-mlogloss:1.61192	validation-mlogloss:1.63739
[36m(train_xgb_ray_tune pid=2376736)[0m [413]	train-mlogloss:1.75865	validation-mlogloss:1.77531
[36m(train_xgb_ray_tune pid=2376729)[0m [437]	train-mlogloss:1.55050	validation-mlogloss:1.60876
[36m(train_xgb_ray_tune pid=2376722)[0m [362]	train-mlogloss:1.50240	validation-mlogloss:1.57683
[36m(train_xgb_ray_tune pid=2376741)[0m [365]	train-mlogloss:1.57589	validation-mlogloss:1.63231
[36m(train_xgb_ray_tune pid=2376747)[0m [344]	train-mlogloss:1.48253	validation-mlogloss:1.57851
[36m(train_xgb_ray_tune pid=2376728)[0m [402]	train-mlogloss:1.51110	validation-mlogloss:1.55787
[36m(train_xgb_ray_tune pid=2376728)[0m [403]	train-mlogloss:1.51050	validation-mlogloss:1.55738
[36m(train_xgb_ray_tune pid=2376750)[0m [340]	train-mlogloss:1.68039	validation-mlogloss:1.71710
[36m(train_xgb_ray_tune pid=2376735)[0m [437]	train-mlogloss:1.83714	validation-mlogloss:1.82823
[36m(train_xgb_ray_tune pid=2376735)[0m [438]	train-mlogloss:1.83638	validation-mlogloss:1.82755
[36m(train_xgb_ray_tune pid=2376740)[0m [527]	train-mlogloss:2.47255	validation-mlogloss:2.46591
[36m(train_xgb_ray_tune pid=2376749)[0m [410]	train-mlogloss:1.97449	validation-mlogloss:1.96237
[36m(train_xgb_ray_tune pid=2376749)[0m [411]	train-mlogloss:1.97383	validation-mlogloss:1.96174
[36m(train_xgb_ray_tune pid=2376748)[0m [410]	train-mlogloss:1.82932	validation-mlogloss:1.82989
[36m(train_xgb_ray_tune pid=2376746)[0m [338]	train-mlogloss:1.60293	validation-mlogloss:1.66283
[36m(train_xgb_ray_tune pid=2376737)[0m [421]	train-mlogloss:1.61169	validation-mlogloss:1.63724
[36m(train_xgb_ray_tune pid=2376736)[0m [414]	train-mlogloss:1.75802	validation-mlogloss:1.77472
[36m(train_xgb_ray_tune pid=2376740)[0m [528]	train-mlogloss:2.47246	validation-mlogloss:2.46581
[36m(train_xgb_ray_tune pid=2376747)[0m [345]	train-mlogloss:1.48177	validation-mlogloss:1.57793
[36m(train_xgb_ray_tune pid=2376748)[0m [411]	train-mlogloss:1.82857	validation-mlogloss:1.82917
[36m(train_xgb_ray_tune pid=2376737)[0m [422]	train-mlogloss:1.61137	validation-mlogloss:1.63701
[36m(train_xgb_ray_tune pid=2376737)[0m [423]	train-mlogloss:1.61109	validation-mlogloss:1.63676
[36m(train_xgb_ray_tune pid=2376736)[0m [415]	train-mlogloss:1.75730	validation-mlogloss:1.77405
[36m(train_xgb_ray_tune pid=2376729)[0m [438]	train-mlogloss:1.55035	validation-mlogloss:1.60863
[36m(train_xgb_ray_tune pid=2376729)[0m [439]	train-mlogloss:1.55007	validation-mlogloss:1.60842
[36m(train_xgb_ray_tune pid=2376722)[0m [363]	train-mlogloss:1.50162	validation-mlogloss:1.57610
[36m(train_xgb_ray_tune pid=2376722)[0m [364]	train-mlogloss:1.50088	validation-mlogloss:1.57560
[36m(train_xgb_ray_tune pid=2376746)[0m [339]	train-mlogloss:1.60200	validation-mlogloss:1.66197
[36m(train_xgb_ray_tune pid=2376741)[0m [366]	train-mlogloss:1.57517	validation-mlogloss:1.63166
[36m(train_xgb_ray_tune pid=2376747)[0m [346]	train-mlogloss:1.48096	validation-mlogloss:1.57738
[36m(train_xgb_ray_tune pid=2376728)[0m [404]	train-mlogloss:1.50988	validation-mlogloss:1.55680
[36m(train_xgb_ray_tune pid=2376728)[0m [405]	train-mlogloss:1.50922	validation-mlogloss:1.55630
[36m(train_xgb_ray_tune pid=2376750)[0m [341]	train-mlogloss:1.67957	validation-mlogloss:1.71638
[36m(train_xgb_ray_tune pid=2376750)[0m [342]	train-mlogloss:1.67871	validation-mlogloss:1.71562
[36m(train_xgb_ray_tune pid=2376735)[0m [439]	train-mlogloss:1.83569	validation-mlogloss:1.82688
[36m(train_xgb_ray_tune pid=2376735)[0m [440]	train-mlogloss:1.83498	validation-mlogloss:1.82622
[36m(train_xgb_ray_tune pid=2376740)[0m [529]	train-mlogloss:2.47246	validation-mlogloss:2.46581
[36m(train_xgb_ray_tune pid=2376749)[0m [412]	train-mlogloss:1.97311	validation-mlogloss:1.96101
[36m(train_xgb_ray_tune pid=2376749)[0m [413]	train-mlogloss:1.97230	validation-mlogloss:1.96022
[36m(train_xgb_ray_tune pid=2376748)[0m [412]	train-mlogloss:1.82771	validation-mlogloss:1.82830
[36m(train_xgb_ray_tune pid=2376737)[0m [424]	train-mlogloss:1.61085	validation-mlogloss:1.63658
[36m(train_xgb_ray_tune pid=2376737)[0m [425]	train-mlogloss:1.61056	validation-mlogloss:1.63639
[36m(train_xgb_ray_tune pid=2376736)[0m [416]	train-mlogloss:1.75660	validation-mlogloss:1.77344
[36m(train_xgb_ray_tune pid=2376729)[0m [440]	train-mlogloss:1.54989	validation-mlogloss:1.60829
[36m(train_xgb_ray_tune pid=2376722)[0m [365]	train-mlogloss:1.50011	validation-mlogloss:1.57491
[36m(train_xgb_ray_tune pid=2376722)[0m [366]	train-mlogloss:1.49930	validation-mlogloss:1.57421
[36m(train_xgb_ray_tune pid=2376746)[0m [340]	train-mlogloss:1.60109	validation-mlogloss:1.66116
[36m(train_xgb_ray_tune pid=2376741)[0m [367]	train-mlogloss:1.57434	validation-mlogloss:1.63096
[36m(train_xgb_ray_tune pid=2376747)[0m [347]	train-mlogloss:1.48015	validation-mlogloss:1.57673
[36m(train_xgb_ray_tune pid=2376747)[0m [348]	train-mlogloss:1.47937	validation-mlogloss:1.57605
[36m(train_xgb_ray_tune pid=2376728)[0m [406]	train-mlogloss:1.50858	validation-mlogloss:1.55579
[36m(train_xgb_ray_tune pid=2376728)[0m [407]	train-mlogloss:1.50796	validation-mlogloss:1.55525
[36m(train_xgb_ray_tune pid=2376750)[0m [343]	train-mlogloss:1.67799	validation-mlogloss:1.71489
[36m(train_xgb_ray_tune pid=2376735)[0m [441]	train-mlogloss:1.83429	validation-mlogloss:1.82553
[36m(train_xgb_ray_tune pid=2376735)[0m [442]	train-mlogloss:1.83349	validation-mlogloss:1.82484
[36m(train_xgb_ray_tune pid=2376740)[0m [530]	train-mlogloss:2.47246	validation-mlogloss:2.46581
[36m(train_xgb_ray_tune pid=2376740)[0m [531]	train-mlogloss:2.47246	validation-mlogloss:2.46581
[36m(train_xgb_ray_tune pid=2376749)[0m [414]	train-mlogloss:1.97159	validation-mlogloss:1.95951
[36m(train_xgb_ray_tune pid=2376749)[0m [415]	train-mlogloss:1.97085	validation-mlogloss:1.95879
[36m(train_xgb_ray_tune pid=2376741)[0m [368]	train-mlogloss:1.57351	validation-mlogloss:1.63026
[36m(train_xgb_ray_tune pid=2376748)[0m [413]	train-mlogloss:1.82685	validation-mlogloss:1.82751
[36m(train_xgb_ray_tune pid=2376737)[0m [426]	train-mlogloss:1.61021	validation-mlogloss:1.63608
[36m(train_xgb_ray_tune pid=2376737)[0m [427]	train-mlogloss:1.61003	validation-mlogloss:1.63593
[36m(train_xgb_ray_tune pid=2376736)[0m [417]	train-mlogloss:1.75592	validation-mlogloss:1.77275
[36m(train_xgb_ray_tune pid=2376729)[0m [441]	train-mlogloss:1.54972	validation-mlogloss:1.60813
[36m(train_xgb_ray_tune pid=2376729)[0m [442]	train-mlogloss:1.54955	validation-mlogloss:1.60798
[36m(train_xgb_ray_tune pid=2376722)[0m [367]	train-mlogloss:1.49847	validation-mlogloss:1.57342
[36m(train_xgb_ray_tune pid=2376746)[0m [341]	train-mlogloss:1.60014	validation-mlogloss:1.66032
[36m(train_xgb_ray_tune pid=2376741)[0m [369]	train-mlogloss:1.57277	validation-mlogloss:1.62972
[36m(train_xgb_ray_tune pid=2376747)[0m [349]	train-mlogloss:1.47855	validation-mlogloss:1.57550
[36m(train_xgb_ray_tune pid=2376728)[0m [408]	train-mlogloss:1.50734	validation-mlogloss:1.55474
[36m(train_xgb_ray_tune pid=2376728)[0m [409]	train-mlogloss:1.50675	validation-mlogloss:1.55416
[36m(train_xgb_ray_tune pid=2376750)[0m [344]	train-mlogloss:1.67715	validation-mlogloss:1.71414
[36m(train_xgb_ray_tune pid=2376750)[0m [345]	train-mlogloss:1.67631	validation-mlogloss:1.71340
[36m(train_xgb_ray_tune pid=2376735)[0m [443]	train-mlogloss:1.83275	validation-mlogloss:1.82410
[36m(train_xgb_ray_tune pid=2376735)[0m [444]	train-mlogloss:1.83195	validation-mlogloss:1.82333
[36m(train_xgb_ray_tune pid=2376740)[0m [532]	train-mlogloss:2.47246	validation-mlogloss:2.46581
[36m(train_xgb_ray_tune pid=2376749)[0m [416]	train-mlogloss:1.97011	validation-mlogloss:1.95808
[36m(train_xgb_ray_tune pid=2376749)[0m [417]	train-mlogloss:1.96946	validation-mlogloss:1.95740
[36m(train_xgb_ray_tune pid=2376748)[0m [414]	train-mlogloss:1.82616	validation-mlogloss:1.82679
[36m(train_xgb_ray_tune pid=2376737)[0m [428]	train-mlogloss:1.60983	validation-mlogloss:1.63578
[36m(train_xgb_ray_tune pid=2376737)[0m [429]	train-mlogloss:1.60966	validation-mlogloss:1.63565
[36m(train_xgb_ray_tune pid=2376736)[0m [418]	train-mlogloss:1.75525	validation-mlogloss:1.77217
[36m(train_xgb_ray_tune pid=2376729)[0m [443]	train-mlogloss:1.54935	validation-mlogloss:1.60782
[36m(train_xgb_ray_tune pid=2376722)[0m [368]	train-mlogloss:1.49766	validation-mlogloss:1.57282
[36m(train_xgb_ray_tune pid=2376722)[0m [369]	train-mlogloss:1.49696	validation-mlogloss:1.57214
[36m(train_xgb_ray_tune pid=2376746)[0m [342]	train-mlogloss:1.59919	validation-mlogloss:1.65952
[36m(train_xgb_ray_tune pid=2376741)[0m [370]	train-mlogloss:1.57212	validation-mlogloss:1.62919
[36m(train_xgb_ray_tune pid=2376747)[0m [350]	train-mlogloss:1.47774	validation-mlogloss:1.57493
[36m(train_xgb_ray_tune pid=2376747)[0m [351]	train-mlogloss:1.47695	validation-mlogloss:1.57429
[36m(train_xgb_ray_tune pid=2376728)[0m [410]	train-mlogloss:1.50612	validation-mlogloss:1.55363
[36m(train_xgb_ray_tune pid=2376728)[0m [411]	train-mlogloss:1.50551	validation-mlogloss:1.55314
[36m(train_xgb_ray_tune pid=2376750)[0m [346]	train-mlogloss:1.67549	validation-mlogloss:1.71260
[36m(train_xgb_ray_tune pid=2376735)[0m [445]	train-mlogloss:1.83132	validation-mlogloss:1.82271
[36m(train_xgb_ray_tune pid=2376740)[0m [533]	train-mlogloss:2.47246	validation-mlogloss:2.46581
[36m(train_xgb_ray_tune pid=2376740)[0m [534]	train-mlogloss:2.47246	validation-mlogloss:2.46581
[36m(train_xgb_ray_tune pid=2376749)[0m [418]	train-mlogloss:1.96869	validation-mlogloss:1.95666
[36m(train_xgb_ray_tune pid=2376749)[0m [419]	train-mlogloss:1.96788	validation-mlogloss:1.95588
[36m(train_xgb_ray_tune pid=2376735)[0m [446]	train-mlogloss:1.83059	validation-mlogloss:1.82205
[36m(train_xgb_ray_tune pid=2376748)[0m [415]	train-mlogloss:1.82539	validation-mlogloss:1.82601
[36m(train_xgb_ray_tune pid=2376737)[0m [430]	train-mlogloss:1.60942	validation-mlogloss:1.63544
[36m(train_xgb_ray_tune pid=2376748)[0m [416]	train-mlogloss:1.82462	validation-mlogloss:1.82525
[36m(train_xgb_ray_tune pid=2376737)[0m [431]	train-mlogloss:1.60906	validation-mlogloss:1.63521
[36m(train_xgb_ray_tune pid=2376737)[0m [432]	train-mlogloss:1.60879	validation-mlogloss:1.63501
[36m(train_xgb_ray_tune pid=2376736)[0m [419]	train-mlogloss:1.75458	validation-mlogloss:1.77153
[36m(train_xgb_ray_tune pid=2376729)[0m [444]	train-mlogloss:1.54918	validation-mlogloss:1.60770
[36m(train_xgb_ray_tune pid=2376722)[0m [370]	train-mlogloss:1.49625	validation-mlogloss:1.57151
[36m(train_xgb_ray_tune pid=2376722)[0m [371]	train-mlogloss:1.49555	validation-mlogloss:1.57092
[36m(train_xgb_ray_tune pid=2376746)[0m [343]	train-mlogloss:1.59826	validation-mlogloss:1.65866
[36m(train_xgb_ray_tune pid=2376741)[0m [371]	train-mlogloss:1.57130	validation-mlogloss:1.62851
[36m(train_xgb_ray_tune pid=2376747)[0m [352]	train-mlogloss:1.47611	validation-mlogloss:1.57369
[36m(train_xgb_ray_tune pid=2376728)[0m [412]	train-mlogloss:1.50488	validation-mlogloss:1.55280
[36m(train_xgb_ray_tune pid=2376750)[0m [347]	train-mlogloss:1.67468	validation-mlogloss:1.71185
[36m(train_xgb_ray_tune pid=2376750)[0m [348]	train-mlogloss:1.67394	validation-mlogloss:1.71120
[36m(train_xgb_ray_tune pid=2376735)[0m [447]	train-mlogloss:1.82994	validation-mlogloss:1.82139
[36m(train_xgb_ray_tune pid=2376740)[0m [535]	train-mlogloss:2.47246	validation-mlogloss:2.46581
[36m(train_xgb_ray_tune pid=2376749)[0m [420]	train-mlogloss:1.96724	validation-mlogloss:1.95529
[36m(train_xgb_ray_tune pid=2376749)[0m [421]	train-mlogloss:1.96658	validation-mlogloss:1.95461
[36m(train_xgb_ray_tune pid=2376736)[0m [420]	train-mlogloss:1.75399	validation-mlogloss:1.77090
[36m(train_xgb_ray_tune pid=2376729)[0m [445]	train-mlogloss:1.54898	validation-mlogloss:1.60753
[36m(train_xgb_ray_tune pid=2376747)[0m [353]	train-mlogloss:1.47534	validation-mlogloss:1.57313
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[36m(train_xgb_ray_tune pid=2376737)[0m [452]	train-mlogloss:1.60415	validation-mlogloss:1.63101
[36m(train_xgb_ray_tune pid=2376737)[0m [453]	train-mlogloss:1.60399	validation-mlogloss:1.63090
[36m(train_xgb_ray_tune pid=2376736)[0m [431]	train-mlogloss:1.74687	validation-mlogloss:1.76412
[36m(train_xgb_ray_tune pid=2376736)[0m [432]	train-mlogloss:1.74626	validation-mlogloss:1.76353
[36m(train_xgb_ray_tune pid=2376722)[0m [387]	train-mlogloss:1.48469	validation-mlogloss:1.56128
[36m(train_xgb_ray_tune pid=2376722)[0m [388]	train-mlogloss:1.48407	validation-mlogloss:1.56068
[36m(train_xgb_ray_tune pid=2376746)[0m [353]	train-mlogloss:1.58876	validation-mlogloss:1.65033
[36m(train_xgb_ray_tune pid=2376747)[0m [368]	train-mlogloss:1.46377	validation-mlogloss:1.56490
[36m(train_xgb_ray_tune pid=2376728)[0m [430]	train-mlogloss:1.49427	validation-mlogloss:1.54477
[36m(train_xgb_ray_tune pid=2376728)[0m [431]	train-mlogloss:1.49369	validation-mlogloss:1.54422
[36m(train_xgb_ray_tune pid=2376750)[0m [363]	train-mlogloss:1.66207	validation-mlogloss:1.70047
[36m(train_xgb_ray_tune pid=2376750)[0m [364]	train-mlogloss:1.66130	validation-mlogloss:1.69978
[36m(train_xgb_ray_tune pid=2376740)[0m [550]	train-mlogloss:2.47227	validation-mlogloss:2.46561
[36m(train_xgb_ray_tune pid=2376749)[0m [440]	train-mlogloss:1.95415	validation-mlogloss:1.94231
[36m(train_xgb_ray_tune pid=2376749)[0m [441]	train-mlogloss:1.95343	validation-mlogloss:1.94159
[36m(train_xgb_ray_tune pid=2376747)[0m [369]	train-mlogloss:1.46316	validation-mlogloss:1.56439
[36m(train_xgb_ray_tune pid=2376740)[0m [551]	train-mlogloss:2.47227	validation-mlogloss:2.46561
[36m(train_xgb_ray_tune pid=2376737)[0m [454]	train-mlogloss:1.60372	validation-mlogloss:1.63066
[36m(train_xgb_ray_tune pid=2376737)[0m [455]	train-mlogloss:1.60352	validation-mlogloss:1.63050
[36m(train_xgb_ray_tune pid=2376736)[0m [433]	train-mlogloss:1.74568	validation-mlogloss:1.76296
[36m(train_xgb_ray_tune pid=2376722)[0m [389]	train-mlogloss:1.48351	validation-mlogloss:1.56009
[36m(train_xgb_ray_tune pid=2376722)[0m [390]	train-mlogloss:1.48286	validation-mlogloss:1.55958
[36m(train_xgb_ray_tune pid=2376741)[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=2376741)[0m   _log_deprecation_warning(
[36m(train_xgb_ray_tune pid=2376746)[0m [354]	train-mlogloss:1.58786	validation-mlogloss:1.64956
[36m(train_xgb_ray_tune pid=2376747)[0m [370]	train-mlogloss:1.46235	validation-mlogloss:1.56389
[36m(train_xgb_ray_tune pid=2376728)[0m [432]	train-mlogloss:1.49318	validation-mlogloss:1.54367
[36m(train_xgb_ray_tune pid=2376728)[0m [433]	train-mlogloss:1.49261	validation-mlogloss:1.54315
[36m(train_xgb_ray_tune pid=2376750)[0m [365]	train-mlogloss:1.66045	validation-mlogloss:1.69907
[36m(train_xgb_ray_tune pid=2376750)[0m [366]	train-mlogloss:1.65970	validation-mlogloss:1.69834
[36m(train_xgb_ray_tune pid=2376740)[0m [552]	train-mlogloss:2.47227	validation-mlogloss:2.46561
[36m(train_xgb_ray_tune pid=2376749)[0m [442]	train-mlogloss:1.95280	validation-mlogloss:1.94098
[36m(train_xgb_ray_tune pid=2376749)[0m [443]	train-mlogloss:1.95211	validation-mlogloss:1.94026
[36m(train_xgb_ray_tune pid=2376737)[0m [456]	train-mlogloss:1.60334	validation-mlogloss:1.63029
[36m(train_xgb_ray_tune pid=2376749)[0m [444]	train-mlogloss:1.95140	validation-mlogloss:1.93956
[36m(train_xgb_ray_tune pid=2376747)[0m [371]	train-mlogloss:1.46160	validation-mlogloss:1.56345
[36m(train_xgb_ray_tune pid=2376736)[0m [434]	train-mlogloss:1.74502	validation-mlogloss:1.76238
[36m(train_xgb_ray_tune pid=2376728)[0m [434]	train-mlogloss:1.49208	validation-mlogloss:1.54278

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:49. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.7961 │
│ time_total_s                 59.7961 │
│ training_iteration                 1 │
│ validation_accuracy          0.45632 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:49. Total running time: 1min 2s
[36m(train_xgb_ray_tune pid=2376737)[0m [457]	train-mlogloss:1.60309	validation-mlogloss:1.63016
[36m(train_xgb_ray_tune pid=2376737)[0m [458]	train-mlogloss:1.60293	validation-mlogloss:1.63000
[36m(train_xgb_ray_tune pid=2376736)[0m [435]	train-mlogloss:1.74445	validation-mlogloss:1.76182
[36m(train_xgb_ray_tune pid=2376722)[0m [391]	train-mlogloss:1.48225	validation-mlogloss:1.55910
[36m(train_xgb_ray_tune pid=2376722)[0m [392]	train-mlogloss:1.48158	validation-mlogloss:1.55861
[36m(train_xgb_ray_tune pid=2376746)[0m [355]	train-mlogloss:1.58688	validation-mlogloss:1.64867
[36m(train_xgb_ray_tune pid=2376747)[0m [372]	train-mlogloss:1.46086	validation-mlogloss:1.56292
[36m(train_xgb_ray_tune pid=2376747)[0m [373]	train-mlogloss:1.46019	validation-mlogloss:1.56235
[36m(train_xgb_ray_tune pid=2376728)[0m [435]	train-mlogloss:1.49151	validation-mlogloss:1.54240
[36m(train_xgb_ray_tune pid=2376728)[0m [436]	train-mlogloss:1.49099	validation-mlogloss:1.54199
[36m(train_xgb_ray_tune pid=2376750)[0m [367]	train-mlogloss:1.65891	validation-mlogloss:1.69768
[36m(train_xgb_ray_tune pid=2376750)[0m [368]	train-mlogloss:1.65821	validation-mlogloss:1.69702
[36m(train_xgb_ray_tune pid=2376740)[0m [553]	train-mlogloss:2.47227	validation-mlogloss:2.46561
[36m(train_xgb_ray_tune pid=2376740)[0m [554]	train-mlogloss:2.47227	validation-mlogloss:2.46561
[36m(train_xgb_ray_tune pid=2376749)[0m [445]	train-mlogloss:1.95086	validation-mlogloss:1.93907
[36m(train_xgb_ray_tune pid=2376749)[0m [446]	train-mlogloss:1.95021	validation-mlogloss:1.93844
[36m(train_xgb_ray_tune pid=2376737)[0m [459]	train-mlogloss:1.60278	validation-mlogloss:1.62984
[36m(train_xgb_ray_tune pid=2376722)[0m [393]	train-mlogloss:1.48097	validation-mlogloss:1.55812
[36m(train_xgb_ray_tune pid=2376746)[0m [356]	train-mlogloss:1.58600	validation-mlogloss:1.64790
[36m(train_xgb_ray_tune pid=2376749)[0m [447]	train-mlogloss:1.94954	validation-mlogloss:1.93783
[36m(train_xgb_ray_tune pid=2376740)[0m [555]	train-mlogloss:2.47218	validation-mlogloss:2.46552
[36m(train_xgb_ray_tune pid=2376736)[0m [436]	train-mlogloss:1.74381	validation-mlogloss:1.76121

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:49. Total running time: 1min 2s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             59.5212 │
│ time_total_s                 59.5212 │
│ training_iteration                 1 │
│ validation_accuracy          0.41126 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:49. Total running time: 1min 2s
[36m(train_xgb_ray_tune pid=2376737)[0m [460]	train-mlogloss:1.60256	validation-mlogloss:1.62967
[36m(train_xgb_ray_tune pid=2376737)[0m [461]	train-mlogloss:1.60231	validation-mlogloss:1.62950
[36m(train_xgb_ray_tune pid=2376736)[0m [437]	train-mlogloss:1.74322	validation-mlogloss:1.76067
[36m(train_xgb_ray_tune pid=2376722)[0m [394]	train-mlogloss:1.48031	validation-mlogloss:1.55763
[36m(train_xgb_ray_tune pid=2376722)[0m [395]	train-mlogloss:1.47971	validation-mlogloss:1.55720
[36m(train_xgb_ray_tune pid=2376746)[0m [357]	train-mlogloss:1.58508	validation-mlogloss:1.64712
[36m(train_xgb_ray_tune pid=2376747)[0m [374]	train-mlogloss:1.45947	validation-mlogloss:1.56178
[36m(train_xgb_ray_tune pid=2376747)[0m [375]	train-mlogloss:1.45881	validation-mlogloss:1.56130
[36m(train_xgb_ray_tune pid=2376728)[0m [437]	train-mlogloss:1.49045	validation-mlogloss:1.54152
[36m(train_xgb_ray_tune pid=2376728)[0m [438]	train-mlogloss:1.48992	validation-mlogloss:1.54094
[36m(train_xgb_ray_tune pid=2376750)[0m [369]	train-mlogloss:1.65741	validation-mlogloss:1.69621
[36m(train_xgb_ray_tune pid=2376750)[0m [370]	train-mlogloss:1.65669	validation-mlogloss:1.69559
[36m(train_xgb_ray_tune pid=2376740)[0m [556]	train-mlogloss:2.47218	validation-mlogloss:2.46552
[36m(train_xgb_ray_tune pid=2376749)[0m [448]	train-mlogloss:1.94889	validation-mlogloss:1.93718
[36m(train_xgb_ray_tune pid=2376749)[0m [449]	train-mlogloss:1.94833	validation-mlogloss:1.93661
[36m(train_xgb_ray_tune pid=2376737)[0m [462]	train-mlogloss:1.60205	validation-mlogloss:1.62935
[36m(train_xgb_ray_tune pid=2376740)[0m [557]	train-mlogloss:2.47218	validation-mlogloss:2.46552
[36m(train_xgb_ray_tune pid=2376736)[0m [438]	train-mlogloss:1.74268	validation-mlogloss:1.76016
[36m(train_xgb_ray_tune pid=2376737)[0m [463]	train-mlogloss:1.60192	validation-mlogloss:1.62922
[36m(train_xgb_ray_tune pid=2376737)[0m [464]	train-mlogloss:1.60168	validation-mlogloss:1.62899
[36m(train_xgb_ray_tune pid=2376737)[0m [465]	train-mlogloss:1.60147	validation-mlogloss:1.62884
[36m(train_xgb_ray_tune pid=2376736)[0m [439]	train-mlogloss:1.74201	validation-mlogloss:1.75957
[36m(train_xgb_ray_tune pid=2376736)[0m [440]	train-mlogloss:1.74142	validation-mlogloss:1.75900
[36m(train_xgb_ray_tune pid=2376722)[0m [396]	train-mlogloss:1.47912	validation-mlogloss:1.55668
[36m(train_xgb_ray_tune pid=2376722)[0m [397]	train-mlogloss:1.47858	validation-mlogloss:1.55609
[36m(train_xgb_ray_tune pid=2376746)[0m [358]	train-mlogloss:1.58418	validation-mlogloss:1.64649
[36m(train_xgb_ray_tune pid=2376747)[0m [376]	train-mlogloss:1.45812	validation-mlogloss:1.56089
[36m(train_xgb_ray_tune pid=2376747)[0m [377]	train-mlogloss:1.45734	validation-mlogloss:1.56037
[36m(train_xgb_ray_tune pid=2376728)[0m [439]	train-mlogloss:1.48938	validation-mlogloss:1.54053
[36m(train_xgb_ray_tune pid=2376728)[0m [440]	train-mlogloss:1.48880	validation-mlogloss:1.54021
[36m(train_xgb_ray_tune pid=2376728)[0m [441]	train-mlogloss:1.48830	validation-mlogloss:1.53988
[36m(train_xgb_ray_tune pid=2376750)[0m [371]	train-mlogloss:1.65601	validation-mlogloss:1.69497
[36m(train_xgb_ray_tune pid=2376750)[0m [372]	train-mlogloss:1.65525	validation-mlogloss:1.69426
[36m(train_xgb_ray_tune pid=2376740)[0m [558]	train-mlogloss:2.47218	validation-mlogloss:2.46552
[36m(train_xgb_ray_tune pid=2376749)[0m [450]	train-mlogloss:1.94766	validation-mlogloss:1.93595
[36m(train_xgb_ray_tune pid=2376749)[0m [451]	train-mlogloss:1.94714	validation-mlogloss:1.93546
[36m(train_xgb_ray_tune pid=2376749)[0m [452]	train-mlogloss:1.94654	validation-mlogloss:1.93486
[36m(train_xgb_ray_tune pid=2376737)[0m [466]	train-mlogloss:1.60136	validation-mlogloss:1.62876
[36m(train_xgb_ray_tune pid=2376737)[0m [467]	train-mlogloss:1.60126	validation-mlogloss:1.62867
[36m(train_xgb_ray_tune pid=2376736)[0m [441]	train-mlogloss:1.74077	validation-mlogloss:1.75841
[36m(train_xgb_ray_tune pid=2376736)[0m [442]	train-mlogloss:1.74025	validation-mlogloss:1.75790
[36m(train_xgb_ray_tune pid=2376722)[0m [398]	train-mlogloss:1.47793	validation-mlogloss:1.55555
[36m(train_xgb_ray_tune pid=2376722)[0m [399]	train-mlogloss:1.47731	validation-mlogloss:1.55505
[36m(train_xgb_ray_tune pid=2376746)[0m [359]	train-mlogloss:1.58321	validation-mlogloss:1.64568
[36m(train_xgb_ray_tune pid=2376747)[0m [378]	train-mlogloss:1.45660	validation-mlogloss:1.55992
[36m(train_xgb_ray_tune pid=2376747)[0m [379]	train-mlogloss:1.45587	validation-mlogloss:1.55940
[36m(train_xgb_ray_tune pid=2376728)[0m [442]	train-mlogloss:1.48778	validation-mlogloss:1.53941
[36m(train_xgb_ray_tune pid=2376728)[0m [443]	train-mlogloss:1.48724	validation-mlogloss:1.53902
[36m(train_xgb_ray_tune pid=2376750)[0m [373]	train-mlogloss:1.65465	validation-mlogloss:1.69369
[36m(train_xgb_ray_tune pid=2376750)[0m [374]	train-mlogloss:1.65390	validation-mlogloss:1.69298
[36m(train_xgb_ray_tune pid=2376740)[0m [559]	train-mlogloss:2.47218	validation-mlogloss:2.46552
[36m(train_xgb_ray_tune pid=2376740)[0m [560]	train-mlogloss:2.47218	validation-mlogloss:2.46552
[36m(train_xgb_ray_tune pid=2376749)[0m [453]	train-mlogloss:1.94598	validation-mlogloss:1.93425
[36m(train_xgb_ray_tune pid=2376749)[0m [454]	train-mlogloss:1.94543	validation-mlogloss:1.93374
[36m(train_xgb_ray_tune pid=2376736)[0m [443]	train-mlogloss:1.73968	validation-mlogloss:1.75737
[36m(train_xgb_ray_tune pid=2376750)[0m [375]	train-mlogloss:1.65320	validation-mlogloss:1.69233
[36m(train_xgb_ray_tune pid=2376746)[0m [360]	train-mlogloss:1.58233	validation-mlogloss:1.64487
[36m(train_xgb_ray_tune pid=2376737)[0m [468]	train-mlogloss:1.60103	validation-mlogloss:1.62850
[36m(train_xgb_ray_tune pid=2376749)[0m [455]	train-mlogloss:1.94481	validation-mlogloss:1.93309
[36m(train_xgb_ray_tune pid=2376740)[0m [561]	train-mlogloss:2.47218	validation-mlogloss:2.46552
[36m(train_xgb_ray_tune pid=2376722)[0m [400]	train-mlogloss:1.47670	validation-mlogloss:1.55444

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:49. Total running time: 1min 3s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             60.2651 │
│ time_total_s                 60.2651 │
│ training_iteration                 1 │
│ validation_accuracy          0.41502 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:49. Total running time: 1min 3s
[36m(train_xgb_ray_tune pid=2376737)[0m [469]	train-mlogloss:1.60093	validation-mlogloss:1.62841
[36m(train_xgb_ray_tune pid=2376737)[0m [470]	train-mlogloss:1.60072	validation-mlogloss:1.62819
[36m(train_xgb_ray_tune pid=2376736)[0m [444]	train-mlogloss:1.73912	validation-mlogloss:1.75686
[36m(train_xgb_ray_tune pid=2376736)[0m [445]	train-mlogloss:1.73853	validation-mlogloss:1.75631
[36m(train_xgb_ray_tune pid=2376722)[0m [401]	train-mlogloss:1.47610	validation-mlogloss:1.55398
[36m(train_xgb_ray_tune pid=2376746)[0m [361]	train-mlogloss:1.58147	validation-mlogloss:1.64415
[36m(train_xgb_ray_tune pid=2376746)[0m [362]	train-mlogloss:1.58057	validation-mlogloss:1.64339
[36m(train_xgb_ray_tune pid=2376747)[0m [380]	train-mlogloss:1.45525	validation-mlogloss:1.55883
[36m(train_xgb_ray_tune pid=2376747)[0m [381]	train-mlogloss:1.45458	validation-mlogloss:1.55839
[36m(train_xgb_ray_tune pid=2376728)[0m [444]	train-mlogloss:1.48669	validation-mlogloss:1.53858
[36m(train_xgb_ray_tune pid=2376728)[0m [445]	train-mlogloss:1.48615	validation-mlogloss:1.53813
[36m(train_xgb_ray_tune pid=2376750)[0m [376]	train-mlogloss:1.65250	validation-mlogloss:1.69172
[36m(train_xgb_ray_tune pid=2376750)[0m [377]	train-mlogloss:1.65182	validation-mlogloss:1.69111
[36m(train_xgb_ray_tune pid=2376740)[0m [562]	train-mlogloss:2.47218	validation-mlogloss:2.46552
[36m(train_xgb_ray_tune pid=2376740)[0m [563]	train-mlogloss:2.47209	validation-mlogloss:2.46543
[36m(train_xgb_ray_tune pid=2376749)[0m [456]	train-mlogloss:1.94422	validation-mlogloss:1.93249
[36m(train_xgb_ray_tune pid=2376749)[0m [457]	train-mlogloss:1.94363	validation-mlogloss:1.93187
[36m(train_xgb_ray_tune pid=2376736)[0m [446]	train-mlogloss:1.73809	validation-mlogloss:1.75592
[36m(train_xgb_ray_tune pid=2376728)[0m [446]	train-mlogloss:1.48560	validation-mlogloss:1.53769
[36m(train_xgb_ray_tune pid=2376737)[0m [471]	train-mlogloss:1.60054	validation-mlogloss:1.62807
[36m(train_xgb_ray_tune pid=2376722)[0m [402]	train-mlogloss:1.47550	validation-mlogloss:1.55347
[36m(train_xgb_ray_tune pid=2376737)[0m [472]	train-mlogloss:1.60036	validation-mlogloss:1.62793
[36m(train_xgb_ray_tune pid=2376737)[0m [473]	train-mlogloss:1.60023	validation-mlogloss:1.62777
[36m(train_xgb_ray_tune pid=2376736)[0m [447]	train-mlogloss:1.73760	validation-mlogloss:1.75546
[36m(train_xgb_ray_tune pid=2376736)[0m [448]	train-mlogloss:1.73701	validation-mlogloss:1.75492
[36m(train_xgb_ray_tune pid=2376722)[0m [403]	train-mlogloss:1.47494	validation-mlogloss:1.55292
[36m(train_xgb_ray_tune pid=2376722)[0m [404]	train-mlogloss:1.47434	validation-mlogloss:1.55236
[36m(train_xgb_ray_tune pid=2376746)[0m [363]	train-mlogloss:1.57968	validation-mlogloss:1.64264
[36m(train_xgb_ray_tune pid=2376746)[0m [364]	train-mlogloss:1.57880	validation-mlogloss:1.64185
[36m(train_xgb_ray_tune pid=2376747)[0m [382]	train-mlogloss:1.45394	validation-mlogloss:1.55796
[36m(train_xgb_ray_tune pid=2376747)[0m [383]	train-mlogloss:1.45327	validation-mlogloss:1.55757
[36m(train_xgb_ray_tune pid=2376728)[0m [447]	train-mlogloss:1.48510	validation-mlogloss:1.53724
[36m(train_xgb_ray_tune pid=2376728)[0m [448]	train-mlogloss:1.48459	validation-mlogloss:1.53690
[36m(train_xgb_ray_tune pid=2376750)[0m [378]	train-mlogloss:1.65112	validation-mlogloss:1.69044
[36m(train_xgb_ray_tune pid=2376750)[0m [379]	train-mlogloss:1.65039	validation-mlogloss:1.68978
[36m(train_xgb_ray_tune pid=2376740)[0m [564]	train-mlogloss:2.47209	validation-mlogloss:2.46543
[36m(train_xgb_ray_tune pid=2376740)[0m [565]	train-mlogloss:2.47200	validation-mlogloss:2.46534
[36m(train_xgb_ray_tune pid=2376740)[0m [566]	train-mlogloss:2.47200	validation-mlogloss:2.46534
[36m(train_xgb_ray_tune pid=2376749)[0m [458]	train-mlogloss:1.94299	validation-mlogloss:1.93122
[36m(train_xgb_ray_tune pid=2376749)[0m [459]	train-mlogloss:1.94244	validation-mlogloss:1.93065
[36m(train_xgb_ray_tune pid=2376749)[0m [460]	train-mlogloss:1.94192	validation-mlogloss:1.93009
[36m(train_xgb_ray_tune pid=2376740)[0m [567]	train-mlogloss:2.47200	validation-mlogloss:2.46534
[36m(train_xgb_ray_tune pid=2376737)[0m [474]	train-mlogloss:1.60004	validation-mlogloss:1.62759

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:49. Total running time: 1min 3s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             60.2786 │
│ time_total_s                 60.2786 │
│ training_iteration                 1 │
│ validation_accuracy           0.4583 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:49. Total running time: 1min 3s
[36m(train_xgb_ray_tune pid=2376737)[0m [475]	train-mlogloss:1.59979	validation-mlogloss:1.62741
[36m(train_xgb_ray_tune pid=2376737)[0m [476]	train-mlogloss:1.59969	validation-mlogloss:1.62730
[36m(train_xgb_ray_tune pid=2376736)[0m [449]	train-mlogloss:1.73636	validation-mlogloss:1.75434
[36m(train_xgb_ray_tune pid=2376736)[0m [450]	train-mlogloss:1.73576	validation-mlogloss:1.75376
[36m(train_xgb_ray_tune pid=2376736)[0m [451]	train-mlogloss:1.73518	validation-mlogloss:1.75319
[36m(train_xgb_ray_tune pid=2376722)[0m [405]	train-mlogloss:1.47377	validation-mlogloss:1.55194
[36m(train_xgb_ray_tune pid=2376722)[0m [406]	train-mlogloss:1.47318	validation-mlogloss:1.55141
[36m(train_xgb_ray_tune pid=2376746)[0m [365]	train-mlogloss:1.57789	validation-mlogloss:1.64110
[36m(train_xgb_ray_tune pid=2376746)[0m [366]	train-mlogloss:1.57696	validation-mlogloss:1.64039
[36m(train_xgb_ray_tune pid=2376747)[0m [384]	train-mlogloss:1.45260	validation-mlogloss:1.55716
[36m(train_xgb_ray_tune pid=2376747)[0m [385]	train-mlogloss:1.45185	validation-mlogloss:1.55656
[36m(train_xgb_ray_tune pid=2376728)[0m [449]	train-mlogloss:1.48407	validation-mlogloss:1.53649
[36m(train_xgb_ray_tune pid=2376728)[0m [450]	train-mlogloss:1.48352	validation-mlogloss:1.53610
[36m(train_xgb_ray_tune pid=2376750)[0m [380]	train-mlogloss:1.64965	validation-mlogloss:1.68911
[36m(train_xgb_ray_tune pid=2376750)[0m [381]	train-mlogloss:1.64897	validation-mlogloss:1.68850
[36m(train_xgb_ray_tune pid=2376740)[0m [568]	train-mlogloss:2.47200	validation-mlogloss:2.46534
[36m(train_xgb_ray_tune pid=2376740)[0m [569]	train-mlogloss:2.47200	validation-mlogloss:2.46534
[36m(train_xgb_ray_tune pid=2376740)[0m [570]	train-mlogloss:2.47191	validation-mlogloss:2.46526
[36m(train_xgb_ray_tune pid=2376749)[0m [461]	train-mlogloss:1.94133	validation-mlogloss:1.92952
[36m(train_xgb_ray_tune pid=2376749)[0m [462]	train-mlogloss:1.94076	validation-mlogloss:1.92893
[36m(train_xgb_ray_tune pid=2376737)[0m [477]	train-mlogloss:1.59957	validation-mlogloss:1.62716
[36m(train_xgb_ray_tune pid=2376737)[0m [478]	train-mlogloss:1.59940	validation-mlogloss:1.62702
[36m(train_xgb_ray_tune pid=2376737)[0m [479]	train-mlogloss:1.59918	validation-mlogloss:1.62682
[36m(train_xgb_ray_tune pid=2376736)[0m [452]	train-mlogloss:1.73464	validation-mlogloss:1.75266
[36m(train_xgb_ray_tune pid=2376736)[0m [453]	train-mlogloss:1.73424	validation-mlogloss:1.75222
[36m(train_xgb_ray_tune pid=2376722)[0m [407]	train-mlogloss:1.47259	validation-mlogloss:1.55096
[36m(train_xgb_ray_tune pid=2376722)[0m [408]	train-mlogloss:1.47197	validation-mlogloss:1.55042
[36m(train_xgb_ray_tune pid=2376746)[0m [367]	train-mlogloss:1.57610	validation-mlogloss:1.63972
[36m(train_xgb_ray_tune pid=2376746)[0m [368]	train-mlogloss:1.57525	validation-mlogloss:1.63902
[36m(train_xgb_ray_tune pid=2376747)[0m [386]	train-mlogloss:1.45120	validation-mlogloss:1.55613
[36m(train_xgb_ray_tune pid=2376747)[0m [387]	train-mlogloss:1.45047	validation-mlogloss:1.55559
[36m(train_xgb_ray_tune pid=2376728)[0m [451]	train-mlogloss:1.48296	validation-mlogloss:1.53574
[36m(train_xgb_ray_tune pid=2376728)[0m [452]	train-mlogloss:1.48251	validation-mlogloss:1.53538
[36m(train_xgb_ray_tune pid=2376728)[0m [453]	train-mlogloss:1.48207	validation-mlogloss:1.53506
[36m(train_xgb_ray_tune pid=2376750)[0m [382]	train-mlogloss:1.64825	validation-mlogloss:1.68791
[36m(train_xgb_ray_tune pid=2376750)[0m [383]	train-mlogloss:1.64750	validation-mlogloss:1.68721
[36m(train_xgb_ray_tune pid=2376740)[0m [571]	train-mlogloss:2.47191	validation-mlogloss:2.46526
[36m(train_xgb_ray_tune pid=2376740)[0m [572]	train-mlogloss:2.47191	validation-mlogloss:2.46526
[36m(train_xgb_ray_tune pid=2376740)[0m [573]	train-mlogloss:2.47191	validation-mlogloss:2.46526
[36m(train_xgb_ray_tune pid=2376740)[0m [574]	train-mlogloss:2.47191	validation-mlogloss:2.46526
[36m(train_xgb_ray_tune pid=2376749)[0m [463]	train-mlogloss:1.94026	validation-mlogloss:1.92843
[36m(train_xgb_ray_tune pid=2376749)[0m [464]	train-mlogloss:1.93969	validation-mlogloss:1.92796
[36m(train_xgb_ray_tune pid=2376749)[0m [465]	train-mlogloss:1.93920	validation-mlogloss:1.92746
[36m(train_xgb_ray_tune pid=2376737)[0m [480]	train-mlogloss:1.59908	validation-mlogloss:1.62676
[36m(train_xgb_ray_tune pid=2376737)[0m [481]	train-mlogloss:1.59895	validation-mlogloss:1.62665
[36m(train_xgb_ray_tune pid=2376737)[0m [482]	train-mlogloss:1.59872	validation-mlogloss:1.62650
[36m(train_xgb_ray_tune pid=2376736)[0m [454]	train-mlogloss:1.73371	validation-mlogloss:1.75171
[36m(train_xgb_ray_tune pid=2376736)[0m [455]	train-mlogloss:1.73317	validation-mlogloss:1.75119
[36m(train_xgb_ray_tune pid=2376736)[0m [456]	train-mlogloss:1.73263	validation-mlogloss:1.75071
[36m(train_xgb_ray_tune pid=2376722)[0m [409]	train-mlogloss:1.47135	validation-mlogloss:1.54996
[36m(train_xgb_ray_tune pid=2376722)[0m [410]	train-mlogloss:1.47084	validation-mlogloss:1.54946
[36m(train_xgb_ray_tune pid=2376746)[0m [369]	train-mlogloss:1.57440	validation-mlogloss:1.63818
[36m(train_xgb_ray_tune pid=2376746)[0m [370]	train-mlogloss:1.57354	validation-mlogloss:1.63741
[36m(train_xgb_ray_tune pid=2376747)[0m [388]	train-mlogloss:1.44978	validation-mlogloss:1.55508
[36m(train_xgb_ray_tune pid=2376747)[0m [389]	train-mlogloss:1.44915	validation-mlogloss:1.55472
[36m(train_xgb_ray_tune pid=2376728)[0m [454]	train-mlogloss:1.48152	validation-mlogloss:1.53458
[36m(train_xgb_ray_tune pid=2376728)[0m [455]	train-mlogloss:1.48100	validation-mlogloss:1.53409
[36m(train_xgb_ray_tune pid=2376750)[0m [384]	train-mlogloss:1.64676	validation-mlogloss:1.68655
[36m(train_xgb_ray_tune pid=2376750)[0m [385]	train-mlogloss:1.64607	validation-mlogloss:1.68590
[36m(train_xgb_ray_tune pid=2376740)[0m [575]	train-mlogloss:2.47191	validation-mlogloss:2.46526
[36m(train_xgb_ray_tune pid=2376740)[0m [576]	train-mlogloss:2.47191	validation-mlogloss:2.46526
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[36m(train_xgb_ray_tune pid=2376749)[0m [466]	train-mlogloss:1.93863	validation-mlogloss:1.92687
[36m(train_xgb_ray_tune pid=2376749)[0m [467]	train-mlogloss:1.93802	validation-mlogloss:1.92632
[36m(train_xgb_ray_tune pid=2376737)[0m [483]	train-mlogloss:1.59858	validation-mlogloss:1.62639
[36m(train_xgb_ray_tune pid=2376746)[0m [371]	train-mlogloss:1.57278	validation-mlogloss:1.63669
[36m(train_xgb_ray_tune pid=2376749)[0m [468]	train-mlogloss:1.93742	validation-mlogloss:1.92569
[36m(train_xgb_ray_tune pid=2376722)[0m [411]	train-mlogloss:1.47026	validation-mlogloss:1.54890
[36m(train_xgb_ray_tune pid=2376737)[0m [484]	train-mlogloss:1.59838	validation-mlogloss:1.62623
[36m(train_xgb_ray_tune pid=2376737)[0m [485]	train-mlogloss:1.59819	validation-mlogloss:1.62611
[36m(train_xgb_ray_tune pid=2376737)[0m [486]	train-mlogloss:1.59811	validation-mlogloss:1.62604
[36m(train_xgb_ray_tune pid=2376736)[0m [457]	train-mlogloss:1.73206	validation-mlogloss:1.75022
[36m(train_xgb_ray_tune pid=2376736)[0m [458]	train-mlogloss:1.73151	validation-mlogloss:1.74970
[36m(train_xgb_ray_tune pid=2376722)[0m [412]	train-mlogloss:1.46965	validation-mlogloss:1.54841
[36m(train_xgb_ray_tune pid=2376722)[0m [413]	train-mlogloss:1.46905	validation-mlogloss:1.54804
[36m(train_xgb_ray_tune pid=2376746)[0m [372]	train-mlogloss:1.57191	validation-mlogloss:1.63591
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[36m(train_xgb_ray_tune pid=2376747)[0m [390]	train-mlogloss:1.44851	validation-mlogloss:1.55434
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[36m(train_xgb_ray_tune pid=2376728)[0m [456]	train-mlogloss:1.48045	validation-mlogloss:1.53379
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[36m(train_xgb_ray_tune pid=2376750)[0m [386]	train-mlogloss:1.64540	validation-mlogloss:1.68523
[36m(train_xgb_ray_tune pid=2376750)[0m [387]	train-mlogloss:1.64471	validation-mlogloss:1.68461
[36m(train_xgb_ray_tune pid=2376740)[0m [578]	train-mlogloss:2.47191	validation-mlogloss:2.46526
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[36m(train_xgb_ray_tune pid=2376749)[0m [469]	train-mlogloss:1.93683	validation-mlogloss:1.92517
[36m(train_xgb_ray_tune pid=2376749)[0m [470]	train-mlogloss:1.93630	validation-mlogloss:1.92465
[36m(train_xgb_ray_tune pid=2376747)[0m [392]	train-mlogloss:1.44720	validation-mlogloss:1.55342
[36m(train_xgb_ray_tune pid=2376728)[0m [458]	train-mlogloss:1.47944	validation-mlogloss:1.53306
[36m(train_xgb_ray_tune pid=2376736)[0m [459]	train-mlogloss:1.73091	validation-mlogloss:1.74912
[36m(train_xgb_ray_tune pid=2376737)[0m [487]	train-mlogloss:1.59793	validation-mlogloss:1.62587
[36m(train_xgb_ray_tune pid=2376737)[0m [488]	train-mlogloss:1.59775	validation-mlogloss:1.62572
[36m(train_xgb_ray_tune pid=2376737)[0m [489]	train-mlogloss:1.59758	validation-mlogloss:1.62557
[36m(train_xgb_ray_tune pid=2376736)[0m [460]	train-mlogloss:1.73032	validation-mlogloss:1.74854
[36m(train_xgb_ray_tune pid=2376736)[0m [461]	train-mlogloss:1.72978	validation-mlogloss:1.74800
[36m(train_xgb_ray_tune pid=2376722)[0m [414]	train-mlogloss:1.46853	validation-mlogloss:1.54757
[36m(train_xgb_ray_tune pid=2376722)[0m [415]	train-mlogloss:1.46792	validation-mlogloss:1.54701
[36m(train_xgb_ray_tune pid=2376746)[0m [374]	train-mlogloss:1.57020	validation-mlogloss:1.63449
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[36m(train_xgb_ray_tune pid=2376728)[0m [459]	train-mlogloss:1.47892	validation-mlogloss:1.53266
[36m(train_xgb_ray_tune pid=2376728)[0m [460]	train-mlogloss:1.47839	validation-mlogloss:1.53222
[36m(train_xgb_ray_tune pid=2376750)[0m [388]	train-mlogloss:1.64402	validation-mlogloss:1.68398
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[36m(train_xgb_ray_tune pid=2376740)[0m [582]	train-mlogloss:2.47183	validation-mlogloss:2.46518
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[36m(train_xgb_ray_tune pid=2376749)[0m [471]	train-mlogloss:1.93578	validation-mlogloss:1.92416
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[36m(train_xgb_ray_tune pid=2376749)[0m [473]	train-mlogloss:1.93458	validation-mlogloss:1.92306
[36m(train_xgb_ray_tune pid=2376737)[0m [490]	train-mlogloss:1.59731	validation-mlogloss:1.62530
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[36m(train_xgb_ray_tune pid=2376736)[0m [462]	train-mlogloss:1.72925	validation-mlogloss:1.74742
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[36m(train_xgb_ray_tune pid=2376722)[0m [416]	train-mlogloss:1.46741	validation-mlogloss:1.54654
[36m(train_xgb_ray_tune pid=2376722)[0m [417]	train-mlogloss:1.46675	validation-mlogloss:1.54603
[36m(train_xgb_ray_tune pid=2376746)[0m [376]	train-mlogloss:1.56848	validation-mlogloss:1.63311
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[36m(train_xgb_ray_tune pid=2376728)[0m [461]	train-mlogloss:1.47781	validation-mlogloss:1.53179
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[36m(train_xgb_ray_tune pid=2376750)[0m [390]	train-mlogloss:1.64265	validation-mlogloss:1.68281
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[36m(train_xgb_ray_tune pid=2376740)[0m [585]	train-mlogloss:2.47163	validation-mlogloss:2.46498
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[36m(train_xgb_ray_tune pid=2376749)[0m [474]	train-mlogloss:1.93408	validation-mlogloss:1.92257
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[36m(train_xgb_ray_tune pid=2376736)[0m [464]	train-mlogloss:1.72825	validation-mlogloss:1.74647
[36m(train_xgb_ray_tune pid=2376749)[0m [476]	train-mlogloss:1.93307	validation-mlogloss:1.92158
[36m(train_xgb_ray_tune pid=2376737)[0m [492]	train-mlogloss:1.59698	validation-mlogloss:1.62500
[36m(train_xgb_ray_tune pid=2376722)[0m [418]	train-mlogloss:1.46620	validation-mlogloss:1.54560
[36m(train_xgb_ray_tune pid=2376746)[0m [378]	train-mlogloss:1.56685	validation-mlogloss:1.63181
[36m(train_xgb_ray_tune pid=2376750)[0m [392]	train-mlogloss:1.64130	validation-mlogloss:1.68151
[36m(train_xgb_ray_tune pid=2376740)[0m [589]	train-mlogloss:2.47163	validation-mlogloss:2.46498
[36m(train_xgb_ray_tune pid=2376737)[0m [493]	train-mlogloss:1.59689	validation-mlogloss:1.62490
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[36m(train_xgb_ray_tune pid=2376737)[0m [495]	train-mlogloss:1.59664	validation-mlogloss:1.62467
[36m(train_xgb_ray_tune pid=2376736)[0m [465]	train-mlogloss:1.72780	validation-mlogloss:1.74609
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[36m(train_xgb_ray_tune pid=2376722)[0m [419]	train-mlogloss:1.46569	validation-mlogloss:1.54514
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[36m(train_xgb_ray_tune pid=2376728)[0m [465]	train-mlogloss:1.47579	validation-mlogloss:1.53025
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[36m(train_xgb_ray_tune pid=2376737)[0m [517]	train-mlogloss:1.59373	validation-mlogloss:1.62209
[36m(train_xgb_ray_tune pid=2376737)[0m [518]	train-mlogloss:1.59357	validation-mlogloss:1.62192
[36m(train_xgb_ray_tune pid=2376737)[0m [519]	train-mlogloss:1.59340	validation-mlogloss:1.62179
[36m(train_xgb_ray_tune pid=2376736)[0m [486]	train-mlogloss:1.71776	validation-mlogloss:1.73685
[36m(train_xgb_ray_tune pid=2376736)[0m [487]	train-mlogloss:1.71733	validation-mlogloss:1.73644
[36m(train_xgb_ray_tune pid=2376736)[0m [488]	train-mlogloss:1.71681	validation-mlogloss:1.73600
[36m(train_xgb_ray_tune pid=2376722)[0m [436]	train-mlogloss:1.45632	validation-mlogloss:1.53807
[36m(train_xgb_ray_tune pid=2376722)[0m [437]	train-mlogloss:1.45588	validation-mlogloss:1.53778
[36m(train_xgb_ray_tune pid=2376746)[0m [397]	train-mlogloss:1.55168	validation-mlogloss:1.61923
[36m(train_xgb_ray_tune pid=2376746)[0m [398]	train-mlogloss:1.55086	validation-mlogloss:1.61849
[36m(train_xgb_ray_tune pid=2376747)[0m [414]	train-mlogloss:1.43354	validation-mlogloss:1.54447
[36m(train_xgb_ray_tune pid=2376747)[0m [415]	train-mlogloss:1.43292	validation-mlogloss:1.54402
[36m(train_xgb_ray_tune pid=2376728)[0m [482]	train-mlogloss:1.46771	validation-mlogloss:1.52452
[36m(train_xgb_ray_tune pid=2376728)[0m [483]	train-mlogloss:1.46726	validation-mlogloss:1.52419
[36m(train_xgb_ray_tune pid=2376750)[0m [410]	train-mlogloss:1.62942	validation-mlogloss:1.67046
[36m(train_xgb_ray_tune pid=2376750)[0m [411]	train-mlogloss:1.62881	validation-mlogloss:1.66990
[36m(train_xgb_ray_tune pid=2376740)[0m [619]	train-mlogloss:2.47132	validation-mlogloss:2.46465
[36m(train_xgb_ray_tune pid=2376740)[0m [620]	train-mlogloss:2.47132	validation-mlogloss:2.46465
[36m(train_xgb_ray_tune pid=2376740)[0m [621]	train-mlogloss:2.47132	validation-mlogloss:2.46465
[36m(train_xgb_ray_tune pid=2376749)[0m [498]	train-mlogloss:1.92140	validation-mlogloss:1.91015
[36m(train_xgb_ray_tune pid=2376749)[0m [499]	train-mlogloss:1.92089	validation-mlogloss:1.90966
[36m(train_xgb_ray_tune pid=2376749)[0m [500]	train-mlogloss:1.92044	validation-mlogloss:1.90918
[36m(train_xgb_ray_tune pid=2376722)[0m [438]	train-mlogloss:1.45535	validation-mlogloss:1.53742
[36m(train_xgb_ray_tune pid=2376737)[0m [520]	train-mlogloss:1.59331	validation-mlogloss:1.62174
[36m(train_xgb_ray_tune pid=2376737)[0m [521]	train-mlogloss:1.59322	validation-mlogloss:1.62164
[36m(train_xgb_ray_tune pid=2376737)[0m [522]	train-mlogloss:1.59316	validation-mlogloss:1.62159
[36m(train_xgb_ray_tune pid=2376737)[0m [523]	train-mlogloss:1.59302	validation-mlogloss:1.62146
[36m(train_xgb_ray_tune pid=2376736)[0m [489]	train-mlogloss:1.71633	validation-mlogloss:1.73557
[36m(train_xgb_ray_tune pid=2376736)[0m [490]	train-mlogloss:1.71589	validation-mlogloss:1.73514
[36m(train_xgb_ray_tune pid=2376736)[0m [491]	train-mlogloss:1.71543	validation-mlogloss:1.73467
[36m(train_xgb_ray_tune pid=2376722)[0m [439]	train-mlogloss:1.45480	validation-mlogloss:1.53696
[36m(train_xgb_ray_tune pid=2376722)[0m [440]	train-mlogloss:1.45428	validation-mlogloss:1.53665
[36m(train_xgb_ray_tune pid=2376746)[0m [399]	train-mlogloss:1.55013	validation-mlogloss:1.61789
[36m(train_xgb_ray_tune pid=2376746)[0m [400]	train-mlogloss:1.54940	validation-mlogloss:1.61729
[36m(train_xgb_ray_tune pid=2376747)[0m [416]	train-mlogloss:1.43236	validation-mlogloss:1.54349
[36m(train_xgb_ray_tune pid=2376747)[0m [417]	train-mlogloss:1.43181	validation-mlogloss:1.54309
[36m(train_xgb_ray_tune pid=2376728)[0m [484]	train-mlogloss:1.46679	validation-mlogloss:1.52380
[36m(train_xgb_ray_tune pid=2376728)[0m [485]	train-mlogloss:1.46626	validation-mlogloss:1.52334
[36m(train_xgb_ray_tune pid=2376750)[0m [412]	train-mlogloss:1.62819	validation-mlogloss:1.66932
[36m(train_xgb_ray_tune pid=2376750)[0m [413]	train-mlogloss:1.62761	validation-mlogloss:1.66880
[36m(train_xgb_ray_tune pid=2376749)[0m [501]	train-mlogloss:1.91996	validation-mlogloss:1.90869
[36m(train_xgb_ray_tune pid=2376749)[0m [502]	train-mlogloss:1.91954	validation-mlogloss:1.90828
[36m(train_xgb_ray_tune pid=2376749)[0m [503]	train-mlogloss:1.91896	validation-mlogloss:1.90776
[36m(train_xgb_ray_tune pid=2376746)[0m [401]	train-mlogloss:1.54863	validation-mlogloss:1.61673
[36m(train_xgb_ray_tune pid=2376728)[0m [486]	train-mlogloss:1.46581	validation-mlogloss:1.52292
[36m(train_xgb_ray_tune pid=2376737)[0m [524]	train-mlogloss:1.59291	validation-mlogloss:1.62136
[36m(train_xgb_ray_tune pid=2376737)[0m [525]	train-mlogloss:1.59282	validation-mlogloss:1.62126
[36m(train_xgb_ray_tune pid=2376737)[0m [526]	train-mlogloss:1.59266	validation-mlogloss:1.62113
[36m(train_xgb_ray_tune pid=2376736)[0m [492]	train-mlogloss:1.71490	validation-mlogloss:1.73416
[36m(train_xgb_ray_tune pid=2376736)[0m [493]	train-mlogloss:1.71440	validation-mlogloss:1.73369
[36m(train_xgb_ray_tune pid=2376722)[0m [441]	train-mlogloss:1.45381	validation-mlogloss:1.53617
[36m(train_xgb_ray_tune pid=2376722)[0m [442]	train-mlogloss:1.45336	validation-mlogloss:1.53574
[36m(train_xgb_ray_tune pid=2376746)[0m [402]	train-mlogloss:1.54788	validation-mlogloss:1.61610
[36m(train_xgb_ray_tune pid=2376746)[0m [403]	train-mlogloss:1.54715	validation-mlogloss:1.61546
[36m(train_xgb_ray_tune pid=2376747)[0m [418]	train-mlogloss:1.43114	validation-mlogloss:1.54270
[36m(train_xgb_ray_tune pid=2376747)[0m [419]	train-mlogloss:1.43047	validation-mlogloss:1.54225
[36m(train_xgb_ray_tune pid=2376728)[0m [487]	train-mlogloss:1.46540	validation-mlogloss:1.52264
[36m(train_xgb_ray_tune pid=2376728)[0m [488]	train-mlogloss:1.46493	validation-mlogloss:1.52220
[36m(train_xgb_ray_tune pid=2376750)[0m [414]	train-mlogloss:1.62705	validation-mlogloss:1.66828
[36m(train_xgb_ray_tune pid=2376750)[0m [415]	train-mlogloss:1.62642	validation-mlogloss:1.66772
[36m(train_xgb_ray_tune pid=2376749)[0m [504]	train-mlogloss:1.91856	validation-mlogloss:1.90740
[36m(train_xgb_ray_tune pid=2376749)[0m [505]	train-mlogloss:1.91801	validation-mlogloss:1.90687
[36m(train_xgb_ray_tune pid=2376750)[0m [416]	train-mlogloss:1.62576	validation-mlogloss:1.66713
[36m(train_xgb_ray_tune pid=2376737)[0m [527]	train-mlogloss:1.59252	validation-mlogloss:1.62106
[36m(train_xgb_ray_tune pid=2376737)[0m [528]	train-mlogloss:1.59241	validation-mlogloss:1.62099
[36m(train_xgb_ray_tune pid=2376737)[0m [529]	train-mlogloss:1.59229	validation-mlogloss:1.62089
[36m(train_xgb_ray_tune pid=2376736)[0m [494]	train-mlogloss:1.71404	validation-mlogloss:1.73336
[36m(train_xgb_ray_tune pid=2376736)[0m [495]	train-mlogloss:1.71359	validation-mlogloss:1.73296
[36m(train_xgb_ray_tune pid=2376736)[0m [496]	train-mlogloss:1.71313	validation-mlogloss:1.73253
[36m(train_xgb_ray_tune pid=2376722)[0m [443]	train-mlogloss:1.45287	validation-mlogloss:1.53530
[36m(train_xgb_ray_tune pid=2376722)[0m [444]	train-mlogloss:1.45241	validation-mlogloss:1.53488
[36m(train_xgb_ray_tune pid=2376746)[0m [404]	train-mlogloss:1.54644	validation-mlogloss:1.61479
[36m(train_xgb_ray_tune pid=2376746)[0m [405]	train-mlogloss:1.54572	validation-mlogloss:1.61410
[36m(train_xgb_ray_tune pid=2376747)[0m [420]	train-mlogloss:1.42994	validation-mlogloss:1.54187
[36m(train_xgb_ray_tune pid=2376747)[0m [421]	train-mlogloss:1.42921	validation-mlogloss:1.54143
[36m(train_xgb_ray_tune pid=2376728)[0m [489]	train-mlogloss:1.46443	validation-mlogloss:1.52189
[36m(train_xgb_ray_tune pid=2376728)[0m [490]	train-mlogloss:1.46396	validation-mlogloss:1.52167
[36m(train_xgb_ray_tune pid=2376750)[0m [417]	train-mlogloss:1.62520	validation-mlogloss:1.66668
[36m(train_xgb_ray_tune pid=2376750)[0m [418]	train-mlogloss:1.62463	validation-mlogloss:1.66614
[36m(train_xgb_ray_tune pid=2376749)[0m [506]	train-mlogloss:1.91756	validation-mlogloss:1.90643
[36m(train_xgb_ray_tune pid=2376749)[0m [507]	train-mlogloss:1.91704	validation-mlogloss:1.90594
[36m(train_xgb_ray_tune pid=2376749)[0m [508]	train-mlogloss:1.91655	validation-mlogloss:1.90543
[36m(train_xgb_ray_tune pid=2376737)[0m [530]	train-mlogloss:1.59208	validation-mlogloss:1.62075
[36m(train_xgb_ray_tune pid=2376737)[0m [531]	train-mlogloss:1.59202	validation-mlogloss:1.62072
[36m(train_xgb_ray_tune pid=2376737)[0m [532]	train-mlogloss:1.59193	validation-mlogloss:1.62067
[36m(train_xgb_ray_tune pid=2376736)[0m [497]	train-mlogloss:1.71275	validation-mlogloss:1.73217
[36m(train_xgb_ray_tune pid=2376736)[0m [498]	train-mlogloss:1.71230	validation-mlogloss:1.73176
[36m(train_xgb_ray_tune pid=2376736)[0m [499]	train-mlogloss:1.71188	validation-mlogloss:1.73140
[36m(train_xgb_ray_tune pid=2376722)[0m [445]	train-mlogloss:1.45187	validation-mlogloss:1.53448
[36m(train_xgb_ray_tune pid=2376722)[0m [446]	train-mlogloss:1.45135	validation-mlogloss:1.53406
[36m(train_xgb_ray_tune pid=2376722)[0m [447]	train-mlogloss:1.45077	validation-mlogloss:1.53376
[36m(train_xgb_ray_tune pid=2376746)[0m [406]	train-mlogloss:1.54503	validation-mlogloss:1.61343
[36m(train_xgb_ray_tune pid=2376746)[0m [407]	train-mlogloss:1.54427	validation-mlogloss:1.61288
[36m(train_xgb_ray_tune pid=2376747)[0m [422]	train-mlogloss:1.42865	validation-mlogloss:1.54110
[36m(train_xgb_ray_tune pid=2376747)[0m [423]	train-mlogloss:1.42813	validation-mlogloss:1.54082
[36m(train_xgb_ray_tune pid=2376728)[0m [491]	train-mlogloss:1.46347	validation-mlogloss:1.52140
[36m(train_xgb_ray_tune pid=2376728)[0m [492]	train-mlogloss:1.46300	validation-mlogloss:1.52097
[36m(train_xgb_ray_tune pid=2376728)[0m [493]	train-mlogloss:1.46250	validation-mlogloss:1.52062
[36m(train_xgb_ray_tune pid=2376750)[0m [419]	train-mlogloss:1.62403	validation-mlogloss:1.66556
[36m(train_xgb_ray_tune pid=2376750)[0m [420]	train-mlogloss:1.62345	validation-mlogloss:1.66511
[36m(train_xgb_ray_tune pid=2376749)[0m [509]	train-mlogloss:1.91610	validation-mlogloss:1.90496
[36m(train_xgb_ray_tune pid=2376749)[0m [510]	train-mlogloss:1.91569	validation-mlogloss:1.90454
[36m(train_xgb_ray_tune pid=2376749)[0m [511]	train-mlogloss:1.91525	validation-mlogloss:1.90410
[36m(train_xgb_ray_tune pid=2376747)[0m [424]	train-mlogloss:1.42752	validation-mlogloss:1.54050
[36m(train_xgb_ray_tune pid=2376746)[0m [408]	train-mlogloss:1.54358	validation-mlogloss:1.61228
[36m(train_xgb_ray_tune pid=2376737)[0m [533]	train-mlogloss:1.59186	validation-mlogloss:1.62061
[36m(train_xgb_ray_tune pid=2376737)[0m [534]	train-mlogloss:1.59172	validation-mlogloss:1.62045
[36m(train_xgb_ray_tune pid=2376737)[0m [535]	train-mlogloss:1.59153	validation-mlogloss:1.62035
[36m(train_xgb_ray_tune pid=2376736)[0m [500]	train-mlogloss:1.71151	validation-mlogloss:1.73107
[36m(train_xgb_ray_tune pid=2376736)[0m [501]	train-mlogloss:1.71109	validation-mlogloss:1.73073
[36m(train_xgb_ray_tune pid=2376736)[0m [502]	train-mlogloss:1.71073	validation-mlogloss:1.73038
[36m(train_xgb_ray_tune pid=2376722)[0m [448]	train-mlogloss:1.45028	validation-mlogloss:1.53335
[36m(train_xgb_ray_tune pid=2376722)[0m [449]	train-mlogloss:1.44976	validation-mlogloss:1.53295
[36m(train_xgb_ray_tune pid=2376746)[0m [409]	train-mlogloss:1.54288	validation-mlogloss:1.61164
[36m(train_xgb_ray_tune pid=2376746)[0m [410]	train-mlogloss:1.54214	validation-mlogloss:1.61110
[36m(train_xgb_ray_tune pid=2376747)[0m [425]	train-mlogloss:1.42697	validation-mlogloss:1.54005
[36m(train_xgb_ray_tune pid=2376747)[0m [426]	train-mlogloss:1.42636	validation-mlogloss:1.53963
[36m(train_xgb_ray_tune pid=2376728)[0m [494]	train-mlogloss:1.46206	validation-mlogloss:1.52023
[36m(train_xgb_ray_tune pid=2376728)[0m [495]	train-mlogloss:1.46159	validation-mlogloss:1.51982
[36m(train_xgb_ray_tune pid=2376750)[0m [421]	train-mlogloss:1.62280	validation-mlogloss:1.66456
[36m(train_xgb_ray_tune pid=2376750)[0m [422]	train-mlogloss:1.62213	validation-mlogloss:1.66402
[36m(train_xgb_ray_tune pid=2376749)[0m [512]	train-mlogloss:1.91472	validation-mlogloss:1.90361
[36m(train_xgb_ray_tune pid=2376749)[0m [513]	train-mlogloss:1.91428	validation-mlogloss:1.90321
[36m(train_xgb_ray_tune pid=2376749)[0m [514]	train-mlogloss:1.91375	validation-mlogloss:1.90266
[36m(train_xgb_ray_tune pid=2376737)[0m [536]	train-mlogloss:1.59144	validation-mlogloss:1.62027

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:52. Total running time: 1min 5s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s              62.762 │
│ time_total_s                  62.762 │
│ training_iteration                 1 │
│ validation_accuracy          0.26186 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:52. Total running time: 1min 5s
[36m(train_xgb_ray_tune pid=2376737)[0m [537]	train-mlogloss:1.59136	validation-mlogloss:1.62021
[36m(train_xgb_ray_tune pid=2376737)[0m [538]	train-mlogloss:1.59129	validation-mlogloss:1.62017
[36m(train_xgb_ray_tune pid=2376737)[0m [539]	train-mlogloss:1.59115	validation-mlogloss:1.62000
[36m(train_xgb_ray_tune pid=2376736)[0m [503]	train-mlogloss:1.71031	validation-mlogloss:1.72999
[36m(train_xgb_ray_tune pid=2376736)[0m [504]	train-mlogloss:1.70992	validation-mlogloss:1.72967
[36m(train_xgb_ray_tune pid=2376736)[0m [505]	train-mlogloss:1.70950	validation-mlogloss:1.72935
[36m(train_xgb_ray_tune pid=2376722)[0m [450]	train-mlogloss:1.44927	validation-mlogloss:1.53259
[36m(train_xgb_ray_tune pid=2376722)[0m [451]	train-mlogloss:1.44872	validation-mlogloss:1.53206
[36m(train_xgb_ray_tune pid=2376722)[0m [452]	train-mlogloss:1.44824	validation-mlogloss:1.53175
[36m(train_xgb_ray_tune pid=2376746)[0m [411]	train-mlogloss:1.54138	validation-mlogloss:1.61042
[36m(train_xgb_ray_tune pid=2376746)[0m [412]	train-mlogloss:1.54065	validation-mlogloss:1.60973
[36m(train_xgb_ray_tune pid=2376747)[0m [427]	train-mlogloss:1.42572	validation-mlogloss:1.53923
[36m(train_xgb_ray_tune pid=2376747)[0m [428]	train-mlogloss:1.42518	validation-mlogloss:1.53886
[36m(train_xgb_ray_tune pid=2376728)[0m [496]	train-mlogloss:1.46112	validation-mlogloss:1.51949
[36m(train_xgb_ray_tune pid=2376728)[0m [497]	train-mlogloss:1.46061	validation-mlogloss:1.51914
[36m(train_xgb_ray_tune pid=2376750)[0m [423]	train-mlogloss:1.62157	validation-mlogloss:1.66355
[36m(train_xgb_ray_tune pid=2376750)[0m [424]	train-mlogloss:1.62098	validation-mlogloss:1.66305
[36m(train_xgb_ray_tune pid=2376749)[0m [515]	train-mlogloss:1.91335	validation-mlogloss:1.90233
[36m(train_xgb_ray_tune pid=2376749)[0m [516]	train-mlogloss:1.91297	validation-mlogloss:1.90194
[36m(train_xgb_ray_tune pid=2376749)[0m [517]	train-mlogloss:1.91245	validation-mlogloss:1.90140
[36m(train_xgb_ray_tune pid=2376728)[0m [498]	train-mlogloss:1.46016	validation-mlogloss:1.51887
[36m(train_xgb_ray_tune pid=2376750)[0m [425]	train-mlogloss:1.62041	validation-mlogloss:1.66252
[36m(train_xgb_ray_tune pid=2376737)[0m [540]	train-mlogloss:1.59112	validation-mlogloss:1.61998
[36m(train_xgb_ray_tune pid=2376737)[0m [541]	train-mlogloss:1.59106	validation-mlogloss:1.61992
[36m(train_xgb_ray_tune pid=2376737)[0m [542]	train-mlogloss:1.59095	validation-mlogloss:1.61986
[36m(train_xgb_ray_tune pid=2376736)[0m [506]	train-mlogloss:1.70915	validation-mlogloss:1.72904
[36m(train_xgb_ray_tune pid=2376736)[0m [507]	train-mlogloss:1.70875	validation-mlogloss:1.72872
[36m(train_xgb_ray_tune pid=2376736)[0m [508]	train-mlogloss:1.70844	validation-mlogloss:1.72840
[36m(train_xgb_ray_tune pid=2376722)[0m [453]	train-mlogloss:1.44770	validation-mlogloss:1.53141
[36m(train_xgb_ray_tune pid=2376722)[0m [454]	train-mlogloss:1.44721	validation-mlogloss:1.53116
[36m(train_xgb_ray_tune pid=2376746)[0m [413]	train-mlogloss:1.53992	validation-mlogloss:1.60911
[36m(train_xgb_ray_tune pid=2376746)[0m [414]	train-mlogloss:1.53925	validation-mlogloss:1.60850
[36m(train_xgb_ray_tune pid=2376746)[0m [415]	train-mlogloss:1.53857	validation-mlogloss:1.60778
[36m(train_xgb_ray_tune pid=2376747)[0m [429]	train-mlogloss:1.42462	validation-mlogloss:1.53839
[36m(train_xgb_ray_tune pid=2376747)[0m [430]	train-mlogloss:1.42410	validation-mlogloss:1.53795
[36m(train_xgb_ray_tune pid=2376747)[0m [431]	train-mlogloss:1.42358	validation-mlogloss:1.53770
[36m(train_xgb_ray_tune pid=2376728)[0m [499]	train-mlogloss:1.45975	validation-mlogloss:1.51858
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[36m(train_xgb_ray_tune pid=2376746)[0m [474]	train-mlogloss:1.50102	validation-mlogloss:1.57643
[36m(train_xgb_ray_tune pid=2376746)[0m [475]	train-mlogloss:1.50046	validation-mlogloss:1.57597
[36m(train_xgb_ray_tune pid=2376746)[0m [476]	train-mlogloss:1.49990	validation-mlogloss:1.57550
[36m(train_xgb_ray_tune pid=2376747)[0m [486]	train-mlogloss:1.39688	validation-mlogloss:1.52051
[36m(train_xgb_ray_tune pid=2376747)[0m [487]	train-mlogloss:1.39641	validation-mlogloss:1.52022
[36m(train_xgb_ray_tune pid=2376747)[0m [488]	train-mlogloss:1.39593	validation-mlogloss:1.51994
[36m(train_xgb_ray_tune pid=2376728)[0m [559]	train-mlogloss:1.43513	validation-mlogloss:1.50137
[36m(train_xgb_ray_tune pid=2376728)[0m [560]	train-mlogloss:1.43475	validation-mlogloss:1.50117
[36m(train_xgb_ray_tune pid=2376728)[0m [561]	train-mlogloss:1.43439	validation-mlogloss:1.50092
[36m(train_xgb_ray_tune pid=2376750)[0m [485]	train-mlogloss:1.59075	validation-mlogloss:1.63602
[36m(train_xgb_ray_tune pid=2376750)[0m [486]	train-mlogloss:1.59041	validation-mlogloss:1.63570
[36m(train_xgb_ray_tune pid=2376750)[0m [487]	train-mlogloss:1.59008	validation-mlogloss:1.63538
[36m(train_xgb_ray_tune pid=2376749)[0m [593]	train-mlogloss:1.88460	validation-mlogloss:1.87484
[36m(train_xgb_ray_tune pid=2376749)[0m [594]	train-mlogloss:1.88425	validation-mlogloss:1.87451
[36m(train_xgb_ray_tune pid=2376749)[0m [595]	train-mlogloss:1.88393	validation-mlogloss:1.87422
[36m(train_xgb_ray_tune pid=2376749)[0m [596]	train-mlogloss:1.88366	validation-mlogloss:1.87396
[36m(train_xgb_ray_tune pid=2376746)[0m [477]	train-mlogloss:1.49939	validation-mlogloss:1.57515

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:54. Total running time: 1min 8s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             65.4992 │
│ time_total_s                 65.4992 │
│ training_iteration                 1 │
│ validation_accuracy          0.46285 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:54. Total running time: 1min 8s
[36m(train_xgb_ray_tune pid=2376737)[0m [628]	train-mlogloss:1.58513	validation-mlogloss:1.61480
[36m(train_xgb_ray_tune pid=2376737)[0m [629]	train-mlogloss:1.58510	validation-mlogloss:1.61478
[36m(train_xgb_ray_tune pid=2376737)[0m [630]	train-mlogloss:1.58507	validation-mlogloss:1.61477
[36m(train_xgb_ray_tune pid=2376737)[0m [631]	train-mlogloss:1.58503	validation-mlogloss:1.61471
[36m(train_xgb_ray_tune pid=2376736)[0m [580]	train-mlogloss:1.68487	validation-mlogloss:1.70700
[36m(train_xgb_ray_tune pid=2376736)[0m [581]	train-mlogloss:1.68467	validation-mlogloss:1.70680
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[36m(train_xgb_ray_tune pid=2376746)[0m [478]	train-mlogloss:1.49889	validation-mlogloss:1.57473
[36m(train_xgb_ray_tune pid=2376746)[0m [479]	train-mlogloss:1.49838	validation-mlogloss:1.57437
[36m(train_xgb_ray_tune pid=2376747)[0m [489]	train-mlogloss:1.39545	validation-mlogloss:1.51960
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[36m(train_xgb_ray_tune pid=2376728)[0m [562]	train-mlogloss:1.43405	validation-mlogloss:1.50062
[36m(train_xgb_ray_tune pid=2376728)[0m [563]	train-mlogloss:1.43375	validation-mlogloss:1.50044
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[36m(train_xgb_ray_tune pid=2376749)[0m [598]	train-mlogloss:1.88307	validation-mlogloss:1.87342
[36m(train_xgb_ray_tune pid=2376749)[0m [599]	train-mlogloss:1.88277	validation-mlogloss:1.87312
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[36m(train_xgb_ray_tune pid=2376746)[0m [480]	train-mlogloss:1.49789	validation-mlogloss:1.57402
[36m(train_xgb_ray_tune pid=2376749)[0m [600]	train-mlogloss:1.88252	validation-mlogloss:1.87291
[36m(train_xgb_ray_tune pid=2376737)[0m [632]	train-mlogloss:1.58500	validation-mlogloss:1.61468
[36m(train_xgb_ray_tune pid=2376737)[0m [633]	train-mlogloss:1.58495	validation-mlogloss:1.61463
[36m(train_xgb_ray_tune pid=2376737)[0m [634]	train-mlogloss:1.58492	validation-mlogloss:1.61460
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[36m(train_xgb_ray_tune pid=2376736)[0m [586]	train-mlogloss:1.68348	validation-mlogloss:1.70579
[36m(train_xgb_ray_tune pid=2376746)[0m [481]	train-mlogloss:1.49734	validation-mlogloss:1.57353
[36m(train_xgb_ray_tune pid=2376746)[0m [482]	train-mlogloss:1.49679	validation-mlogloss:1.57307
[36m(train_xgb_ray_tune pid=2376747)[0m [492]	train-mlogloss:1.39422	validation-mlogloss:1.51882
[36m(train_xgb_ray_tune pid=2376747)[0m [493]	train-mlogloss:1.39375	validation-mlogloss:1.51861
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[36m(train_xgb_ray_tune pid=2376728)[0m [565]	train-mlogloss:1.43307	validation-mlogloss:1.49989
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[36m(train_xgb_ray_tune pid=2376728)[0m [568]	train-mlogloss:1.43199	validation-mlogloss:1.49912
[36m(train_xgb_ray_tune pid=2376750)[0m [491]	train-mlogloss:1.58846	validation-mlogloss:1.63398
[36m(train_xgb_ray_tune pid=2376750)[0m [492]	train-mlogloss:1.58809	validation-mlogloss:1.63368
[36m(train_xgb_ray_tune pid=2376750)[0m [493]	train-mlogloss:1.58771	validation-mlogloss:1.63329
[36m(train_xgb_ray_tune pid=2376749)[0m [601]	train-mlogloss:1.88222	validation-mlogloss:1.87266
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[36m(train_xgb_ray_tune pid=2376736)[0m [590]	train-mlogloss:1.68246	validation-mlogloss:1.70478
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[36m(train_xgb_ray_tune pid=2376750)[0m [494]	train-mlogloss:1.58737	validation-mlogloss:1.63296
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[36m(train_xgb_ray_tune pid=2376750)[0m [497]	train-mlogloss:1.58614	validation-mlogloss:1.63188
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[36m(train_xgb_ray_tune pid=2376749)[0m [619]	train-mlogloss:1.87709	validation-mlogloss:1.86770
[36m(train_xgb_ray_tune pid=2376737)[0m [654]	train-mlogloss:1.58408	validation-mlogloss:1.61400
[36m(train_xgb_ray_tune pid=2376737)[0m [655]	train-mlogloss:1.58406	validation-mlogloss:1.61396
[36m(train_xgb_ray_tune pid=2376737)[0m [656]	train-mlogloss:1.58403	validation-mlogloss:1.61396
[36m(train_xgb_ray_tune pid=2376737)[0m [657]	train-mlogloss:1.58401	validation-mlogloss:1.61393
[36m(train_xgb_ray_tune pid=2376736)[0m [602]	train-mlogloss:1.67942	validation-mlogloss:1.70204
[36m(train_xgb_ray_tune pid=2376736)[0m [603]	train-mlogloss:1.67916	validation-mlogloss:1.70185
[36m(train_xgb_ray_tune pid=2376736)[0m [604]	train-mlogloss:1.67890	validation-mlogloss:1.70162
[36m(train_xgb_ray_tune pid=2376736)[0m [605]	train-mlogloss:1.67870	validation-mlogloss:1.70144
[36m(train_xgb_ray_tune pid=2376746)[0m [496]	train-mlogloss:1.48961	validation-mlogloss:1.56665
[36m(train_xgb_ray_tune pid=2376746)[0m [497]	train-mlogloss:1.48915	validation-mlogloss:1.56617
[36m(train_xgb_ray_tune pid=2376746)[0m [498]	train-mlogloss:1.48865	validation-mlogloss:1.56579
[36m(train_xgb_ray_tune pid=2376728)[0m [582]	train-mlogloss:1.42712	validation-mlogloss:1.49613
[36m(train_xgb_ray_tune pid=2376728)[0m [583]	train-mlogloss:1.42679	validation-mlogloss:1.49594
[36m(train_xgb_ray_tune pid=2376728)[0m [584]	train-mlogloss:1.42642	validation-mlogloss:1.49570
[36m(train_xgb_ray_tune pid=2376750)[0m [507]	train-mlogloss:1.58246	validation-mlogloss:1.62853
[36m(train_xgb_ray_tune pid=2376750)[0m [508]	train-mlogloss:1.58218	validation-mlogloss:1.62826
[36m(train_xgb_ray_tune pid=2376750)[0m [509]	train-mlogloss:1.58190	validation-mlogloss:1.62798
[36m(train_xgb_ray_tune pid=2376750)[0m [510]	train-mlogloss:1.58150	validation-mlogloss:1.62769
[36m(train_xgb_ray_tune pid=2376749)[0m [620]	train-mlogloss:1.87679	validation-mlogloss:1.86738
[36m(train_xgb_ray_tune pid=2376749)[0m [621]	train-mlogloss:1.87650	validation-mlogloss:1.86713
[36m(train_xgb_ray_tune pid=2376749)[0m [622]	train-mlogloss:1.87620	validation-mlogloss:1.86687
[36m(train_xgb_ray_tune pid=2376749)[0m [623]	train-mlogloss:1.87592	validation-mlogloss:1.86659
[36m(train_xgb_ray_tune pid=2376737)[0m [658]	train-mlogloss:1.58398	validation-mlogloss:1.61392
[36m(train_xgb_ray_tune pid=2376736)[0m [606]	train-mlogloss:1.67843	validation-mlogloss:1.70123
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[36m(train_xgb_ray_tune pid=2376737)[0m [660]	train-mlogloss:1.58398	validation-mlogloss:1.61392
[36m(train_xgb_ray_tune pid=2376737)[0m [661]	train-mlogloss:1.58395	validation-mlogloss:1.61390
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[36m(train_xgb_ray_tune pid=2376736)[0m [608]	train-mlogloss:1.67803	validation-mlogloss:1.70088
[36m(train_xgb_ray_tune pid=2376736)[0m [609]	train-mlogloss:1.67781	validation-mlogloss:1.70068
[36m(train_xgb_ray_tune pid=2376736)[0m [610]	train-mlogloss:1.67759	validation-mlogloss:1.70051
[36m(train_xgb_ray_tune pid=2376746)[0m [499]	train-mlogloss:1.48814	validation-mlogloss:1.56539
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[36m(train_xgb_ray_tune pid=2376746)[0m [501]	train-mlogloss:1.48719	validation-mlogloss:1.56456
[36m(train_xgb_ray_tune pid=2376746)[0m [502]	train-mlogloss:1.48667	validation-mlogloss:1.56411
[36m(train_xgb_ray_tune pid=2376728)[0m [585]	train-mlogloss:1.42605	validation-mlogloss:1.49541
[36m(train_xgb_ray_tune pid=2376728)[0m [586]	train-mlogloss:1.42566	validation-mlogloss:1.49517
[36m(train_xgb_ray_tune pid=2376728)[0m [587]	train-mlogloss:1.42534	validation-mlogloss:1.49499
[36m(train_xgb_ray_tune pid=2376750)[0m [511]	train-mlogloss:1.58115	validation-mlogloss:1.62739
[36m(train_xgb_ray_tune pid=2376750)[0m [512]	train-mlogloss:1.58075	validation-mlogloss:1.62706
[36m(train_xgb_ray_tune pid=2376750)[0m [513]	train-mlogloss:1.58044	validation-mlogloss:1.62675
[36m(train_xgb_ray_tune pid=2376749)[0m [624]	train-mlogloss:1.87565	validation-mlogloss:1.86633
[36m(train_xgb_ray_tune pid=2376749)[0m [625]	train-mlogloss:1.87535	validation-mlogloss:1.86605
[36m(train_xgb_ray_tune pid=2376749)[0m [626]	train-mlogloss:1.87503	validation-mlogloss:1.86574
[36m(train_xgb_ray_tune pid=2376749)[0m [627]	train-mlogloss:1.87467	validation-mlogloss:1.86537
[36m(train_xgb_ray_tune pid=2376737)[0m [663]	train-mlogloss:1.58387	validation-mlogloss:1.61385
[36m(train_xgb_ray_tune pid=2376737)[0m [664]	train-mlogloss:1.58382	validation-mlogloss:1.61381
[36m(train_xgb_ray_tune pid=2376736)[0m [611]	train-mlogloss:1.67738	validation-mlogloss:1.70030
[36m(train_xgb_ray_tune pid=2376736)[0m [612]	train-mlogloss:1.67718	validation-mlogloss:1.70012
[36m(train_xgb_ray_tune pid=2376736)[0m [613]	train-mlogloss:1.67692	validation-mlogloss:1.69984
[36m(train_xgb_ray_tune pid=2376736)[0m [614]	train-mlogloss:1.67673	validation-mlogloss:1.69966
[36m(train_xgb_ray_tune pid=2376746)[0m [503]	train-mlogloss:1.48618	validation-mlogloss:1.56364
[36m(train_xgb_ray_tune pid=2376746)[0m [504]	train-mlogloss:1.48571	validation-mlogloss:1.56328
[36m(train_xgb_ray_tune pid=2376746)[0m [505]	train-mlogloss:1.48522	validation-mlogloss:1.56292
[36m(train_xgb_ray_tune pid=2376728)[0m [588]	train-mlogloss:1.42496	validation-mlogloss:1.49481
[36m(train_xgb_ray_tune pid=2376728)[0m [589]	train-mlogloss:1.42462	validation-mlogloss:1.49451
[36m(train_xgb_ray_tune pid=2376728)[0m [590]	train-mlogloss:1.42429	validation-mlogloss:1.49417
[36m(train_xgb_ray_tune pid=2376728)[0m [591]	train-mlogloss:1.42398	validation-mlogloss:1.49404
[36m(train_xgb_ray_tune pid=2376750)[0m [514]	train-mlogloss:1.58005	validation-mlogloss:1.62639
[36m(train_xgb_ray_tune pid=2376750)[0m [515]	train-mlogloss:1.57976	validation-mlogloss:1.62611
[36m(train_xgb_ray_tune pid=2376750)[0m [516]	train-mlogloss:1.57948	validation-mlogloss:1.62587
[36m(train_xgb_ray_tune pid=2376750)[0m [517]	train-mlogloss:1.57919	validation-mlogloss:1.62566
[36m(train_xgb_ray_tune pid=2376749)[0m [628]	train-mlogloss:1.87435	validation-mlogloss:1.86505
[36m(train_xgb_ray_tune pid=2376749)[0m [629]	train-mlogloss:1.87414	validation-mlogloss:1.86487
[36m(train_xgb_ray_tune pid=2376749)[0m [630]	train-mlogloss:1.87388	validation-mlogloss:1.86462
[36m(train_xgb_ray_tune pid=2376749)[0m [631]	train-mlogloss:1.87366	validation-mlogloss:1.86439
[36m(train_xgb_ray_tune pid=2376736)[0m [615]	train-mlogloss:1.67651	validation-mlogloss:1.69948
[36m(train_xgb_ray_tune pid=2376736)[0m [616]	train-mlogloss:1.67635	validation-mlogloss:1.69932
[36m(train_xgb_ray_tune pid=2376736)[0m [617]	train-mlogloss:1.67610	validation-mlogloss:1.69913
[36m(train_xgb_ray_tune pid=2376736)[0m [618]	train-mlogloss:1.67587	validation-mlogloss:1.69893
[36m(train_xgb_ray_tune pid=2376746)[0m [506]	train-mlogloss:1.48476	validation-mlogloss:1.56251
[36m(train_xgb_ray_tune pid=2376746)[0m [507]	train-mlogloss:1.48428	validation-mlogloss:1.56215
[36m(train_xgb_ray_tune pid=2376746)[0m [508]	train-mlogloss:1.48377	validation-mlogloss:1.56167
[36m(train_xgb_ray_tune pid=2376746)[0m [509]	train-mlogloss:1.48325	validation-mlogloss:1.56126
[36m(train_xgb_ray_tune pid=2376728)[0m [592]	train-mlogloss:1.42361	validation-mlogloss:1.49383
[36m(train_xgb_ray_tune pid=2376728)[0m [593]	train-mlogloss:1.42329	validation-mlogloss:1.49366
[36m(train_xgb_ray_tune pid=2376728)[0m [594]	train-mlogloss:1.42293	validation-mlogloss:1.49339
[36m(train_xgb_ray_tune pid=2376750)[0m [518]	train-mlogloss:1.57891	validation-mlogloss:1.62538
[36m(train_xgb_ray_tune pid=2376750)[0m [519]	train-mlogloss:1.57858	validation-mlogloss:1.62512
[36m(train_xgb_ray_tune pid=2376750)[0m [520]	train-mlogloss:1.57824	validation-mlogloss:1.62482
[36m(train_xgb_ray_tune pid=2376749)[0m [632]	train-mlogloss:1.87339	validation-mlogloss:1.86414
[36m(train_xgb_ray_tune pid=2376749)[0m [633]	train-mlogloss:1.87315	validation-mlogloss:1.86392
[36m(train_xgb_ray_tune pid=2376749)[0m [634]	train-mlogloss:1.87286	validation-mlogloss:1.86367
[36m(train_xgb_ray_tune pid=2376749)[0m [635]	train-mlogloss:1.87258	validation-mlogloss:1.86342
[36m(train_xgb_ray_tune pid=2376736)[0m [619]	train-mlogloss:1.67570	validation-mlogloss:1.69877
[36m(train_xgb_ray_tune pid=2376736)[0m [620]	train-mlogloss:1.67550	validation-mlogloss:1.69860
[36m(train_xgb_ray_tune pid=2376736)[0m [621]	train-mlogloss:1.67530	validation-mlogloss:1.69844
[36m(train_xgb_ray_tune pid=2376736)[0m [622]	train-mlogloss:1.67503	validation-mlogloss:1.69825
[36m(train_xgb_ray_tune pid=2376746)[0m [510]	train-mlogloss:1.48282	validation-mlogloss:1.56084
[36m(train_xgb_ray_tune pid=2376746)[0m [511]	train-mlogloss:1.48241	validation-mlogloss:1.56042
[36m(train_xgb_ray_tune pid=2376746)[0m [512]	train-mlogloss:1.48189	validation-mlogloss:1.56005
[36m(train_xgb_ray_tune pid=2376728)[0m [595]	train-mlogloss:1.42259	validation-mlogloss:1.49326
[36m(train_xgb_ray_tune pid=2376728)[0m [596]	train-mlogloss:1.42226	validation-mlogloss:1.49308
[36m(train_xgb_ray_tune pid=2376728)[0m [597]	train-mlogloss:1.42192	validation-mlogloss:1.49283
[36m(train_xgb_ray_tune pid=2376750)[0m [521]	train-mlogloss:1.57790	validation-mlogloss:1.62456
[36m(train_xgb_ray_tune pid=2376750)[0m [522]	train-mlogloss:1.57753	validation-mlogloss:1.62429
[36m(train_xgb_ray_tune pid=2376750)[0m [523]	train-mlogloss:1.57732	validation-mlogloss:1.62408
[36m(train_xgb_ray_tune pid=2376750)[0m [524]	train-mlogloss:1.57709	validation-mlogloss:1.62387
[36m(train_xgb_ray_tune pid=2376749)[0m [636]	train-mlogloss:1.87234	validation-mlogloss:1.86319
[36m(train_xgb_ray_tune pid=2376749)[0m [637]	train-mlogloss:1.87207	validation-mlogloss:1.86288
[36m(train_xgb_ray_tune pid=2376749)[0m [638]	train-mlogloss:1.87178	validation-mlogloss:1.86256
[36m(train_xgb_ray_tune pid=2376749)[0m [639]	train-mlogloss:1.87148	validation-mlogloss:1.86228
[36m(train_xgb_ray_tune pid=2376728)[0m [598]	train-mlogloss:1.42158	validation-mlogloss:1.49268
[36m(train_xgb_ray_tune pid=2376736)[0m [623]	train-mlogloss:1.67483	validation-mlogloss:1.69812
[36m(train_xgb_ray_tune pid=2376736)[0m [624]	train-mlogloss:1.67467	validation-mlogloss:1.69794
[36m(train_xgb_ray_tune pid=2376736)[0m [625]	train-mlogloss:1.67442	validation-mlogloss:1.69771
[36m(train_xgb_ray_tune pid=2376736)[0m [626]	train-mlogloss:1.67423	validation-mlogloss:1.69754
[36m(train_xgb_ray_tune pid=2376746)[0m [513]	train-mlogloss:1.48146	validation-mlogloss:1.55965
[36m(train_xgb_ray_tune pid=2376746)[0m [514]	train-mlogloss:1.48102	validation-mlogloss:1.55928
[36m(train_xgb_ray_tune pid=2376746)[0m [515]	train-mlogloss:1.48051	validation-mlogloss:1.55889
[36m(train_xgb_ray_tune pid=2376746)[0m [516]	train-mlogloss:1.47998	validation-mlogloss:1.55851
[36m(train_xgb_ray_tune pid=2376728)[0m [599]	train-mlogloss:1.42120	validation-mlogloss:1.49247
[36m(train_xgb_ray_tune pid=2376728)[0m [600]	train-mlogloss:1.42087	validation-mlogloss:1.49232
[36m(train_xgb_ray_tune pid=2376728)[0m [601]	train-mlogloss:1.42058	validation-mlogloss:1.49215
[36m(train_xgb_ray_tune pid=2376750)[0m [525]	train-mlogloss:1.57680	validation-mlogloss:1.62366
[36m(train_xgb_ray_tune pid=2376750)[0m [526]	train-mlogloss:1.57652	validation-mlogloss:1.62340
[36m(train_xgb_ray_tune pid=2376750)[0m [527]	train-mlogloss:1.57624	validation-mlogloss:1.62315
[36m(train_xgb_ray_tune pid=2376750)[0m [528]	train-mlogloss:1.57596	validation-mlogloss:1.62290
[36m(train_xgb_ray_tune pid=2376749)[0m [640]	train-mlogloss:1.87118	validation-mlogloss:1.86197
[36m(train_xgb_ray_tune pid=2376749)[0m [641]	train-mlogloss:1.87096	validation-mlogloss:1.86176
[36m(train_xgb_ray_tune pid=2376749)[0m [642]	train-mlogloss:1.87068	validation-mlogloss:1.86154
[36m(train_xgb_ray_tune pid=2376749)[0m [643]	train-mlogloss:1.87047	validation-mlogloss:1.86133

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:56. Total running time: 1min 9s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             66.4107 │
│ time_total_s                 66.4107 │
│ training_iteration                 1 │
│ validation_accuracy          0.47174 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:56. Total running time: 1min 9s
[36m(train_xgb_ray_tune pid=2376736)[0m [627]	train-mlogloss:1.67404	validation-mlogloss:1.69737
[36m(train_xgb_ray_tune pid=2376736)[0m [628]	train-mlogloss:1.67375	validation-mlogloss:1.69716
[36m(train_xgb_ray_tune pid=2376736)[0m [629]	train-mlogloss:1.67358	validation-mlogloss:1.69702
[36m(train_xgb_ray_tune pid=2376746)[0m [517]	train-mlogloss:1.47950	validation-mlogloss:1.55817
[36m(train_xgb_ray_tune pid=2376746)[0m [518]	train-mlogloss:1.47902	validation-mlogloss:1.55775
[36m(train_xgb_ray_tune pid=2376746)[0m [519]	train-mlogloss:1.47859	validation-mlogloss:1.55733
[36m(train_xgb_ray_tune pid=2376728)[0m [602]	train-mlogloss:1.42027	validation-mlogloss:1.49200
[36m(train_xgb_ray_tune pid=2376728)[0m [603]	train-mlogloss:1.41996	validation-mlogloss:1.49191
[36m(train_xgb_ray_tune pid=2376728)[0m [604]	train-mlogloss:1.41963	validation-mlogloss:1.49161
[36m(train_xgb_ray_tune pid=2376750)[0m [529]	train-mlogloss:1.57571	validation-mlogloss:1.62268
[36m(train_xgb_ray_tune pid=2376750)[0m [530]	train-mlogloss:1.57542	validation-mlogloss:1.62242
[36m(train_xgb_ray_tune pid=2376750)[0m [531]	train-mlogloss:1.57512	validation-mlogloss:1.62214
[36m(train_xgb_ray_tune pid=2376750)[0m [532]	train-mlogloss:1.57485	validation-mlogloss:1.62185
[36m(train_xgb_ray_tune pid=2376749)[0m [644]	train-mlogloss:1.87019	validation-mlogloss:1.86106
[36m(train_xgb_ray_tune pid=2376749)[0m [645]	train-mlogloss:1.86985	validation-mlogloss:1.86072
[36m(train_xgb_ray_tune pid=2376749)[0m [646]	train-mlogloss:1.86960	validation-mlogloss:1.86050
[36m(train_xgb_ray_tune pid=2376749)[0m [647]	train-mlogloss:1.86936	validation-mlogloss:1.86031
[36m(train_xgb_ray_tune pid=2376736)[0m [630]	train-mlogloss:1.67332	validation-mlogloss:1.69678
[36m(train_xgb_ray_tune pid=2376749)[0m [648]	train-mlogloss:1.86914	validation-mlogloss:1.86013
[36m(train_xgb_ray_tune pid=2376736)[0m [631]	train-mlogloss:1.67310	validation-mlogloss:1.69659
[36m(train_xgb_ray_tune pid=2376736)[0m [632]	train-mlogloss:1.67281	validation-mlogloss:1.69633
[36m(train_xgb_ray_tune pid=2376736)[0m [633]	train-mlogloss:1.67263	validation-mlogloss:1.69618
[36m(train_xgb_ray_tune pid=2376736)[0m [634]	train-mlogloss:1.67246	validation-mlogloss:1.69600
[36m(train_xgb_ray_tune pid=2376746)[0m [520]	train-mlogloss:1.47818	validation-mlogloss:1.55703
[36m(train_xgb_ray_tune pid=2376746)[0m [521]	train-mlogloss:1.47771	validation-mlogloss:1.55677
[36m(train_xgb_ray_tune pid=2376746)[0m [522]	train-mlogloss:1.47721	validation-mlogloss:1.55639
[36m(train_xgb_ray_tune pid=2376746)[0m [523]	train-mlogloss:1.47678	validation-mlogloss:1.55603
[36m(train_xgb_ray_tune pid=2376728)[0m [605]	train-mlogloss:1.41936	validation-mlogloss:1.49143
[36m(train_xgb_ray_tune pid=2376728)[0m [606]	train-mlogloss:1.41907	validation-mlogloss:1.49129
[36m(train_xgb_ray_tune pid=2376728)[0m [607]	train-mlogloss:1.41872	validation-mlogloss:1.49106
[36m(train_xgb_ray_tune pid=2376728)[0m [608]	train-mlogloss:1.41841	validation-mlogloss:1.49088
[36m(train_xgb_ray_tune pid=2376750)[0m [533]	train-mlogloss:1.57458	validation-mlogloss:1.62162
[36m(train_xgb_ray_tune pid=2376750)[0m [534]	train-mlogloss:1.57435	validation-mlogloss:1.62141
[36m(train_xgb_ray_tune pid=2376750)[0m [535]	train-mlogloss:1.57406	validation-mlogloss:1.62111
[36m(train_xgb_ray_tune pid=2376750)[0m [536]	train-mlogloss:1.57375	validation-mlogloss:1.62083
[36m(train_xgb_ray_tune pid=2376749)[0m [649]	train-mlogloss:1.86894	validation-mlogloss:1.85994
[36m(train_xgb_ray_tune pid=2376749)[0m [650]	train-mlogloss:1.86871	validation-mlogloss:1.85977
[36m(train_xgb_ray_tune pid=2376749)[0m [651]	train-mlogloss:1.86849	validation-mlogloss:1.85959
[36m(train_xgb_ray_tune pid=2376749)[0m [652]	train-mlogloss:1.86829	validation-mlogloss:1.85944

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:56. Total running time: 1min 9s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             66.8366 │
│ time_total_s                 66.8366 │
│ training_iteration                 1 │
│ validation_accuracy          0.43755 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:56. Total running time: 1min 9s
[36m(train_xgb_ray_tune pid=2376736)[0m [635]	train-mlogloss:1.67231	validation-mlogloss:1.69588
[36m(train_xgb_ray_tune pid=2376736)[0m [636]	train-mlogloss:1.67208	validation-mlogloss:1.69566
[36m(train_xgb_ray_tune pid=2376736)[0m [637]	train-mlogloss:1.67187	validation-mlogloss:1.69544
[36m(train_xgb_ray_tune pid=2376736)[0m [638]	train-mlogloss:1.67167	validation-mlogloss:1.69529
[36m(train_xgb_ray_tune pid=2376746)[0m [524]	train-mlogloss:1.47634	validation-mlogloss:1.55558
[36m(train_xgb_ray_tune pid=2376746)[0m [525]	train-mlogloss:1.47589	validation-mlogloss:1.55518
[36m(train_xgb_ray_tune pid=2376746)[0m [526]	train-mlogloss:1.47536	validation-mlogloss:1.55465
[36m(train_xgb_ray_tune pid=2376728)[0m [609]	train-mlogloss:1.41807	validation-mlogloss:1.49072
[36m(train_xgb_ray_tune pid=2376728)[0m [610]	train-mlogloss:1.41777	validation-mlogloss:1.49052
[36m(train_xgb_ray_tune pid=2376728)[0m [611]	train-mlogloss:1.41740	validation-mlogloss:1.49024
[36m(train_xgb_ray_tune pid=2376750)[0m [537]	train-mlogloss:1.57346	validation-mlogloss:1.62056
[36m(train_xgb_ray_tune pid=2376750)[0m [538]	train-mlogloss:1.57316	validation-mlogloss:1.62033
[36m(train_xgb_ray_tune pid=2376750)[0m [539]	train-mlogloss:1.57291	validation-mlogloss:1.62010
[36m(train_xgb_ray_tune pid=2376749)[0m [653]	train-mlogloss:1.86807	validation-mlogloss:1.85926
[36m(train_xgb_ray_tune pid=2376749)[0m [654]	train-mlogloss:1.86785	validation-mlogloss:1.85905
[36m(train_xgb_ray_tune pid=2376749)[0m [655]	train-mlogloss:1.86761	validation-mlogloss:1.85881
[36m(train_xgb_ray_tune pid=2376749)[0m [656]	train-mlogloss:1.86741	validation-mlogloss:1.85864
[36m(train_xgb_ray_tune pid=2376736)[0m [639]	train-mlogloss:1.67146	validation-mlogloss:1.69513
[36m(train_xgb_ray_tune pid=2376736)[0m [640]	train-mlogloss:1.67129	validation-mlogloss:1.69497
[36m(train_xgb_ray_tune pid=2376736)[0m [641]	train-mlogloss:1.67110	validation-mlogloss:1.69481
[36m(train_xgb_ray_tune pid=2376736)[0m [642]	train-mlogloss:1.67090	validation-mlogloss:1.69467
[36m(train_xgb_ray_tune pid=2376746)[0m [527]	train-mlogloss:1.47492	validation-mlogloss:1.55429
[36m(train_xgb_ray_tune pid=2376746)[0m [528]	train-mlogloss:1.47441	validation-mlogloss:1.55395
[36m(train_xgb_ray_tune pid=2376746)[0m [529]	train-mlogloss:1.47400	validation-mlogloss:1.55365
[36m(train_xgb_ray_tune pid=2376746)[0m [530]	train-mlogloss:1.47362	validation-mlogloss:1.55334
[36m(train_xgb_ray_tune pid=2376728)[0m [612]	train-mlogloss:1.41709	validation-mlogloss:1.49001
[36m(train_xgb_ray_tune pid=2376728)[0m [613]	train-mlogloss:1.41674	validation-mlogloss:1.48984
[36m(train_xgb_ray_tune pid=2376728)[0m [614]	train-mlogloss:1.41639	validation-mlogloss:1.48953
[36m(train_xgb_ray_tune pid=2376728)[0m [615]	train-mlogloss:1.41605	validation-mlogloss:1.48943
[36m(train_xgb_ray_tune pid=2376750)[0m [540]	train-mlogloss:1.57263	validation-mlogloss:1.61987
[36m(train_xgb_ray_tune pid=2376750)[0m [541]	train-mlogloss:1.57234	validation-mlogloss:1.61960
[36m(train_xgb_ray_tune pid=2376750)[0m [542]	train-mlogloss:1.57205	validation-mlogloss:1.61934
[36m(train_xgb_ray_tune pid=2376750)[0m [543]	train-mlogloss:1.57180	validation-mlogloss:1.61911
[36m(train_xgb_ray_tune pid=2376749)[0m [657]	train-mlogloss:1.86718	validation-mlogloss:1.85842
[36m(train_xgb_ray_tune pid=2376749)[0m [658]	train-mlogloss:1.86700	validation-mlogloss:1.85823
[36m(train_xgb_ray_tune pid=2376749)[0m [659]	train-mlogloss:1.86683	validation-mlogloss:1.85804
[36m(train_xgb_ray_tune pid=2376749)[0m [660]	train-mlogloss:1.86654	validation-mlogloss:1.85776
[36m(train_xgb_ray_tune pid=2376749)[0m [661]	train-mlogloss:1.86635	validation-mlogloss:1.85754
[36m(train_xgb_ray_tune pid=2376736)[0m [643]	train-mlogloss:1.67073	validation-mlogloss:1.69455
[36m(train_xgb_ray_tune pid=2376750)[0m [544]	train-mlogloss:1.57153	validation-mlogloss:1.61890
[36m(train_xgb_ray_tune pid=2376736)[0m [644]	train-mlogloss:1.67055	validation-mlogloss:1.69438
[36m(train_xgb_ray_tune pid=2376736)[0m [645]	train-mlogloss:1.67034	validation-mlogloss:1.69419
[36m(train_xgb_ray_tune pid=2376736)[0m [646]	train-mlogloss:1.67014	validation-mlogloss:1.69399
[36m(train_xgb_ray_tune pid=2376736)[0m [647]	train-mlogloss:1.66995	validation-mlogloss:1.69383
[36m(train_xgb_ray_tune pid=2376746)[0m [531]	train-mlogloss:1.47319	validation-mlogloss:1.55300
[36m(train_xgb_ray_tune pid=2376746)[0m [532]	train-mlogloss:1.47274	validation-mlogloss:1.55261
[36m(train_xgb_ray_tune pid=2376746)[0m [533]	train-mlogloss:1.47229	validation-mlogloss:1.55228
[36m(train_xgb_ray_tune pid=2376746)[0m [534]	train-mlogloss:1.47189	validation-mlogloss:1.55195
[36m(train_xgb_ray_tune pid=2376728)[0m [616]	train-mlogloss:1.41570	validation-mlogloss:1.48928
[36m(train_xgb_ray_tune pid=2376728)[0m [617]	train-mlogloss:1.41536	validation-mlogloss:1.48913
[36m(train_xgb_ray_tune pid=2376728)[0m [618]	train-mlogloss:1.41512	validation-mlogloss:1.48896
[36m(train_xgb_ray_tune pid=2376728)[0m [619]	train-mlogloss:1.41481	validation-mlogloss:1.48875
[36m(train_xgb_ray_tune pid=2376750)[0m [545]	train-mlogloss:1.57131	validation-mlogloss:1.61872
[36m(train_xgb_ray_tune pid=2376750)[0m [546]	train-mlogloss:1.57102	validation-mlogloss:1.61846
[36m(train_xgb_ray_tune pid=2376750)[0m [547]	train-mlogloss:1.57076	validation-mlogloss:1.61825
[36m(train_xgb_ray_tune pid=2376750)[0m [548]	train-mlogloss:1.57056	validation-mlogloss:1.61805
[36m(train_xgb_ray_tune pid=2376749)[0m [662]	train-mlogloss:1.86615	validation-mlogloss:1.85736
[36m(train_xgb_ray_tune pid=2376749)[0m [663]	train-mlogloss:1.86594	validation-mlogloss:1.85716
[36m(train_xgb_ray_tune pid=2376749)[0m [664]	train-mlogloss:1.86575	validation-mlogloss:1.85696
[36m(train_xgb_ray_tune pid=2376749)[0m [665]	train-mlogloss:1.86543	validation-mlogloss:1.85661
[36m(train_xgb_ray_tune pid=2376749)[0m [666]	train-mlogloss:1.86526	validation-mlogloss:1.85647
[36m(train_xgb_ray_tune pid=2376736)[0m [648]	train-mlogloss:1.66976	validation-mlogloss:1.69365
[36m(train_xgb_ray_tune pid=2376736)[0m [649]	train-mlogloss:1.66957	validation-mlogloss:1.69350
[36m(train_xgb_ray_tune pid=2376736)[0m [650]	train-mlogloss:1.66940	validation-mlogloss:1.69339
[36m(train_xgb_ray_tune pid=2376736)[0m [651]	train-mlogloss:1.66921	validation-mlogloss:1.69321
[36m(train_xgb_ray_tune pid=2376746)[0m [535]	train-mlogloss:1.47143	validation-mlogloss:1.55158
[36m(train_xgb_ray_tune pid=2376746)[0m [536]	train-mlogloss:1.47102	validation-mlogloss:1.55122
[36m(train_xgb_ray_tune pid=2376746)[0m [537]	train-mlogloss:1.47053	validation-mlogloss:1.55084
[36m(train_xgb_ray_tune pid=2376746)[0m [538]	train-mlogloss:1.47006	validation-mlogloss:1.55053
[36m(train_xgb_ray_tune pid=2376728)[0m [620]	train-mlogloss:1.41451	validation-mlogloss:1.48870
[36m(train_xgb_ray_tune pid=2376728)[0m [621]	train-mlogloss:1.41424	validation-mlogloss:1.48855
[36m(train_xgb_ray_tune pid=2376728)[0m [622]	train-mlogloss:1.41396	validation-mlogloss:1.48841
[36m(train_xgb_ray_tune pid=2376728)[0m [623]	train-mlogloss:1.41365	validation-mlogloss:1.48813
[36m(train_xgb_ray_tune pid=2376750)[0m [549]	train-mlogloss:1.57027	validation-mlogloss:1.61779
[36m(train_xgb_ray_tune pid=2376750)[0m [550]	train-mlogloss:1.57000	validation-mlogloss:1.61758
[36m(train_xgb_ray_tune pid=2376750)[0m [551]	train-mlogloss:1.56970	validation-mlogloss:1.61732
[36m(train_xgb_ray_tune pid=2376750)[0m [552]	train-mlogloss:1.56942	validation-mlogloss:1.61711
[36m(train_xgb_ray_tune pid=2376749)[0m [667]	train-mlogloss:1.86497	validation-mlogloss:1.85620
[36m(train_xgb_ray_tune pid=2376749)[0m [668]	train-mlogloss:1.86473	validation-mlogloss:1.85596
[36m(train_xgb_ray_tune pid=2376749)[0m [669]	train-mlogloss:1.86442	validation-mlogloss:1.85570
[36m(train_xgb_ray_tune pid=2376749)[0m [670]	train-mlogloss:1.86421	validation-mlogloss:1.85552
[36m(train_xgb_ray_tune pid=2376736)[0m [652]	train-mlogloss:1.66900	validation-mlogloss:1.69302
[36m(train_xgb_ray_tune pid=2376749)[0m [671]	train-mlogloss:1.86395	validation-mlogloss:1.85527
[36m(train_xgb_ray_tune pid=2376736)[0m [653]	train-mlogloss:1.66882	validation-mlogloss:1.69288
[36m(train_xgb_ray_tune pid=2376736)[0m [654]	train-mlogloss:1.66866	validation-mlogloss:1.69273
[36m(train_xgb_ray_tune pid=2376736)[0m [655]	train-mlogloss:1.66849	validation-mlogloss:1.69260
[36m(train_xgb_ray_tune pid=2376736)[0m [656]	train-mlogloss:1.66833	validation-mlogloss:1.69248
[36m(train_xgb_ray_tune pid=2376746)[0m [539]	train-mlogloss:1.46960	validation-mlogloss:1.55013
[36m(train_xgb_ray_tune pid=2376746)[0m [540]	train-mlogloss:1.46919	validation-mlogloss:1.54981
[36m(train_xgb_ray_tune pid=2376746)[0m [541]	train-mlogloss:1.46877	validation-mlogloss:1.54949
[36m(train_xgb_ray_tune pid=2376728)[0m [624]	train-mlogloss:1.41333	validation-mlogloss:1.48796
[36m(train_xgb_ray_tune pid=2376728)[0m [625]	train-mlogloss:1.41297	validation-mlogloss:1.48785
[36m(train_xgb_ray_tune pid=2376728)[0m [626]	train-mlogloss:1.41275	validation-mlogloss:1.48781
[36m(train_xgb_ray_tune pid=2376728)[0m [627]	train-mlogloss:1.41246	validation-mlogloss:1.48759
[36m(train_xgb_ray_tune pid=2376750)[0m [553]	train-mlogloss:1.56920	validation-mlogloss:1.61691
[36m(train_xgb_ray_tune pid=2376750)[0m [554]	train-mlogloss:1.56884	validation-mlogloss:1.61661
[36m(train_xgb_ray_tune pid=2376750)[0m [555]	train-mlogloss:1.56861	validation-mlogloss:1.61639
[36m(train_xgb_ray_tune pid=2376750)[0m [556]	train-mlogloss:1.56832	validation-mlogloss:1.61614
[36m(train_xgb_ray_tune pid=2376749)[0m [672]	train-mlogloss:1.86370	validation-mlogloss:1.85507
[36m(train_xgb_ray_tune pid=2376749)[0m [673]	train-mlogloss:1.86354	validation-mlogloss:1.85492
[36m(train_xgb_ray_tune pid=2376736)[0m [657]	train-mlogloss:1.66813	validation-mlogloss:1.69228
[36m(train_xgb_ray_tune pid=2376736)[0m [658]	train-mlogloss:1.66795	validation-mlogloss:1.69214
[36m(train_xgb_ray_tune pid=2376736)[0m [659]	train-mlogloss:1.66777	validation-mlogloss:1.69198
[36m(train_xgb_ray_tune pid=2376736)[0m [660]	train-mlogloss:1.66762	validation-mlogloss:1.69184
[36m(train_xgb_ray_tune pid=2376736)[0m [661]	train-mlogloss:1.66747	validation-mlogloss:1.69174
[36m(train_xgb_ray_tune pid=2376746)[0m [542]	train-mlogloss:1.46840	validation-mlogloss:1.54916
[36m(train_xgb_ray_tune pid=2376746)[0m [543]	train-mlogloss:1.46803	validation-mlogloss:1.54883
[36m(train_xgb_ray_tune pid=2376746)[0m [544]	train-mlogloss:1.46766	validation-mlogloss:1.54848
[36m(train_xgb_ray_tune pid=2376746)[0m [545]	train-mlogloss:1.46729	validation-mlogloss:1.54807
[36m(train_xgb_ray_tune pid=2376728)[0m [628]	train-mlogloss:1.41218	validation-mlogloss:1.48745
[36m(train_xgb_ray_tune pid=2376750)[0m [557]	train-mlogloss:1.56809	validation-mlogloss:1.61600
[36m(train_xgb_ray_tune pid=2376750)[0m [558]	train-mlogloss:1.56794	validation-mlogloss:1.61586
[36m(train_xgb_ray_tune pid=2376750)[0m [559]	train-mlogloss:1.56767	validation-mlogloss:1.61566
[36m(train_xgb_ray_tune pid=2376750)[0m [560]	train-mlogloss:1.56745	validation-mlogloss:1.61547
[36m(train_xgb_ray_tune pid=2376736)[0m [662]	train-mlogloss:1.66729	validation-mlogloss:1.69157
[36m(train_xgb_ray_tune pid=2376736)[0m [663]	train-mlogloss:1.66717	validation-mlogloss:1.69146
[36m(train_xgb_ray_tune pid=2376736)[0m [664]	train-mlogloss:1.66699	validation-mlogloss:1.69132
[36m(train_xgb_ray_tune pid=2376736)[0m [665]	train-mlogloss:1.66686	validation-mlogloss:1.69124
[36m(train_xgb_ray_tune pid=2376746)[0m [546]	train-mlogloss:1.46687	validation-mlogloss:1.54778
[36m(train_xgb_ray_tune pid=2376746)[0m [547]	train-mlogloss:1.46648	validation-mlogloss:1.54747
[36m(train_xgb_ray_tune pid=2376746)[0m [548]	train-mlogloss:1.46600	validation-mlogloss:1.54710
[36m(train_xgb_ray_tune pid=2376746)[0m [549]	train-mlogloss:1.46563	validation-mlogloss:1.54678
[36m(train_xgb_ray_tune pid=2376750)[0m [561]	train-mlogloss:1.56725	validation-mlogloss:1.61531
[36m(train_xgb_ray_tune pid=2376750)[0m [562]	train-mlogloss:1.56707	validation-mlogloss:1.61518
[36m(train_xgb_ray_tune pid=2376750)[0m [563]	train-mlogloss:1.56686	validation-mlogloss:1.61499
[36m(train_xgb_ray_tune pid=2376750)[0m [564]	train-mlogloss:1.56664	validation-mlogloss:1.61482
[36m(train_xgb_ray_tune pid=2376736)[0m [666]	train-mlogloss:1.66666	validation-mlogloss:1.69104
[36m(train_xgb_ray_tune pid=2376736)[0m [667]	train-mlogloss:1.66648	validation-mlogloss:1.69086
[36m(train_xgb_ray_tune pid=2376736)[0m [668]	train-mlogloss:1.66630	validation-mlogloss:1.69067
[36m(train_xgb_ray_tune pid=2376736)[0m [669]	train-mlogloss:1.66614	validation-mlogloss:1.69054
[36m(train_xgb_ray_tune pid=2376736)[0m [670]	train-mlogloss:1.66595	validation-mlogloss:1.69038
[36m(train_xgb_ray_tune pid=2376746)[0m [550]	train-mlogloss:1.46517	validation-mlogloss:1.54639
[36m(train_xgb_ray_tune pid=2376746)[0m [551]	train-mlogloss:1.46476	validation-mlogloss:1.54607
[36m(train_xgb_ray_tune pid=2376746)[0m [552]	train-mlogloss:1.46445	validation-mlogloss:1.54576
[36m(train_xgb_ray_tune pid=2376746)[0m [553]	train-mlogloss:1.46398	validation-mlogloss:1.54538
[36m(train_xgb_ray_tune pid=2376750)[0m [565]	train-mlogloss:1.56642	validation-mlogloss:1.61466
[36m(train_xgb_ray_tune pid=2376750)[0m [566]	train-mlogloss:1.56624	validation-mlogloss:1.61449
[36m(train_xgb_ray_tune pid=2376750)[0m [567]	train-mlogloss:1.56596	validation-mlogloss:1.61425
[36m(train_xgb_ray_tune pid=2376750)[0m [568]	train-mlogloss:1.56566	validation-mlogloss:1.61400
[36m(train_xgb_ray_tune pid=2376750)[0m [569]	train-mlogloss:1.56543	validation-mlogloss:1.61381
[36m(train_xgb_ray_tune pid=2376736)[0m [671]	train-mlogloss:1.66575	validation-mlogloss:1.69022
[36m(train_xgb_ray_tune pid=2376736)[0m [672]	train-mlogloss:1.66557	validation-mlogloss:1.69003
[36m(train_xgb_ray_tune pid=2376736)[0m [673]	train-mlogloss:1.66541	validation-mlogloss:1.68985
[36m(train_xgb_ray_tune pid=2376736)[0m [674]	train-mlogloss:1.66527	validation-mlogloss:1.68976
[36m(train_xgb_ray_tune pid=2376746)[0m [554]	train-mlogloss:1.46357	validation-mlogloss:1.54502
[36m(train_xgb_ray_tune pid=2376746)[0m [555]	train-mlogloss:1.46312	validation-mlogloss:1.54475
[36m(train_xgb_ray_tune pid=2376746)[0m [556]	train-mlogloss:1.46272	validation-mlogloss:1.54448
[36m(train_xgb_ray_tune pid=2376746)[0m [557]	train-mlogloss:1.46234	validation-mlogloss:1.54417
[36m(train_xgb_ray_tune pid=2376750)[0m [570]	train-mlogloss:1.56518	validation-mlogloss:1.61356
[36m(train_xgb_ray_tune pid=2376750)[0m [571]	train-mlogloss:1.56492	validation-mlogloss:1.61336
[36m(train_xgb_ray_tune pid=2376750)[0m [572]	train-mlogloss:1.56473	validation-mlogloss:1.61320
[36m(train_xgb_ray_tune pid=2376750)[0m [573]	train-mlogloss:1.56447	validation-mlogloss:1.61299
[36m(train_xgb_ray_tune pid=2376736)[0m [675]	train-mlogloss:1.66509	validation-mlogloss:1.68960

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:57. Total running time: 1min 10s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             67.6722 │
│ time_total_s                 67.6722 │
│ training_iteration                 1 │
│ validation_accuracy           0.4087 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:57. Total running time: 1min 10s
[36m(train_xgb_ray_tune pid=2376736)[0m [676]	train-mlogloss:1.66494	validation-mlogloss:1.68946
[36m(train_xgb_ray_tune pid=2376746)[0m [558]	train-mlogloss:1.46187	validation-mlogloss:1.54389
[36m(train_xgb_ray_tune pid=2376746)[0m [559]	train-mlogloss:1.46147	validation-mlogloss:1.54362
[36m(train_xgb_ray_tune pid=2376746)[0m [560]	train-mlogloss:1.46103	validation-mlogloss:1.54328
[36m(train_xgb_ray_tune pid=2376746)[0m [561]	train-mlogloss:1.46066	validation-mlogloss:1.54293
[36m(train_xgb_ray_tune pid=2376750)[0m [574]	train-mlogloss:1.56425	validation-mlogloss:1.61282
[36m(train_xgb_ray_tune pid=2376750)[0m [575]	train-mlogloss:1.56405	validation-mlogloss:1.61265
[36m(train_xgb_ray_tune pid=2376750)[0m [576]	train-mlogloss:1.56393	validation-mlogloss:1.61251
[36m(train_xgb_ray_tune pid=2376750)[0m [577]	train-mlogloss:1.56366	validation-mlogloss:1.61235
[36m(train_xgb_ray_tune pid=2376746)[0m [562]	train-mlogloss:1.46027	validation-mlogloss:1.54258
[36m(train_xgb_ray_tune pid=2376746)[0m [563]	train-mlogloss:1.45988	validation-mlogloss:1.54235
[36m(train_xgb_ray_tune pid=2376746)[0m [564]	train-mlogloss:1.45947	validation-mlogloss:1.54205
[36m(train_xgb_ray_tune pid=2376746)[0m [565]	train-mlogloss:1.45899	validation-mlogloss:1.54164
[36m(train_xgb_ray_tune pid=2376750)[0m [578]	train-mlogloss:1.56339	validation-mlogloss:1.61214
[36m(train_xgb_ray_tune pid=2376750)[0m [579]	train-mlogloss:1.56319	validation-mlogloss:1.61195
[36m(train_xgb_ray_tune pid=2376750)[0m [580]	train-mlogloss:1.56296	validation-mlogloss:1.61175
[36m(train_xgb_ray_tune pid=2376750)[0m [581]	train-mlogloss:1.56273	validation-mlogloss:1.61154
[36m(train_xgb_ray_tune pid=2376750)[0m [582]	train-mlogloss:1.56257	validation-mlogloss:1.61142
[36m(train_xgb_ray_tune pid=2376746)[0m [566]	train-mlogloss:1.45855	validation-mlogloss:1.54134
[36m(train_xgb_ray_tune pid=2376746)[0m [567]	train-mlogloss:1.45816	validation-mlogloss:1.54099
[36m(train_xgb_ray_tune pid=2376746)[0m [568]	train-mlogloss:1.45781	validation-mlogloss:1.54075
[36m(train_xgb_ray_tune pid=2376746)[0m [569]	train-mlogloss:1.45739	validation-mlogloss:1.54042
[36m(train_xgb_ray_tune pid=2376750)[0m [583]	train-mlogloss:1.56229	validation-mlogloss:1.61119
[36m(train_xgb_ray_tune pid=2376750)[0m [584]	train-mlogloss:1.56209	validation-mlogloss:1.61099
[36m(train_xgb_ray_tune pid=2376750)[0m [585]	train-mlogloss:1.56191	validation-mlogloss:1.61081
[36m(train_xgb_ray_tune pid=2376750)[0m [586]	train-mlogloss:1.56172	validation-mlogloss:1.61059

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:57. Total running time: 1min 11s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             68.2104 │
│ time_total_s                 68.2104 │
│ training_iteration                 1 │
│ validation_accuracy          0.47332 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:57. Total running time: 1min 11s
[36m(train_xgb_ray_tune pid=2376746)[0m [570]	train-mlogloss:1.45698	validation-mlogloss:1.54011
[36m(train_xgb_ray_tune pid=2376746)[0m [571]	train-mlogloss:1.45660	validation-mlogloss:1.53977
[36m(train_xgb_ray_tune pid=2376746)[0m [572]	train-mlogloss:1.45623	validation-mlogloss:1.53945
[36m(train_xgb_ray_tune pid=2376746)[0m [573]	train-mlogloss:1.45587	validation-mlogloss:1.53921
[36m(train_xgb_ray_tune pid=2376750)[0m [587]	train-mlogloss:1.56150	validation-mlogloss:1.61040
[36m(train_xgb_ray_tune pid=2376750)[0m [588]	train-mlogloss:1.56133	validation-mlogloss:1.61024
[36m(train_xgb_ray_tune pid=2376750)[0m [589]	train-mlogloss:1.56115	validation-mlogloss:1.61012
[36m(train_xgb_ray_tune pid=2376750)[0m [590]	train-mlogloss:1.56094	validation-mlogloss:1.60990
[36m(train_xgb_ray_tune pid=2376750)[0m [591]	train-mlogloss:1.56069	validation-mlogloss:1.60966
[36m(train_xgb_ray_tune pid=2376746)[0m [574]	train-mlogloss:1.45549	validation-mlogloss:1.53899
[36m(train_xgb_ray_tune pid=2376746)[0m [575]	train-mlogloss:1.45508	validation-mlogloss:1.53873
[36m(train_xgb_ray_tune pid=2376746)[0m [576]	train-mlogloss:1.45465	validation-mlogloss:1.53841
[36m(train_xgb_ray_tune pid=2376746)[0m [577]	train-mlogloss:1.45425	validation-mlogloss:1.53807
[36m(train_xgb_ray_tune pid=2376750)[0m [592]	train-mlogloss:1.56045	validation-mlogloss:1.60943
[36m(train_xgb_ray_tune pid=2376750)[0m [593]	train-mlogloss:1.56024	validation-mlogloss:1.60926
[36m(train_xgb_ray_tune pid=2376750)[0m [594]	train-mlogloss:1.56009	validation-mlogloss:1.60915
[36m(train_xgb_ray_tune pid=2376750)[0m [595]	train-mlogloss:1.55985	validation-mlogloss:1.60896
[36m(train_xgb_ray_tune pid=2376746)[0m [578]	train-mlogloss:1.45387	validation-mlogloss:1.53779
[36m(train_xgb_ray_tune pid=2376746)[0m [579]	train-mlogloss:1.45348	validation-mlogloss:1.53753
[36m(train_xgb_ray_tune pid=2376746)[0m [580]	train-mlogloss:1.45312	validation-mlogloss:1.53728
[36m(train_xgb_ray_tune pid=2376746)[0m [581]	train-mlogloss:1.45273	validation-mlogloss:1.53700

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:58. Total running time: 1min 11s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             68.5574 │
│ time_total_s                 68.5574 │
│ training_iteration                 1 │
│ validation_accuracy          0.42569 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:58. Total running time: 1min 11s
[36m(train_xgb_ray_tune pid=2376746)[0m [582]	train-mlogloss:1.45236	validation-mlogloss:1.53667
[36m(train_xgb_ray_tune pid=2376746)[0m [583]	train-mlogloss:1.45202	validation-mlogloss:1.53645
[36m(train_xgb_ray_tune pid=2376746)[0m [584]	train-mlogloss:1.45164	validation-mlogloss:1.53613
[36m(train_xgb_ray_tune pid=2376746)[0m [585]	train-mlogloss:1.45124	validation-mlogloss:1.53584
[36m(train_xgb_ray_tune pid=2376746)[0m [586]	train-mlogloss:1.45080	validation-mlogloss:1.53560
[36m(train_xgb_ray_tune pid=2376746)[0m [587]	train-mlogloss:1.45041	validation-mlogloss:1.53531
[36m(train_xgb_ray_tune pid=2376746)[0m [588]	train-mlogloss:1.45006	validation-mlogloss:1.53500
[36m(train_xgb_ray_tune pid=2376746)[0m [589]	train-mlogloss:1.44973	validation-mlogloss:1.53468
[36m(train_xgb_ray_tune pid=2376746)[0m [590]	train-mlogloss:1.44933	validation-mlogloss:1.53437
[36m(train_xgb_ray_tune pid=2376746)[0m [591]	train-mlogloss:1.44891	validation-mlogloss:1.53405
[36m(train_xgb_ray_tune pid=2376746)[0m [592]	train-mlogloss:1.44856	validation-mlogloss:1.53378
[36m(train_xgb_ray_tune pid=2376746)[0m [593]	train-mlogloss:1.44819	validation-mlogloss:1.53347
[36m(train_xgb_ray_tune pid=2376746)[0m [594]	train-mlogloss:1.44780	validation-mlogloss:1.53319
[36m(train_xgb_ray_tune pid=2376746)[0m [595]	train-mlogloss:1.44740	validation-mlogloss:1.53295
[36m(train_xgb_ray_tune pid=2376746)[0m [596]	train-mlogloss:1.44705	validation-mlogloss:1.53259
[36m(train_xgb_ray_tune pid=2376746)[0m [597]	train-mlogloss:1.44667	validation-mlogloss:1.53233

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:18:58. Total running time: 1min 11s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             68.7644 │
│ time_total_s                 68.7644 │
│ training_iteration                 1 │
│ validation_accuracy          0.45455 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:18:58. Total running time: 1min 11s
[36m(train_xgb_ray_tune pid=2376746)[0m [598]	train-mlogloss:1.44634	validation-mlogloss:1.53201
[36m(train_xgb_ray_tune pid=2376746)[0m [599]	train-mlogloss:1.44594	validation-mlogloss:1.53173
[36m(train_xgb_ray_tune pid=2376746)[0m [600]	train-mlogloss:1.44556	validation-mlogloss:1.53142
[36m(train_xgb_ray_tune pid=2376746)[0m [601]	train-mlogloss:1.44524	validation-mlogloss:1.53112
[36m(train_xgb_ray_tune pid=2376746)[0m [602]	train-mlogloss:1.44489	validation-mlogloss:1.53086
[36m(train_xgb_ray_tune pid=2376746)[0m [603]	train-mlogloss:1.44455	validation-mlogloss:1.53064
[36m(train_xgb_ray_tune pid=2376746)[0m [604]	train-mlogloss:1.44421	validation-mlogloss:1.53039
[36m(train_xgb_ray_tune pid=2376746)[0m [605]	train-mlogloss:1.44386	validation-mlogloss:1.53008
[36m(train_xgb_ray_tune pid=2376746)[0m [606]	train-mlogloss:1.44354	validation-mlogloss:1.52978
[36m(train_xgb_ray_tune pid=2376746)[0m [607]	train-mlogloss:1.44323	validation-mlogloss:1.52953
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[36m(train_xgb_ray_tune pid=2376746)[0m [610]	train-mlogloss:1.44218	validation-mlogloss:1.52877
[36m(train_xgb_ray_tune pid=2376746)[0m [611]	train-mlogloss:1.44181	validation-mlogloss:1.52853
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[36m(train_xgb_ray_tune pid=2376746)[0m [617]	train-mlogloss:1.43957	validation-mlogloss:1.52702
[36m(train_xgb_ray_tune pid=2376746)[0m [618]	train-mlogloss:1.43924	validation-mlogloss:1.52675
[36m(train_xgb_ray_tune pid=2376746)[0m [619]	train-mlogloss:1.43889	validation-mlogloss:1.52646
[36m(train_xgb_ray_tune pid=2376746)[0m [620]	train-mlogloss:1.43851	validation-mlogloss:1.52621
[36m(train_xgb_ray_tune pid=2376746)[0m [621]	train-mlogloss:1.43816	validation-mlogloss:1.52596
[36m(train_xgb_ray_tune pid=2376746)[0m [622]	train-mlogloss:1.43786	validation-mlogloss:1.52580
[36m(train_xgb_ray_tune pid=2376746)[0m [623]	train-mlogloss:1.43753	validation-mlogloss:1.52560
[36m(train_xgb_ray_tune pid=2376746)[0m [624]	train-mlogloss:1.43721	validation-mlogloss:1.52535
[36m(train_xgb_ray_tune pid=2376746)[0m [625]	train-mlogloss:1.43686	validation-mlogloss:1.52506
[36m(train_xgb_ray_tune pid=2376746)[0m [626]	train-mlogloss:1.43659	validation-mlogloss:1.52479
[36m(train_xgb_ray_tune pid=2376746)[0m [627]	train-mlogloss:1.43625	validation-mlogloss:1.52458
[36m(train_xgb_ray_tune pid=2376746)[0m [628]	train-mlogloss:1.43592	validation-mlogloss:1.52429
[36m(train_xgb_ray_tune pid=2376746)[0m [629]	train-mlogloss:1.43565	validation-mlogloss:1.52410
[36m(train_xgb_ray_tune pid=2376746)[0m [630]	train-mlogloss:1.43526	validation-mlogloss:1.52379
[36m(train_xgb_ray_tune pid=2376746)[0m [631]	train-mlogloss:1.43488	validation-mlogloss:1.52358
[36m(train_xgb_ray_tune pid=2376746)[0m [632]	train-mlogloss:1.43458	validation-mlogloss:1.52333
[36m(train_xgb_ray_tune pid=2376746)[0m [633]	train-mlogloss:1.43423	validation-mlogloss:1.52311
[36m(train_xgb_ray_tune pid=2376746)[0m [634]	train-mlogloss:1.43388	validation-mlogloss:1.52287
[36m(train_xgb_ray_tune pid=2376746)[0m [635]	train-mlogloss:1.43356	validation-mlogloss:1.52260
[36m(train_xgb_ray_tune pid=2376746)[0m [636]	train-mlogloss:1.43323	validation-mlogloss:1.52238
[36m(train_xgb_ray_tune pid=2376746)[0m [637]	train-mlogloss:1.43289	validation-mlogloss:1.52217
2025-11-07 10:19:01,176	INFO tune.py:1009 -- Wrote the latest version of all result files and experiment state to '/mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_XGB_acc_17_classes/XGBoost_hyperparameters_tuning' in 0.0085s.
[36m(train_xgb_ray_tune pid=2376746)[0m [638]	train-mlogloss:1.43261	validation-mlogloss:1.52190
[36m(train_xgb_ray_tune pid=2376746)[0m [639]	train-mlogloss:1.43234	validation-mlogloss:1.52166
[36m(train_xgb_ray_tune pid=2376746)[0m [640]	train-mlogloss:1.43205	validation-mlogloss:1.52145
[36m(train_xgb_ray_tune pid=2376746)[0m [641]	train-mlogloss:1.43172	validation-mlogloss:1.52123
[36m(train_xgb_ray_tune pid=2376746)[0m [642]	train-mlogloss:1.43145	validation-mlogloss:1.52102
[36m(train_xgb_ray_tune pid=2376746)[0m [643]	train-mlogloss:1.43109	validation-mlogloss:1.52083
[36m(train_xgb_ray_tune pid=2376746)[0m [644]	train-mlogloss:1.43076	validation-mlogloss:1.52070
[36m(train_xgb_ray_tune pid=2376746)[0m [645]	train-mlogloss:1.43047	validation-mlogloss:1.52045
[36m(train_xgb_ray_tune pid=2376746)[0m [646]	train-mlogloss:1.43017	validation-mlogloss:1.52012
[36m(train_xgb_ray_tune pid=2376746)[0m [647]	train-mlogloss:1.42988	validation-mlogloss:1.51995
[36m(train_xgb_ray_tune pid=2376746)[0m [648]	train-mlogloss:1.42958	validation-mlogloss:1.51974
[36m(train_xgb_ray_tune pid=2376746)[0m [649]	train-mlogloss:1.42927	validation-mlogloss:1.51955
[36m(train_xgb_ray_tune pid=2376746)[0m [650]	train-mlogloss:1.42895	validation-mlogloss:1.51936
[36m(train_xgb_ray_tune pid=2376746)[0m [651]	train-mlogloss:1.42865	validation-mlogloss:1.51914
[36m(train_xgb_ray_tune pid=2376746)[0m [652]	train-mlogloss:1.42834	validation-mlogloss:1.51895
[36m(train_xgb_ray_tune pid=2376746)[0m [653]	train-mlogloss:1.42804	validation-mlogloss:1.51872
[36m(train_xgb_ray_tune pid=2376746)[0m [654]	train-mlogloss:1.42773	validation-mlogloss:1.51847
[36m(train_xgb_ray_tune pid=2376746)[0m [655]	train-mlogloss:1.42744	validation-mlogloss:1.51831
[36m(train_xgb_ray_tune pid=2376746)[0m [656]	train-mlogloss:1.42713	validation-mlogloss:1.51815
[36m(train_xgb_ray_tune pid=2376746)[0m [657]	train-mlogloss:1.42689	validation-mlogloss:1.51798
[36m(train_xgb_ray_tune pid=2376746)[0m [658]	train-mlogloss:1.42656	validation-mlogloss:1.51778
[36m(train_xgb_ray_tune pid=2376746)[0m [659]	train-mlogloss:1.42629	validation-mlogloss:1.51756
[36m(train_xgb_ray_tune pid=2376746)[0m [660]	train-mlogloss:1.42597	validation-mlogloss:1.51733
[36m(train_xgb_ray_tune pid=2376746)[0m [661]	train-mlogloss:1.42569	validation-mlogloss:1.51714
[36m(train_xgb_ray_tune pid=2376746)[0m [662]	train-mlogloss:1.42540	validation-mlogloss:1.51700
[36m(train_xgb_ray_tune pid=2376746)[0m [663]	train-mlogloss:1.42510	validation-mlogloss:1.51683
[36m(train_xgb_ray_tune pid=2376746)[0m [664]	train-mlogloss:1.42490	validation-mlogloss:1.51663
[36m(train_xgb_ray_tune pid=2376746)[0m [665]	train-mlogloss:1.42463	validation-mlogloss:1.51645
[36m(train_xgb_ray_tune pid=2376746)[0m [666]	train-mlogloss:1.42433	validation-mlogloss:1.51624
[36m(train_xgb_ray_tune pid=2376746)[0m [667]	train-mlogloss:1.42404	validation-mlogloss:1.51602
[36m(train_xgb_ray_tune pid=2376746)[0m [668]	train-mlogloss:1.42372	validation-mlogloss:1.51591
[36m(train_xgb_ray_tune pid=2376746)[0m [669]	train-mlogloss:1.42341	validation-mlogloss:1.51564
[36m(train_xgb_ray_tune pid=2376746)[0m [670]	train-mlogloss:1.42315	validation-mlogloss:1.51545
[36m(train_xgb_ray_tune pid=2376746)[0m [671]	train-mlogloss:1.42282	validation-mlogloss:1.51527
[36m(train_xgb_ray_tune pid=2376746)[0m [672]	train-mlogloss:1.42250	validation-mlogloss:1.51512
[36m(train_xgb_ray_tune pid=2376746)[0m [673]	train-mlogloss:1.42221	validation-mlogloss:1.51494
[36m(train_xgb_ray_tune pid=2376746)[0m [674]	train-mlogloss:1.42196	validation-mlogloss:1.51475
[36m(train_xgb_ray_tune pid=2376746)[0m [675]	train-mlogloss:1.42174	validation-mlogloss:1.51459
[36m(train_xgb_ray_tune pid=2376746)[0m [676]	train-mlogloss:1.42142	validation-mlogloss:1.51437

Trial trial_9fac0 finished iteration 1 at 2025-11-07 10:19:01. Total running time: 1min 14s
╭──────────────────────────────────────╮
│ Trial trial_9fac0 result             │
├──────────────────────────────────────┤
│ checkpoint_dir_name                  │
│ time_this_iter_s             71.4858 │
│ time_total_s                 71.4858 │
│ training_iteration                 1 │
│ validation_accuracy          0.46482 │
╰──────────────────────────────────────╯

Trial trial_9fac0 completed after 1 iterations at 2025-11-07 10:19:01. Total running time: 1min 14s

Trial status: 20 TERMINATED
Current time: 2025-11-07 10:19:01. Total running time: 1min 14s
Logical resource usage: 1.0/20 CPUs, 0/1 GPUs (0.0/1.0 accelerator_type:G)
[36m(train_xgb_ray_tune pid=2376746)[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 7x across cluster][0m
[36m(train_xgb_ray_tune pid=2376746)[0m   _log_deprecation_warning([32m [repeated 7x across cluster][0m
╭────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ 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_9fac0    TERMINATED                 629             3        0.0129937       0.550764             0.826163    6.03809                  189       1.39658       47.2362              4621        1            68.2104                0.47332  │
│ trial_9fac0    TERMINATED                 496             3        0.011035        0.782066             0.57042     9.27748                  175       8.37826        6.3327              5415        1            65.4992                0.462846 │
│ trial_9fac0    TERMINATED                 367             2        0.0119098       0.822007             0.650588   16.299                    145      20.0921       538.425               1516        1            51.6578                0.4083   │
│ trial_9fac0    TERMINATED                 458             3        0.0130302       0.668953             0.523103    5.35156                   64      60.2809         5.22198             5380        1            60.2786                0.4583   │
│ trial_9fac0    TERMINATED                 356             2        0.0138383       0.824493             0.601674   25.6598                    95     465.168          7.60973             8067        1            42.3991                0.22747  │
│ trial_9fac0    TERMINATED                 466             2        0.00866783      0.506976             0.814022   21.9078                   170       3.50686      253.511               6833        1            60.2651                0.41502  │
│ trial_9fac0    TERMINATED                 665             2        0.0142689       0.700216             0.722649   22.8491                   126       9.67768      139.085               6238        1            66.8366                0.437549 │
│ trial_9fac0    TERMINATED                 373             3        0.00947212      0.564613             0.636997   12.8265                    76       2.40208       49.9773              7375        1            59.7961                0.456324 │
│ trial_9fac0    TERMINATED                 267             2        0.018387        0.590628             0.699044   16.8385                   165       7.23861      436.303               3292        1            44.8149                0.414625 │
│ trial_9fac0    TERMINATED                 387             3        0.0055591       0.717148             0.568672   17.6475                    81      67.7854       119.06                5871        1            56.7127                0.426482 │
│ trial_9fac0    TERMINATED                 507             3        0.0103495       0.821424             0.79813    12.3559                    83       6.42268        6.56943              354        1            66.4107                0.471739 │
│ trial_9fac0    TERMINATED                 677             3        0.00832983      0.81227              0.590491   12.3513                   179       2.83345       12.1536              7308        1            71.4858                0.464822 │
│ trial_9fac0    TERMINATED                 596             3        0.0133168       0.711692             0.693198    5.42144                  143      49.0868       214.24                3090        1            68.7644                0.454545 │
│ trial_9fac0    TERMINATED                 423             3        0.0131105       0.651097             0.668651   19.0739                   168       6.14307      683.04                 900        1            59.5212                0.411265 │
│ trial_9fac0    TERMINATED                 251             2        0.0151609       0.841882             0.513321   29.2414                   177       9.6365         6.78319             3251        1            41.8084                0.431423 │
│ trial_9fac0    TERMINATED                 209             3        0.0129513       0.629164             0.631645    9.7387                    70       2.36629        5.1895              4523        1            41.5151                0.463241 │
│ trial_9fac0    TERMINATED                 674             2        0.0105649       0.591425             0.549683   15.0153                   193      52.9773       522.183               3209        1            67.6722                0.408696 │
│ trial_9fac0    TERMINATED                 685             2        0.0140109       0.745984             0.730149   14.4615                    89     401.815         55.8077              2699        1            62.762                 0.261858 │
│ trial_9fac0    TERMINATED                 677             3        0.0123745       0.610346             0.800135   11.9738                   113      31.1399       397.982               5500        1            68.5574                0.425692 │
│ trial_9fac0    TERMINATED                 256             2        0.0194417       0.816468             0.590046    5.98402                  117     750.545        251.807                392        1            33.3086                0.131621 │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Mejores hiperparámetros XGBoost: {'num_boost_round': 629, 'max_depth': 3, 'learning_rate': 0.012993676043017946, 'subsample': 0.5507643279118813, 'colsample_bytree': 0.8261631451078342, 'gamma': 6.038085781948473, 'min_child_weight': 189, 'reg_alpha': 1.3965836389518418, 'reg_lambda': 47.23622442906644, 'random_state': 4621}
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[557]	train-mlogloss:1.43604	validation-mlogloss:1.50298
[558]	train-mlogloss:1.43562	validation-mlogloss:1.50271
[559]	train-mlogloss:1.43529	validation-mlogloss:1.50248
[560]	train-mlogloss:1.43488	validation-mlogloss:1.50225
[561]	train-mlogloss:1.43453	validation-mlogloss:1.50202
[562]	train-mlogloss:1.43415	validation-mlogloss:1.50175
[563]	train-mlogloss:1.43380	validation-mlogloss:1.50150
[564]	train-mlogloss:1.43346	validation-mlogloss:1.50126
[565]	train-mlogloss:1.43308	validation-mlogloss:1.50104
[566]	train-mlogloss:1.43268	validation-mlogloss:1.50087
[567]	train-mlogloss:1.43232	validation-mlogloss:1.50062
[568]	train-mlogloss:1.43193	validation-mlogloss:1.50038
[569]	train-mlogloss:1.43152	validation-mlogloss:1.50021
[570]	train-mlogloss:1.43120	validation-mlogloss:1.49999
[571]	train-mlogloss:1.43079	validation-mlogloss:1.49974
[572]	train-mlogloss:1.43043	validation-mlogloss:1.49950
[573]	train-mlogloss:1.43008	validation-mlogloss:1.49922
[574]	train-mlogloss:1.42971	validation-mlogloss:1.49900
[575]	train-mlogloss:1.42932	validation-mlogloss:1.49878
[576]	train-mlogloss:1.42896	validation-mlogloss:1.49853
[577]	train-mlogloss:1.42856	validation-mlogloss:1.49822
[578]	train-mlogloss:1.42821	validation-mlogloss:1.49792
[579]	train-mlogloss:1.42789	validation-mlogloss:1.49775
[580]	train-mlogloss:1.42754	validation-mlogloss:1.49754
[581]	train-mlogloss:1.42717	validation-mlogloss:1.49736
[582]	train-mlogloss:1.42682	validation-mlogloss:1.49716
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[584]	train-mlogloss:1.42611	validation-mlogloss:1.49691
[585]	train-mlogloss:1.42581	validation-mlogloss:1.49671
[586]	train-mlogloss:1.42551	validation-mlogloss:1.49656
[587]	train-mlogloss:1.42512	validation-mlogloss:1.49627
[588]	train-mlogloss:1.42482	validation-mlogloss:1.49605
[589]	train-mlogloss:1.42446	validation-mlogloss:1.49573
[590]	train-mlogloss:1.42413	validation-mlogloss:1.49552
[591]	train-mlogloss:1.42380	validation-mlogloss:1.49542
[592]	train-mlogloss:1.42346	validation-mlogloss:1.49522
[593]	train-mlogloss:1.42313	validation-mlogloss:1.49498
[594]	train-mlogloss:1.42279	validation-mlogloss:1.49480
[595]	train-mlogloss:1.42241	validation-mlogloss:1.49459
[596]	train-mlogloss:1.42211	validation-mlogloss:1.49445
[597]	train-mlogloss:1.42175	validation-mlogloss:1.49437
[598]	train-mlogloss:1.42148	validation-mlogloss:1.49418
[599]	train-mlogloss:1.42117	validation-mlogloss:1.49397
[600]	train-mlogloss:1.42083	validation-mlogloss:1.49379
[601]	train-mlogloss:1.42053	validation-mlogloss:1.49370
[602]	train-mlogloss:1.42021	validation-mlogloss:1.49353
[603]	train-mlogloss:1.41985	validation-mlogloss:1.49322
[604]	train-mlogloss:1.41952	validation-mlogloss:1.49295
[605]	train-mlogloss:1.41919	validation-mlogloss:1.49283
[606]	train-mlogloss:1.41882	validation-mlogloss:1.49266
[607]	train-mlogloss:1.41843	validation-mlogloss:1.49245
[608]	train-mlogloss:1.41806	validation-mlogloss:1.49234
[609]	train-mlogloss:1.41774	validation-mlogloss:1.49205
[610]	train-mlogloss:1.41738	validation-mlogloss:1.49186
[611]	train-mlogloss:1.41703	validation-mlogloss:1.49162
[612]	train-mlogloss:1.41675	validation-mlogloss:1.49146
[613]	train-mlogloss:1.41644	validation-mlogloss:1.49138
[614]	train-mlogloss:1.41608	validation-mlogloss:1.49122
[615]	train-mlogloss:1.41583	validation-mlogloss:1.49107
[616]	train-mlogloss:1.41552	validation-mlogloss:1.49088
[617]	train-mlogloss:1.41520	validation-mlogloss:1.49067
[618]	train-mlogloss:1.41487	validation-mlogloss:1.49045
[619]	train-mlogloss:1.41452	validation-mlogloss:1.49019
[620]	train-mlogloss:1.41420	validation-mlogloss:1.49006
[621]	train-mlogloss:1.41387	validation-mlogloss:1.48987
[622]	train-mlogloss:1.41355	validation-mlogloss:1.48965
[623]	train-mlogloss:1.41325	validation-mlogloss:1.48952
[624]	train-mlogloss:1.41299	validation-mlogloss:1.48936
[625]	train-mlogloss:1.41268	validation-mlogloss:1.48913
[626]	train-mlogloss:1.41244	validation-mlogloss:1.48896
[627]	train-mlogloss:1.41215	validation-mlogloss:1.48879
[628]	train-mlogloss:1.41186	validation-mlogloss:1.48848
Saved model to disk.
2025-11-07 10:19:22.833282: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:19:22.844460: 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 00:00:1762507162.857588 2379257 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507162.861795 2379257 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507162.871614 2379257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507162.871634 2379257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507162.871637 2379257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507162.871639 2379257 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:19:22.874704: 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:2.69711	validation-mlogloss:2.69709
[1]	train-mlogloss:2.68597	validation-mlogloss:2.68582
[2]	train-mlogloss:2.67557	validation-mlogloss:2.67538
[3]	train-mlogloss:2.66523	validation-mlogloss:2.66485
[4]	train-mlogloss:2.65522	validation-mlogloss:2.65470
[5]	train-mlogloss:2.64516	validation-mlogloss:2.64464
[6]	train-mlogloss:2.63513	validation-mlogloss:2.63468
[7]	train-mlogloss:2.62494	validation-mlogloss:2.62449
[8]	train-mlogloss:2.61486	validation-mlogloss:2.61420
[9]	train-mlogloss:2.60500	validation-mlogloss:2.60419
[10]	train-mlogloss:2.59519	validation-mlogloss:2.59420
[11]	train-mlogloss:2.58543	validation-mlogloss:2.58436
[12]	train-mlogloss:2.57625	validation-mlogloss:2.57528
[13]	train-mlogloss:2.56673	validation-mlogloss:2.56579
[14]	train-mlogloss:2.55745	validation-mlogloss:2.55643
[15]	train-mlogloss:2.54843	validation-mlogloss:2.54714
[16]	train-mlogloss:2.53889	validation-mlogloss:2.53758
[17]	train-mlogloss:2.52985	validation-mlogloss:2.52851
[18]	train-mlogloss:2.52068	validation-mlogloss:2.51944
[19]	train-mlogloss:2.51136	validation-mlogloss:2.50988
[20]	train-mlogloss:2.50206	validation-mlogloss:2.50057
[21]	train-mlogloss:2.49318	validation-mlogloss:2.49146
[22]	train-mlogloss:2.48450	validation-mlogloss:2.48270
[23]	train-mlogloss:2.47603	validation-mlogloss:2.47419
[24]	train-mlogloss:2.46710	validation-mlogloss:2.46511
[25]	train-mlogloss:2.45868	validation-mlogloss:2.45662
[26]	train-mlogloss:2.45009	validation-mlogloss:2.44824
[27]	train-mlogloss:2.44193	validation-mlogloss:2.44017
[28]	train-mlogloss:2.43353	validation-mlogloss:2.43162
[29]	train-mlogloss:2.42558	validation-mlogloss:2.42360
[30]	train-mlogloss:2.41799	validation-mlogloss:2.41589
[31]	train-mlogloss:2.40976	validation-mlogloss:2.40779
[32]	train-mlogloss:2.40175	validation-mlogloss:2.39981
[33]	train-mlogloss:2.39371	validation-mlogloss:2.39187
[34]	train-mlogloss:2.38573	validation-mlogloss:2.38364
[35]	train-mlogloss:2.37824	validation-mlogloss:2.37608
[36]	train-mlogloss:2.37079	validation-mlogloss:2.36868
[37]	train-mlogloss:2.36332	validation-mlogloss:2.36120
[38]	train-mlogloss:2.35610	validation-mlogloss:2.35401
[39]	train-mlogloss:2.34903	validation-mlogloss:2.34686
[40]	train-mlogloss:2.34170	validation-mlogloss:2.33973
[41]	train-mlogloss:2.33449	validation-mlogloss:2.33243
[42]	train-mlogloss:2.32756	validation-mlogloss:2.32549
[43]	train-mlogloss:2.32082	validation-mlogloss:2.31895
[44]	train-mlogloss:2.31389	validation-mlogloss:2.31195
[45]	train-mlogloss:2.30695	validation-mlogloss:2.30506
[46]	train-mlogloss:2.30005	validation-mlogloss:2.29815
[47]	train-mlogloss:2.29306	validation-mlogloss:2.29110
[48]	train-mlogloss:2.28654	validation-mlogloss:2.28487
[49]	train-mlogloss:2.28012	validation-mlogloss:2.27831
[50]	train-mlogloss:2.27314	validation-mlogloss:2.27141
[51]	train-mlogloss:2.26644	validation-mlogloss:2.26496
[52]	train-mlogloss:2.25998	validation-mlogloss:2.25852
[53]	train-mlogloss:2.25378	validation-mlogloss:2.25240
[54]	train-mlogloss:2.24749	validation-mlogloss:2.24626
[55]	train-mlogloss:2.24124	validation-mlogloss:2.23989
[56]	train-mlogloss:2.23527	validation-mlogloss:2.23392
[57]	train-mlogloss:2.22947	validation-mlogloss:2.22794
[58]	train-mlogloss:2.22332	validation-mlogloss:2.22186
[59]	train-mlogloss:2.21718	validation-mlogloss:2.21581
[60]	train-mlogloss:2.21139	validation-mlogloss:2.21029
[61]	train-mlogloss:2.20556	validation-mlogloss:2.20452
[62]	train-mlogloss:2.19968	validation-mlogloss:2.19879
[63]	train-mlogloss:2.19380	validation-mlogloss:2.19298
[64]	train-mlogloss:2.18826	validation-mlogloss:2.18749
[65]	train-mlogloss:2.18256	validation-mlogloss:2.18179
[66]	train-mlogloss:2.17707	validation-mlogloss:2.17650
[67]	train-mlogloss:2.17171	validation-mlogloss:2.17139
[68]	train-mlogloss:2.16613	validation-mlogloss:2.16607
[69]	train-mlogloss:2.16076	validation-mlogloss:2.16117
[70]	train-mlogloss:2.15537	validation-mlogloss:2.15563
[71]	train-mlogloss:2.15042	validation-mlogloss:2.15081
[72]	train-mlogloss:2.14497	validation-mlogloss:2.14551
[73]	train-mlogloss:2.13967	validation-mlogloss:2.14031
[74]	train-mlogloss:2.13437	validation-mlogloss:2.13534
[75]	train-mlogloss:2.12930	validation-mlogloss:2.13037
[76]	train-mlogloss:2.12433	validation-mlogloss:2.12571
[77]	train-mlogloss:2.11912	validation-mlogloss:2.12068
[78]	train-mlogloss:2.11408	validation-mlogloss:2.11573
[79]	train-mlogloss:2.10900	validation-mlogloss:2.11081
[80]	train-mlogloss:2.10405	validation-mlogloss:2.10602
[81]	train-mlogloss:2.09929	validation-mlogloss:2.10123
[82]	train-mlogloss:2.09461	validation-mlogloss:2.09668
[83]	train-mlogloss:2.09002	validation-mlogloss:2.09213
[84]	train-mlogloss:2.08544	validation-mlogloss:2.08761
[85]	train-mlogloss:2.08068	validation-mlogloss:2.08288
[86]	train-mlogloss:2.07598	validation-mlogloss:2.07863
[87]	train-mlogloss:2.07127	validation-mlogloss:2.07398
[88]	train-mlogloss:2.06673	validation-mlogloss:2.06947
[89]	train-mlogloss:2.06221	validation-mlogloss:2.06503
[90]	train-mlogloss:2.05755	validation-mlogloss:2.06070
[91]	train-mlogloss:2.05304	validation-mlogloss:2.05640
[92]	train-mlogloss:2.04874	validation-mlogloss:2.05218
[93]	train-mlogloss:2.04423	validation-mlogloss:2.04778
[94]	train-mlogloss:2.03989	validation-mlogloss:2.04361
[95]	train-mlogloss:2.03585	validation-mlogloss:2.03940
[96]	train-mlogloss:2.03172	validation-mlogloss:2.03555
[97]	train-mlogloss:2.02754	validation-mlogloss:2.03165
[98]	train-mlogloss:2.02328	validation-mlogloss:2.02758
[99]	train-mlogloss:2.01915	validation-mlogloss:2.02356
[100]	train-mlogloss:2.01523	validation-mlogloss:2.01977
[101]	train-mlogloss:2.01127	validation-mlogloss:2.01620
[102]	train-mlogloss:2.00744	validation-mlogloss:2.01252
[103]	train-mlogloss:2.00343	validation-mlogloss:2.00849
[104]	train-mlogloss:1.99957	validation-mlogloss:2.00489
[105]	train-mlogloss:1.99570	validation-mlogloss:2.00109
[106]	train-mlogloss:1.99184	validation-mlogloss:1.99732
[107]	train-mlogloss:1.98790	validation-mlogloss:1.99352
[108]	train-mlogloss:1.98431	validation-mlogloss:1.99005
[109]	train-mlogloss:1.98046	validation-mlogloss:1.98643
[110]	train-mlogloss:1.97680	validation-mlogloss:1.98296
[111]	train-mlogloss:1.97319	validation-mlogloss:1.97954
[112]	train-mlogloss:1.96948	validation-mlogloss:1.97607
[113]	train-mlogloss:1.96595	validation-mlogloss:1.97273
[114]	train-mlogloss:1.96225	validation-mlogloss:1.96924
[115]	train-mlogloss:1.95876	validation-mlogloss:1.96600
[116]	train-mlogloss:1.95516	validation-mlogloss:1.96259
[117]	train-mlogloss:1.95166	validation-mlogloss:1.95935
[118]	train-mlogloss:1.94821	validation-mlogloss:1.95613
[119]	train-mlogloss:1.94500	validation-mlogloss:1.95310
[120]	train-mlogloss:1.94156	validation-mlogloss:1.94976
[121]	train-mlogloss:1.93836	validation-mlogloss:1.94676
[122]	train-mlogloss:1.93497	validation-mlogloss:1.94326
[123]	train-mlogloss:1.93142	validation-mlogloss:1.93972
[124]	train-mlogloss:1.92811	validation-mlogloss:1.93647
[125]	train-mlogloss:1.92492	validation-mlogloss:1.93331
[126]	train-mlogloss:1.92163	validation-mlogloss:1.93020
[127]	train-mlogloss:1.91851	validation-mlogloss:1.92736
[128]	train-mlogloss:1.91537	validation-mlogloss:1.92444
[129]	train-mlogloss:1.91232	validation-mlogloss:1.92138
[130]	train-mlogloss:1.90916	validation-mlogloss:1.91839
[131]	train-mlogloss:1.90611	validation-mlogloss:1.91539
[132]	train-mlogloss:1.90293	validation-mlogloss:1.91242
[133]	train-mlogloss:1.89990	validation-mlogloss:1.90969
[134]	train-mlogloss:1.89694	validation-mlogloss:1.90660
[135]	train-mlogloss:1.89400	validation-mlogloss:1.90366
[136]	train-mlogloss:1.89099	validation-mlogloss:1.90069
[137]	train-mlogloss:1.88766	validation-mlogloss:1.89758
[138]	train-mlogloss:1.88476	validation-mlogloss:1.89474
[139]	train-mlogloss:1.88174	validation-mlogloss:1.89163
[140]	train-mlogloss:1.87871	validation-mlogloss:1.88893
[141]	train-mlogloss:1.87551	validation-mlogloss:1.88575
[142]	train-mlogloss:1.87248	validation-mlogloss:1.88289
[143]	train-mlogloss:1.86951	validation-mlogloss:1.88015
[144]	train-mlogloss:1.86664	validation-mlogloss:1.87740
[145]	train-mlogloss:1.86384	validation-mlogloss:1.87471
[146]	train-mlogloss:1.86098	validation-mlogloss:1.87218
[147]	train-mlogloss:1.85828	validation-mlogloss:1.86948
[148]	train-mlogloss:1.85558	validation-mlogloss:1.86692
[149]	train-mlogloss:1.85303	validation-mlogloss:1.86449
[150]	train-mlogloss:1.85020	validation-mlogloss:1.86174
[151]	train-mlogloss:1.84748	validation-mlogloss:1.85911
[152]	train-mlogloss:1.84459	validation-mlogloss:1.85631
[153]	train-mlogloss:1.84185	validation-mlogloss:1.85359
[154]	train-mlogloss:1.83905	validation-mlogloss:1.85112
[155]	train-mlogloss:1.83631	validation-mlogloss:1.84838
[156]	train-mlogloss:1.83367	validation-mlogloss:1.84615
[157]	train-mlogloss:1.83123	validation-mlogloss:1.84387
[158]	train-mlogloss:1.82865	validation-mlogloss:1.84136
[159]	train-mlogloss:1.82614	validation-mlogloss:1.83900
[160]	train-mlogloss:1.82356	validation-mlogloss:1.83657
[161]	train-mlogloss:1.82080	validation-mlogloss:1.83398
[162]	train-mlogloss:1.81821	validation-mlogloss:1.83170
[163]	train-mlogloss:1.81568	validation-mlogloss:1.82934
[164]	train-mlogloss:1.81310	validation-mlogloss:1.82683
[165]	train-mlogloss:1.81067	validation-mlogloss:1.82457
[166]	train-mlogloss:1.80816	validation-mlogloss:1.82221
[167]	train-mlogloss:1.80562	validation-mlogloss:1.81977
[168]	train-mlogloss:1.80327	validation-mlogloss:1.81751
[169]	train-mlogloss:1.80082	validation-mlogloss:1.81507
[170]	train-mlogloss:1.79852	validation-mlogloss:1.81291
[171]	train-mlogloss:1.79627	validation-mlogloss:1.81076
[172]	train-mlogloss:1.79404	validation-mlogloss:1.80861
[173]	train-mlogloss:1.79175	validation-mlogloss:1.80643
[174]	train-mlogloss:1.78953	validation-mlogloss:1.80433
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[176]	train-mlogloss:1.78492	validation-mlogloss:1.79996
[177]	train-mlogloss:1.78268	validation-mlogloss:1.79807
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[179]	train-mlogloss:1.77820	validation-mlogloss:1.79378
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[182]	train-mlogloss:1.77154	validation-mlogloss:1.78765
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[184]	train-mlogloss:1.76728	validation-mlogloss:1.78399
[185]	train-mlogloss:1.76513	validation-mlogloss:1.78192
[186]	train-mlogloss:1.76318	validation-mlogloss:1.78012
[187]	train-mlogloss:1.76112	validation-mlogloss:1.77804
[188]	train-mlogloss:1.75903	validation-mlogloss:1.77609
[189]	train-mlogloss:1.75689	validation-mlogloss:1.77410
[190]	train-mlogloss:1.75494	validation-mlogloss:1.77231
[191]	train-mlogloss:1.75290	validation-mlogloss:1.77034
[192]	train-mlogloss:1.75092	validation-mlogloss:1.76860
[193]	train-mlogloss:1.74896	validation-mlogloss:1.76667
[194]	train-mlogloss:1.74696	validation-mlogloss:1.76486
[195]	train-mlogloss:1.74504	validation-mlogloss:1.76302
[196]	train-mlogloss:1.74301	validation-mlogloss:1.76108
[197]	train-mlogloss:1.74102	validation-mlogloss:1.75923
[198]	train-mlogloss:1.73925	validation-mlogloss:1.75748
[199]	train-mlogloss:1.73733	validation-mlogloss:1.75560
[200]	train-mlogloss:1.73549	validation-mlogloss:1.75394
[201]	train-mlogloss:1.73357	validation-mlogloss:1.75237
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[600]	train-mlogloss:1.42161	validation-mlogloss:1.49087
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[603]	train-mlogloss:1.42067	validation-mlogloss:1.49032
[604]	train-mlogloss:1.42037	validation-mlogloss:1.49012
[605]	train-mlogloss:1.42007	validation-mlogloss:1.48987
[606]	train-mlogloss:1.41975	validation-mlogloss:1.48967
[607]	train-mlogloss:1.41949	validation-mlogloss:1.48950
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[610]	train-mlogloss:1.41845	validation-mlogloss:1.48888
[611]	train-mlogloss:1.41815	validation-mlogloss:1.48872
[612]	train-mlogloss:1.41783	validation-mlogloss:1.48850
[613]	train-mlogloss:1.41750	validation-mlogloss:1.48821
[614]	train-mlogloss:1.41719	validation-mlogloss:1.48803
[615]	train-mlogloss:1.41693	validation-mlogloss:1.48777
[616]	train-mlogloss:1.41660	validation-mlogloss:1.48766
[617]	train-mlogloss:1.41625	validation-mlogloss:1.48744
[618]	train-mlogloss:1.41593	validation-mlogloss:1.48731
[619]	train-mlogloss:1.41563	validation-mlogloss:1.48713
[620]	train-mlogloss:1.41531	validation-mlogloss:1.48696
[621]	train-mlogloss:1.41498	validation-mlogloss:1.48672
[622]	train-mlogloss:1.41469	validation-mlogloss:1.48650
[623]	train-mlogloss:1.41443	validation-mlogloss:1.48626
[624]	train-mlogloss:1.41410	validation-mlogloss:1.48598
[625]	train-mlogloss:1.41378	validation-mlogloss:1.48578
[626]	train-mlogloss:1.41352	validation-mlogloss:1.48563
[627]	train-mlogloss:1.41316	validation-mlogloss:1.48545
[628]	train-mlogloss:1.41283	validation-mlogloss:1.48531
Saved model to disk.
2025-11-07 10:19:49.567781: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:19:49.578926: 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 00:00:1762507189.591924 2379706 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507189.595965 2379706 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507189.605904 2379706 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507189.605925 2379706 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507189.605928 2379706 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507189.605930 2379706 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:19:49.608911: 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:2.68636	validation-mlogloss:2.68641
[2]	train-mlogloss:2.67550	validation-mlogloss:2.67573
[3]	train-mlogloss:2.66513	validation-mlogloss:2.66513
[4]	train-mlogloss:2.65489	validation-mlogloss:2.65483
[5]	train-mlogloss:2.64450	validation-mlogloss:2.64467
[6]	train-mlogloss:2.63463	validation-mlogloss:2.63458
[7]	train-mlogloss:2.62446	validation-mlogloss:2.62430
[8]	train-mlogloss:2.61459	validation-mlogloss:2.61439
[9]	train-mlogloss:2.60499	validation-mlogloss:2.60485
[10]	train-mlogloss:2.59493	validation-mlogloss:2.59455
[11]	train-mlogloss:2.58528	validation-mlogloss:2.58468
[12]	train-mlogloss:2.57588	validation-mlogloss:2.57535
[13]	train-mlogloss:2.56611	validation-mlogloss:2.56553
[14]	train-mlogloss:2.55697	validation-mlogloss:2.55620
[15]	train-mlogloss:2.54752	validation-mlogloss:2.54665
[16]	train-mlogloss:2.53873	validation-mlogloss:2.53771
[17]	train-mlogloss:2.52948	validation-mlogloss:2.52823
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[19]	train-mlogloss:2.51103	validation-mlogloss:2.50987
[20]	train-mlogloss:2.50206	validation-mlogloss:2.50112
[21]	train-mlogloss:2.49268	validation-mlogloss:2.49185
[22]	train-mlogloss:2.48402	validation-mlogloss:2.48319
[23]	train-mlogloss:2.47495	validation-mlogloss:2.47427
[24]	train-mlogloss:2.46633	validation-mlogloss:2.46581
[25]	train-mlogloss:2.45770	validation-mlogloss:2.45706
[26]	train-mlogloss:2.44912	validation-mlogloss:2.44830
[27]	train-mlogloss:2.44062	validation-mlogloss:2.43981
[28]	train-mlogloss:2.43239	validation-mlogloss:2.43157
[29]	train-mlogloss:2.42412	validation-mlogloss:2.42334
[30]	train-mlogloss:2.41588	validation-mlogloss:2.41506
[31]	train-mlogloss:2.40800	validation-mlogloss:2.40725
[32]	train-mlogloss:2.40004	validation-mlogloss:2.39932
[33]	train-mlogloss:2.39231	validation-mlogloss:2.39172
[34]	train-mlogloss:2.38453	validation-mlogloss:2.38388
[35]	train-mlogloss:2.37675	validation-mlogloss:2.37614
[36]	train-mlogloss:2.36937	validation-mlogloss:2.36905
[37]	train-mlogloss:2.36178	validation-mlogloss:2.36166
[38]	train-mlogloss:2.35445	validation-mlogloss:2.35428
[39]	train-mlogloss:2.34691	validation-mlogloss:2.34664
[40]	train-mlogloss:2.33946	validation-mlogloss:2.33906
[41]	train-mlogloss:2.33207	validation-mlogloss:2.33195
[42]	train-mlogloss:2.32512	validation-mlogloss:2.32485
[43]	train-mlogloss:2.31828	validation-mlogloss:2.31779
[44]	train-mlogloss:2.31137	validation-mlogloss:2.31082
[45]	train-mlogloss:2.30429	validation-mlogloss:2.30391
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[47]	train-mlogloss:2.29093	validation-mlogloss:2.29082
[48]	train-mlogloss:2.28420	validation-mlogloss:2.28412
[49]	train-mlogloss:2.27764	validation-mlogloss:2.27754
[50]	train-mlogloss:2.27125	validation-mlogloss:2.27115
[51]	train-mlogloss:2.26468	validation-mlogloss:2.26454
[52]	train-mlogloss:2.25792	validation-mlogloss:2.25796
[53]	train-mlogloss:2.25154	validation-mlogloss:2.25167
[54]	train-mlogloss:2.24520	validation-mlogloss:2.24552
[55]	train-mlogloss:2.23902	validation-mlogloss:2.23939
[56]	train-mlogloss:2.23287	validation-mlogloss:2.23325
[57]	train-mlogloss:2.22665	validation-mlogloss:2.22720
[58]	train-mlogloss:2.22055	validation-mlogloss:2.22114
[59]	train-mlogloss:2.21442	validation-mlogloss:2.21511
[60]	train-mlogloss:2.20849	validation-mlogloss:2.20949
[61]	train-mlogloss:2.20258	validation-mlogloss:2.20374
[62]	train-mlogloss:2.19659	validation-mlogloss:2.19770
[63]	train-mlogloss:2.19072	validation-mlogloss:2.19196
[64]	train-mlogloss:2.18490	validation-mlogloss:2.18647
[65]	train-mlogloss:2.17930	validation-mlogloss:2.18085
[66]	train-mlogloss:2.17380	validation-mlogloss:2.17542
[67]	train-mlogloss:2.16812	validation-mlogloss:2.16991
[68]	train-mlogloss:2.16268	validation-mlogloss:2.16438
[69]	train-mlogloss:2.15732	validation-mlogloss:2.15915
[70]	train-mlogloss:2.15205	validation-mlogloss:2.15414
[71]	train-mlogloss:2.14672	validation-mlogloss:2.14903
[72]	train-mlogloss:2.14153	validation-mlogloss:2.14416
[73]	train-mlogloss:2.13623	validation-mlogloss:2.13897
[74]	train-mlogloss:2.13118	validation-mlogloss:2.13393
[75]	train-mlogloss:2.12610	validation-mlogloss:2.12900
[76]	train-mlogloss:2.12095	validation-mlogloss:2.12423
[77]	train-mlogloss:2.11588	validation-mlogloss:2.11912
[78]	train-mlogloss:2.11097	validation-mlogloss:2.11416
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[81]	train-mlogloss:2.09619	validation-mlogloss:2.10044
[82]	train-mlogloss:2.09156	validation-mlogloss:2.09590
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[85]	train-mlogloss:2.07708	validation-mlogloss:2.08188
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[88]	train-mlogloss:2.06349	validation-mlogloss:2.06879
[89]	train-mlogloss:2.05903	validation-mlogloss:2.06443
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[95]	train-mlogloss:2.03336	validation-mlogloss:2.04020
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[101]	train-mlogloss:2.00875	validation-mlogloss:2.01650
[102]	train-mlogloss:2.00476	validation-mlogloss:2.01271
[103]	train-mlogloss:2.00086	validation-mlogloss:2.00899
[104]	train-mlogloss:1.99685	validation-mlogloss:2.00509
[105]	train-mlogloss:1.99301	validation-mlogloss:2.00151
[106]	train-mlogloss:1.98916	validation-mlogloss:1.99803
[107]	train-mlogloss:1.98535	validation-mlogloss:1.99455
[108]	train-mlogloss:1.98164	validation-mlogloss:1.99086
[109]	train-mlogloss:1.97804	validation-mlogloss:1.98735
[110]	train-mlogloss:1.97448	validation-mlogloss:1.98402
[111]	train-mlogloss:1.97076	validation-mlogloss:1.98033
[112]	train-mlogloss:1.96729	validation-mlogloss:1.97708
[113]	train-mlogloss:1.96362	validation-mlogloss:1.97370
[114]	train-mlogloss:1.95994	validation-mlogloss:1.97039
[115]	train-mlogloss:1.95633	validation-mlogloss:1.96677
[116]	train-mlogloss:1.95288	validation-mlogloss:1.96328
[117]	train-mlogloss:1.94936	validation-mlogloss:1.96012
[118]	train-mlogloss:1.94606	validation-mlogloss:1.95703
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[122]	train-mlogloss:1.93257	validation-mlogloss:1.94404
[123]	train-mlogloss:1.92928	validation-mlogloss:1.94095
[124]	train-mlogloss:1.92613	validation-mlogloss:1.93804
[125]	train-mlogloss:1.92278	validation-mlogloss:1.93485
[126]	train-mlogloss:1.91966	validation-mlogloss:1.93183
[127]	train-mlogloss:1.91635	validation-mlogloss:1.92868
[128]	train-mlogloss:1.91308	validation-mlogloss:1.92571
[129]	train-mlogloss:1.91004	validation-mlogloss:1.92279
[130]	train-mlogloss:1.90691	validation-mlogloss:1.91992
[131]	train-mlogloss:1.90381	validation-mlogloss:1.91705
[132]	train-mlogloss:1.90077	validation-mlogloss:1.91420
[133]	train-mlogloss:1.89776	validation-mlogloss:1.91119
[134]	train-mlogloss:1.89456	validation-mlogloss:1.90798
[135]	train-mlogloss:1.89120	validation-mlogloss:1.90500
[136]	train-mlogloss:1.88831	validation-mlogloss:1.90227
[137]	train-mlogloss:1.88520	validation-mlogloss:1.89937
[138]	train-mlogloss:1.88231	validation-mlogloss:1.89666
[139]	train-mlogloss:1.87949	validation-mlogloss:1.89423
[140]	train-mlogloss:1.87651	validation-mlogloss:1.89155
[141]	train-mlogloss:1.87371	validation-mlogloss:1.88866
[142]	train-mlogloss:1.87090	validation-mlogloss:1.88617
[143]	train-mlogloss:1.86810	validation-mlogloss:1.88351
[144]	train-mlogloss:1.86536	validation-mlogloss:1.88111
[145]	train-mlogloss:1.86240	validation-mlogloss:1.87839
[146]	train-mlogloss:1.85953	validation-mlogloss:1.87562
[147]	train-mlogloss:1.85684	validation-mlogloss:1.87304
[148]	train-mlogloss:1.85415	validation-mlogloss:1.87062
[149]	train-mlogloss:1.85152	validation-mlogloss:1.86806
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[559]	train-mlogloss:1.43409	validation-mlogloss:1.50122
[560]	train-mlogloss:1.43367	validation-mlogloss:1.50099
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[564]	train-mlogloss:1.43217	validation-mlogloss:1.49998
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[579]	train-mlogloss:1.42681	validation-mlogloss:1.49667
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[595]	train-mlogloss:1.42139	validation-mlogloss:1.49307
[596]	train-mlogloss:1.42105	validation-mlogloss:1.49284
[597]	train-mlogloss:1.42074	validation-mlogloss:1.49265
[598]	train-mlogloss:1.42043	validation-mlogloss:1.49245
[599]	train-mlogloss:1.42009	validation-mlogloss:1.49227
[600]	train-mlogloss:1.41980	validation-mlogloss:1.49207
[601]	train-mlogloss:1.41952	validation-mlogloss:1.49189
[602]	train-mlogloss:1.41919	validation-mlogloss:1.49169
[603]	train-mlogloss:1.41891	validation-mlogloss:1.49149
[604]	train-mlogloss:1.41858	validation-mlogloss:1.49120
[605]	train-mlogloss:1.41835	validation-mlogloss:1.49104
[606]	train-mlogloss:1.41801	validation-mlogloss:1.49079
[607]	train-mlogloss:1.41767	validation-mlogloss:1.49059
[608]	train-mlogloss:1.41732	validation-mlogloss:1.49045
[609]	train-mlogloss:1.41697	validation-mlogloss:1.49021
[610]	train-mlogloss:1.41668	validation-mlogloss:1.49002
[611]	train-mlogloss:1.41636	validation-mlogloss:1.48989
[612]	train-mlogloss:1.41600	validation-mlogloss:1.48972
[613]	train-mlogloss:1.41571	validation-mlogloss:1.48953
[614]	train-mlogloss:1.41537	validation-mlogloss:1.48926
[615]	train-mlogloss:1.41507	validation-mlogloss:1.48899
[616]	train-mlogloss:1.41474	validation-mlogloss:1.48885
[617]	train-mlogloss:1.41446	validation-mlogloss:1.48874
[618]	train-mlogloss:1.41418	validation-mlogloss:1.48852
[619]	train-mlogloss:1.41389	validation-mlogloss:1.48839
[620]	train-mlogloss:1.41360	validation-mlogloss:1.48819
[621]	train-mlogloss:1.41328	validation-mlogloss:1.48801
[622]	train-mlogloss:1.41293	validation-mlogloss:1.48784
[623]	train-mlogloss:1.41264	validation-mlogloss:1.48777
[624]	train-mlogloss:1.41234	validation-mlogloss:1.48761
[625]	train-mlogloss:1.41206	validation-mlogloss:1.48749
[626]	train-mlogloss:1.41172	validation-mlogloss:1.48728
[627]	train-mlogloss:1.41141	validation-mlogloss:1.48712
[628]	train-mlogloss:1.41109	validation-mlogloss:1.48691
Saved model to disk.
2025-11-07 10:20:15.477511: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:20:15.488649: 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 00:00:1762507215.502020 2380129 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507215.506352 2380129 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507215.516796 2380129 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507215.516819 2380129 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507215.516821 2380129 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507215.516823 2380129 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:20:15.520091: 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:2.69735	validation-mlogloss:2.69732
[1]	train-mlogloss:2.68738	validation-mlogloss:2.68708
[2]	train-mlogloss:2.67671	validation-mlogloss:2.67621
[3]	train-mlogloss:2.66594	validation-mlogloss:2.66551
[4]	train-mlogloss:2.65562	validation-mlogloss:2.65511
[5]	train-mlogloss:2.64535	validation-mlogloss:2.64505
[6]	train-mlogloss:2.63542	validation-mlogloss:2.63488
[7]	train-mlogloss:2.62546	validation-mlogloss:2.62481
[8]	train-mlogloss:2.61510	validation-mlogloss:2.61457
[9]	train-mlogloss:2.60538	validation-mlogloss:2.60490
[10]	train-mlogloss:2.59503	validation-mlogloss:2.59463
[11]	train-mlogloss:2.58538	validation-mlogloss:2.58473
[12]	train-mlogloss:2.57520	validation-mlogloss:2.57476
[13]	train-mlogloss:2.56575	validation-mlogloss:2.56520
[14]	train-mlogloss:2.55580	validation-mlogloss:2.55510
[15]	train-mlogloss:2.54693	validation-mlogloss:2.54615
[16]	train-mlogloss:2.53749	validation-mlogloss:2.53681
[17]	train-mlogloss:2.52810	validation-mlogloss:2.52741
[18]	train-mlogloss:2.51926	validation-mlogloss:2.51851
[19]	train-mlogloss:2.50990	validation-mlogloss:2.50916
[20]	train-mlogloss:2.50107	validation-mlogloss:2.50021
[21]	train-mlogloss:2.49217	validation-mlogloss:2.49112
[22]	train-mlogloss:2.48360	validation-mlogloss:2.48242
[23]	train-mlogloss:2.47453	validation-mlogloss:2.47346
[24]	train-mlogloss:2.46591	validation-mlogloss:2.46482
[25]	train-mlogloss:2.45765	validation-mlogloss:2.45646
[26]	train-mlogloss:2.44910	validation-mlogloss:2.44777
[27]	train-mlogloss:2.44087	validation-mlogloss:2.43951
[28]	train-mlogloss:2.43275	validation-mlogloss:2.43140
[29]	train-mlogloss:2.42466	validation-mlogloss:2.42316
[30]	train-mlogloss:2.41653	validation-mlogloss:2.41525
[31]	train-mlogloss:2.40852	validation-mlogloss:2.40733
[32]	train-mlogloss:2.40050	validation-mlogloss:2.39919
[33]	train-mlogloss:2.39299	validation-mlogloss:2.39161
[34]	train-mlogloss:2.38525	validation-mlogloss:2.38407
[35]	train-mlogloss:2.37766	validation-mlogloss:2.37652
[36]	train-mlogloss:2.37016	validation-mlogloss:2.36903
[37]	train-mlogloss:2.36265	validation-mlogloss:2.36187
[38]	train-mlogloss:2.35510	validation-mlogloss:2.35408
[39]	train-mlogloss:2.34770	validation-mlogloss:2.34665
[40]	train-mlogloss:2.34056	validation-mlogloss:2.33948
[41]	train-mlogloss:2.33347	validation-mlogloss:2.33229
[42]	train-mlogloss:2.32670	validation-mlogloss:2.32555
[43]	train-mlogloss:2.31988	validation-mlogloss:2.31869
[44]	train-mlogloss:2.31314	validation-mlogloss:2.31219
[45]	train-mlogloss:2.30611	validation-mlogloss:2.30530
[46]	train-mlogloss:2.29904	validation-mlogloss:2.29826
[47]	train-mlogloss:2.29218	validation-mlogloss:2.29148
[48]	train-mlogloss:2.28559	validation-mlogloss:2.28491
[49]	train-mlogloss:2.27881	validation-mlogloss:2.27825
[50]	train-mlogloss:2.27253	validation-mlogloss:2.27192
[51]	train-mlogloss:2.26603	validation-mlogloss:2.26545
[52]	train-mlogloss:2.25949	validation-mlogloss:2.25893
[53]	train-mlogloss:2.25323	validation-mlogloss:2.25258
[54]	train-mlogloss:2.24709	validation-mlogloss:2.24643
[55]	train-mlogloss:2.24103	validation-mlogloss:2.24053
[56]	train-mlogloss:2.23485	validation-mlogloss:2.23440
[57]	train-mlogloss:2.22893	validation-mlogloss:2.22853
[58]	train-mlogloss:2.22286	validation-mlogloss:2.22261
[59]	train-mlogloss:2.21671	validation-mlogloss:2.21637
[60]	train-mlogloss:2.21064	validation-mlogloss:2.21041
[61]	train-mlogloss:2.20469	validation-mlogloss:2.20452
[62]	train-mlogloss:2.19878	validation-mlogloss:2.19883
[63]	train-mlogloss:2.19295	validation-mlogloss:2.19320
[64]	train-mlogloss:2.18731	validation-mlogloss:2.18759
[65]	train-mlogloss:2.18179	validation-mlogloss:2.18206
[66]	train-mlogloss:2.17599	validation-mlogloss:2.17673
[67]	train-mlogloss:2.17079	validation-mlogloss:2.17179
[68]	train-mlogloss:2.16510	validation-mlogloss:2.16638
[69]	train-mlogloss:2.15961	validation-mlogloss:2.16090
[70]	train-mlogloss:2.15431	validation-mlogloss:2.15562
[71]	train-mlogloss:2.14908	validation-mlogloss:2.15040
[72]	train-mlogloss:2.14377	validation-mlogloss:2.14513
[73]	train-mlogloss:2.13843	validation-mlogloss:2.13985
[74]	train-mlogloss:2.13296	validation-mlogloss:2.13474
[75]	train-mlogloss:2.12783	validation-mlogloss:2.12963
[76]	train-mlogloss:2.12278	validation-mlogloss:2.12464
[77]	train-mlogloss:2.11773	validation-mlogloss:2.11974
[78]	train-mlogloss:2.11290	validation-mlogloss:2.11508
[79]	train-mlogloss:2.10792	validation-mlogloss:2.11014
[80]	train-mlogloss:2.10294	validation-mlogloss:2.10548
[81]	train-mlogloss:2.09795	validation-mlogloss:2.10058
[82]	train-mlogloss:2.09320	validation-mlogloss:2.09607
[83]	train-mlogloss:2.08848	validation-mlogloss:2.09150
[84]	train-mlogloss:2.08389	validation-mlogloss:2.08694
[85]	train-mlogloss:2.07910	validation-mlogloss:2.08239
[86]	train-mlogloss:2.07440	validation-mlogloss:2.07780
[87]	train-mlogloss:2.06995	validation-mlogloss:2.07365
[88]	train-mlogloss:2.06553	validation-mlogloss:2.06925
[89]	train-mlogloss:2.06080	validation-mlogloss:2.06488
[90]	train-mlogloss:2.05641	validation-mlogloss:2.06060
[91]	train-mlogloss:2.05184	validation-mlogloss:2.05610
[92]	train-mlogloss:2.04741	validation-mlogloss:2.05213
[93]	train-mlogloss:2.04314	validation-mlogloss:2.04812
[94]	train-mlogloss:2.03885	validation-mlogloss:2.04400
[95]	train-mlogloss:2.03457	validation-mlogloss:2.03994
[96]	train-mlogloss:2.03030	validation-mlogloss:2.03574
[97]	train-mlogloss:2.02609	validation-mlogloss:2.03168
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[398]	train-mlogloss:1.51332	validation-mlogloss:1.55914
[399]	train-mlogloss:1.51277	validation-mlogloss:1.55870
[400]	train-mlogloss:1.51217	validation-mlogloss:1.55831
[401]	train-mlogloss:1.51154	validation-mlogloss:1.55782
[402]	train-mlogloss:1.51093	validation-mlogloss:1.55730
[403]	train-mlogloss:1.51032	validation-mlogloss:1.55681
[404]	train-mlogloss:1.50969	validation-mlogloss:1.55628
[405]	train-mlogloss:1.50905	validation-mlogloss:1.55580
[406]	train-mlogloss:1.50839	validation-mlogloss:1.55533
[407]	train-mlogloss:1.50777	validation-mlogloss:1.55482
[408]	train-mlogloss:1.50719	validation-mlogloss:1.55443
[409]	train-mlogloss:1.50657	validation-mlogloss:1.55386
[410]	train-mlogloss:1.50593	validation-mlogloss:1.55342
[411]	train-mlogloss:1.50536	validation-mlogloss:1.55287
[412]	train-mlogloss:1.50476	validation-mlogloss:1.55241
[413]	train-mlogloss:1.50419	validation-mlogloss:1.55201
[414]	train-mlogloss:1.50354	validation-mlogloss:1.55156
[415]	train-mlogloss:1.50290	validation-mlogloss:1.55093
[416]	train-mlogloss:1.50230	validation-mlogloss:1.55047
[417]	train-mlogloss:1.50172	validation-mlogloss:1.54999
[418]	train-mlogloss:1.50115	validation-mlogloss:1.54932
[419]	train-mlogloss:1.50054	validation-mlogloss:1.54876
[420]	train-mlogloss:1.49995	validation-mlogloss:1.54840
[421]	train-mlogloss:1.49933	validation-mlogloss:1.54792
[422]	train-mlogloss:1.49875	validation-mlogloss:1.54744
[423]	train-mlogloss:1.49817	validation-mlogloss:1.54708
[424]	train-mlogloss:1.49761	validation-mlogloss:1.54660
[425]	train-mlogloss:1.49701	validation-mlogloss:1.54622
[426]	train-mlogloss:1.49645	validation-mlogloss:1.54580
[427]	train-mlogloss:1.49585	validation-mlogloss:1.54531
[428]	train-mlogloss:1.49526	validation-mlogloss:1.54482
[429]	train-mlogloss:1.49468	validation-mlogloss:1.54436
[430]	train-mlogloss:1.49411	validation-mlogloss:1.54397
[431]	train-mlogloss:1.49356	validation-mlogloss:1.54343
[432]	train-mlogloss:1.49301	validation-mlogloss:1.54299
[433]	train-mlogloss:1.49247	validation-mlogloss:1.54249
[434]	train-mlogloss:1.49186	validation-mlogloss:1.54212
[435]	train-mlogloss:1.49129	validation-mlogloss:1.54167
[436]	train-mlogloss:1.49077	validation-mlogloss:1.54123
[437]	train-mlogloss:1.49021	validation-mlogloss:1.54076
[438]	train-mlogloss:1.48964	validation-mlogloss:1.54028
[439]	train-mlogloss:1.48907	validation-mlogloss:1.53976
[440]	train-mlogloss:1.48852	validation-mlogloss:1.53933
[441]	train-mlogloss:1.48792	validation-mlogloss:1.53885
[442]	train-mlogloss:1.48739	validation-mlogloss:1.53843
[443]	train-mlogloss:1.48683	validation-mlogloss:1.53795
[444]	train-mlogloss:1.48632	validation-mlogloss:1.53749
[445]	train-mlogloss:1.48574	validation-mlogloss:1.53692
[446]	train-mlogloss:1.48520	validation-mlogloss:1.53660
[447]	train-mlogloss:1.48464	validation-mlogloss:1.53620
[448]	train-mlogloss:1.48407	validation-mlogloss:1.53587
[449]	train-mlogloss:1.48360	validation-mlogloss:1.53543
[450]	train-mlogloss:1.48314	validation-mlogloss:1.53507
[451]	train-mlogloss:1.48264	validation-mlogloss:1.53461
[452]	train-mlogloss:1.48210	validation-mlogloss:1.53411
[453]	train-mlogloss:1.48155	validation-mlogloss:1.53378
[454]	train-mlogloss:1.48099	validation-mlogloss:1.53339
[455]	train-mlogloss:1.48044	validation-mlogloss:1.53298
[456]	train-mlogloss:1.47992	validation-mlogloss:1.53260
[457]	train-mlogloss:1.47941	validation-mlogloss:1.53232
[458]	train-mlogloss:1.47891	validation-mlogloss:1.53188
[459]	train-mlogloss:1.47843	validation-mlogloss:1.53154
[460]	train-mlogloss:1.47793	validation-mlogloss:1.53111
[461]	train-mlogloss:1.47741	validation-mlogloss:1.53070
[462]	train-mlogloss:1.47694	validation-mlogloss:1.53036
[463]	train-mlogloss:1.47647	validation-mlogloss:1.52999
[464]	train-mlogloss:1.47603	validation-mlogloss:1.52969
[465]	train-mlogloss:1.47553	validation-mlogloss:1.52931
[466]	train-mlogloss:1.47502	validation-mlogloss:1.52885
[467]	train-mlogloss:1.47455	validation-mlogloss:1.52856
[468]	train-mlogloss:1.47404	validation-mlogloss:1.52834
[469]	train-mlogloss:1.47350	validation-mlogloss:1.52789
[470]	train-mlogloss:1.47302	validation-mlogloss:1.52755
[471]	train-mlogloss:1.47252	validation-mlogloss:1.52714
[472]	train-mlogloss:1.47209	validation-mlogloss:1.52680
[473]	train-mlogloss:1.47158	validation-mlogloss:1.52630
[474]	train-mlogloss:1.47107	validation-mlogloss:1.52589
[475]	train-mlogloss:1.47059	validation-mlogloss:1.52539
[476]	train-mlogloss:1.47016	validation-mlogloss:1.52505
[477]	train-mlogloss:1.46964	validation-mlogloss:1.52461
[478]	train-mlogloss:1.46920	validation-mlogloss:1.52424
[479]	train-mlogloss:1.46865	validation-mlogloss:1.52399
[480]	train-mlogloss:1.46824	validation-mlogloss:1.52375
[481]	train-mlogloss:1.46775	validation-mlogloss:1.52338
[482]	train-mlogloss:1.46732	validation-mlogloss:1.52298
[483]	train-mlogloss:1.46680	validation-mlogloss:1.52270
[484]	train-mlogloss:1.46632	validation-mlogloss:1.52244
[485]	train-mlogloss:1.46578	validation-mlogloss:1.52212
[486]	train-mlogloss:1.46535	validation-mlogloss:1.52183
[487]	train-mlogloss:1.46487	validation-mlogloss:1.52154
[488]	train-mlogloss:1.46439	validation-mlogloss:1.52124
[489]	train-mlogloss:1.46392	validation-mlogloss:1.52080
[490]	train-mlogloss:1.46346	validation-mlogloss:1.52038
[491]	train-mlogloss:1.46299	validation-mlogloss:1.52010
[492]	train-mlogloss:1.46258	validation-mlogloss:1.51983
[493]	train-mlogloss:1.46211	validation-mlogloss:1.51959
[494]	train-mlogloss:1.46165	validation-mlogloss:1.51934
[495]	train-mlogloss:1.46126	validation-mlogloss:1.51907
[496]	train-mlogloss:1.46078	validation-mlogloss:1.51878
[497]	train-mlogloss:1.46029	validation-mlogloss:1.51851
[498]	train-mlogloss:1.45984	validation-mlogloss:1.51830
[499]	train-mlogloss:1.45937	validation-mlogloss:1.51808
[500]	train-mlogloss:1.45891	validation-mlogloss:1.51768
[501]	train-mlogloss:1.45855	validation-mlogloss:1.51745
[502]	train-mlogloss:1.45811	validation-mlogloss:1.51729
[503]	train-mlogloss:1.45765	validation-mlogloss:1.51695
[504]	train-mlogloss:1.45718	validation-mlogloss:1.51660
[505]	train-mlogloss:1.45675	validation-mlogloss:1.51631
[506]	train-mlogloss:1.45631	validation-mlogloss:1.51602
[507]	train-mlogloss:1.45592	validation-mlogloss:1.51571
[508]	train-mlogloss:1.45549	validation-mlogloss:1.51546
[509]	train-mlogloss:1.45499	validation-mlogloss:1.51501
[510]	train-mlogloss:1.45462	validation-mlogloss:1.51473
[511]	train-mlogloss:1.45417	validation-mlogloss:1.51435
[512]	train-mlogloss:1.45372	validation-mlogloss:1.51402
[513]	train-mlogloss:1.45328	validation-mlogloss:1.51366
[514]	train-mlogloss:1.45286	validation-mlogloss:1.51327
[515]	train-mlogloss:1.45244	validation-mlogloss:1.51299
[516]	train-mlogloss:1.45196	validation-mlogloss:1.51268
[517]	train-mlogloss:1.45154	validation-mlogloss:1.51236
[518]	train-mlogloss:1.45111	validation-mlogloss:1.51212
[519]	train-mlogloss:1.45076	validation-mlogloss:1.51193
[520]	train-mlogloss:1.45035	validation-mlogloss:1.51162
[521]	train-mlogloss:1.44996	validation-mlogloss:1.51137
[522]	train-mlogloss:1.44956	validation-mlogloss:1.51111
[523]	train-mlogloss:1.44916	validation-mlogloss:1.51095
[524]	train-mlogloss:1.44874	validation-mlogloss:1.51068
[525]	train-mlogloss:1.44830	validation-mlogloss:1.51041
[526]	train-mlogloss:1.44791	validation-mlogloss:1.51007
[527]	train-mlogloss:1.44749	validation-mlogloss:1.50964
[528]	train-mlogloss:1.44706	validation-mlogloss:1.50930
[529]	train-mlogloss:1.44664	validation-mlogloss:1.50899
[530]	train-mlogloss:1.44623	validation-mlogloss:1.50869
[531]	train-mlogloss:1.44583	validation-mlogloss:1.50847
[532]	train-mlogloss:1.44548	validation-mlogloss:1.50811
[533]	train-mlogloss:1.44509	validation-mlogloss:1.50779
[534]	train-mlogloss:1.44475	validation-mlogloss:1.50747
[535]	train-mlogloss:1.44433	validation-mlogloss:1.50726
[536]	train-mlogloss:1.44395	validation-mlogloss:1.50700
[537]	train-mlogloss:1.44360	validation-mlogloss:1.50676
[538]	train-mlogloss:1.44316	validation-mlogloss:1.50646
[539]	train-mlogloss:1.44275	validation-mlogloss:1.50624
[540]	train-mlogloss:1.44238	validation-mlogloss:1.50596
[541]	train-mlogloss:1.44204	validation-mlogloss:1.50565
[542]	train-mlogloss:1.44165	validation-mlogloss:1.50535
[543]	train-mlogloss:1.44127	validation-mlogloss:1.50509
[544]	train-mlogloss:1.44085	validation-mlogloss:1.50479
[545]	train-mlogloss:1.44047	validation-mlogloss:1.50465
[546]	train-mlogloss:1.44006	validation-mlogloss:1.50450
[547]	train-mlogloss:1.43972	validation-mlogloss:1.50429
[548]	train-mlogloss:1.43931	validation-mlogloss:1.50406
[549]	train-mlogloss:1.43895	validation-mlogloss:1.50385
[550]	train-mlogloss:1.43853	validation-mlogloss:1.50362
[551]	train-mlogloss:1.43813	validation-mlogloss:1.50345
[552]	train-mlogloss:1.43777	validation-mlogloss:1.50325
[553]	train-mlogloss:1.43745	validation-mlogloss:1.50305
[554]	train-mlogloss:1.43711	validation-mlogloss:1.50277
[555]	train-mlogloss:1.43673	validation-mlogloss:1.50253
[556]	train-mlogloss:1.43631	validation-mlogloss:1.50216
[557]	train-mlogloss:1.43596	validation-mlogloss:1.50195
[558]	train-mlogloss:1.43559	validation-mlogloss:1.50171
[559]	train-mlogloss:1.43520	validation-mlogloss:1.50145
[560]	train-mlogloss:1.43482	validation-mlogloss:1.50125
[561]	train-mlogloss:1.43446	validation-mlogloss:1.50099
[562]	train-mlogloss:1.43409	validation-mlogloss:1.50074
[563]	train-mlogloss:1.43368	validation-mlogloss:1.50052
[564]	train-mlogloss:1.43329	validation-mlogloss:1.50037
[565]	train-mlogloss:1.43291	validation-mlogloss:1.50009
[566]	train-mlogloss:1.43254	validation-mlogloss:1.49984
[567]	train-mlogloss:1.43221	validation-mlogloss:1.49969
[568]	train-mlogloss:1.43187	validation-mlogloss:1.49948
[569]	train-mlogloss:1.43155	validation-mlogloss:1.49926
[570]	train-mlogloss:1.43121	validation-mlogloss:1.49901
[571]	train-mlogloss:1.43086	validation-mlogloss:1.49883
[572]	train-mlogloss:1.43051	validation-mlogloss:1.49869
[573]	train-mlogloss:1.43020	validation-mlogloss:1.49845
[574]	train-mlogloss:1.42980	validation-mlogloss:1.49820
[575]	train-mlogloss:1.42943	validation-mlogloss:1.49804
[576]	train-mlogloss:1.42910	validation-mlogloss:1.49788
[577]	train-mlogloss:1.42874	validation-mlogloss:1.49769
[578]	train-mlogloss:1.42837	validation-mlogloss:1.49745
[579]	train-mlogloss:1.42801	validation-mlogloss:1.49724
[580]	train-mlogloss:1.42767	validation-mlogloss:1.49704
[581]	train-mlogloss:1.42734	validation-mlogloss:1.49680
[582]	train-mlogloss:1.42700	validation-mlogloss:1.49667
[583]	train-mlogloss:1.42666	validation-mlogloss:1.49658
[584]	train-mlogloss:1.42634	validation-mlogloss:1.49639
[585]	train-mlogloss:1.42596	validation-mlogloss:1.49628
[586]	train-mlogloss:1.42562	validation-mlogloss:1.49607
[587]	train-mlogloss:1.42525	validation-mlogloss:1.49586
[588]	train-mlogloss:1.42488	validation-mlogloss:1.49574
[589]	train-mlogloss:1.42455	validation-mlogloss:1.49557
[590]	train-mlogloss:1.42418	validation-mlogloss:1.49543
[591]	train-mlogloss:1.42390	validation-mlogloss:1.49525
[592]	train-mlogloss:1.42356	validation-mlogloss:1.49499
[593]	train-mlogloss:1.42318	validation-mlogloss:1.49480
[594]	train-mlogloss:1.42288	validation-mlogloss:1.49468
[595]	train-mlogloss:1.42256	validation-mlogloss:1.49453
[596]	train-mlogloss:1.42227	validation-mlogloss:1.49435
[597]	train-mlogloss:1.42193	validation-mlogloss:1.49411
[598]	train-mlogloss:1.42157	validation-mlogloss:1.49389
[599]	train-mlogloss:1.42120	validation-mlogloss:1.49363
[600]	train-mlogloss:1.42093	validation-mlogloss:1.49347
[601]	train-mlogloss:1.42060	validation-mlogloss:1.49329
[602]	train-mlogloss:1.42029	validation-mlogloss:1.49308
[603]	train-mlogloss:1.41994	validation-mlogloss:1.49297
[604]	train-mlogloss:1.41962	validation-mlogloss:1.49288
[605]	train-mlogloss:1.41928	validation-mlogloss:1.49266
[606]	train-mlogloss:1.41894	validation-mlogloss:1.49257
[607]	train-mlogloss:1.41864	validation-mlogloss:1.49247
[608]	train-mlogloss:1.41831	validation-mlogloss:1.49222
[609]	train-mlogloss:1.41803	validation-mlogloss:1.49201
[610]	train-mlogloss:1.41767	validation-mlogloss:1.49178
[611]	train-mlogloss:1.41735	validation-mlogloss:1.49148
[612]	train-mlogloss:1.41700	validation-mlogloss:1.49130
[613]	train-mlogloss:1.41674	validation-mlogloss:1.49107
[614]	train-mlogloss:1.41647	validation-mlogloss:1.49093
[615]	train-mlogloss:1.41615	validation-mlogloss:1.49076
[616]	train-mlogloss:1.41579	validation-mlogloss:1.49068
[617]	train-mlogloss:1.41552	validation-mlogloss:1.49045
[618]	train-mlogloss:1.41522	validation-mlogloss:1.49024
[619]	train-mlogloss:1.41497	validation-mlogloss:1.49004
[620]	train-mlogloss:1.41466	validation-mlogloss:1.48978
[621]	train-mlogloss:1.41435	validation-mlogloss:1.48951
[622]	train-mlogloss:1.41406	validation-mlogloss:1.48927
[623]	train-mlogloss:1.41375	validation-mlogloss:1.48904
[624]	train-mlogloss:1.41348	validation-mlogloss:1.48886
[625]	train-mlogloss:1.41317	validation-mlogloss:1.48865
[626]	train-mlogloss:1.41285	validation-mlogloss:1.48854
[627]	train-mlogloss:1.41257	validation-mlogloss:1.48843
[628]	train-mlogloss:1.41226	validation-mlogloss:1.48829
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.98 [%]
Global accuracy score (test) = 43.66 [%]
Global F1 score (train) = 55.84 [%]
Global F1 score (test) = 42.74 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.21      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.39      0.34       184
       CAMINAR USUAL SPEED       0.25      0.14      0.17       184
            CAMINAR ZIGZAG       0.27      0.27      0.27       184
          DE PIE BARRIENDO       0.45      0.48      0.47       184
   DE PIE DOBLANDO TOALLAS       0.43      0.45      0.44       184
    DE PIE MOVIENDO LIBROS       0.42      0.39      0.40       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.41      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.29      0.24      0.26       184
         SENTADO USANDO PC       0.17      0.07      0.09       184
      SENTADO VIENDO LA TV       0.41      0.37      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.57      0.72      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 1: 43.66 [%]
F1-score capturado en la ejecución 1: 42.74 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.91 [%]
Global accuracy score (test) = 44.32 [%]
Global F1 score (train) = 55.76 [%]
Global F1 score (test) = 43.35 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.21      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.32      0.42      0.37       184
       CAMINAR USUAL SPEED       0.31      0.16      0.21       184
            CAMINAR ZIGZAG       0.30      0.30      0.30       184
          DE PIE BARRIENDO       0.47      0.51      0.49       184
   DE PIE DOBLANDO TOALLAS       0.43      0.41      0.42       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.30      0.27      0.29       184
         SENTADO USANDO PC       0.17      0.07      0.09       184
      SENTADO VIENDO LA TV       0.42      0.36      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.54      0.72      0.62       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.45      0.43      2737
              weighted avg       0.44      0.44      0.43      2737


Accuracy capturado en la ejecución 2: 44.32 [%]
F1-score capturado en la ejecución 2: 43.35 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.99 [%]
Global accuracy score (test) = 43.77 [%]
Global F1 score (train) = 55.81 [%]
Global F1 score (test) = 42.72 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.21      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.41      0.35       184
       CAMINAR USUAL SPEED       0.25      0.14      0.18       184
            CAMINAR ZIGZAG       0.30      0.28      0.29       184
          DE PIE BARRIENDO       0.47      0.49      0.48       184
   DE PIE DOBLANDO TOALLAS       0.42      0.42      0.42       184
    DE PIE MOVIENDO LIBROS       0.40      0.39      0.40       184
          DE PIE USANDO PC       0.54      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.28      0.26      0.27       184
         SENTADO USANDO PC       0.15      0.05      0.07       184
      SENTADO VIENDO LA TV       0.39      0.36      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.63       184
                    TROTAR       0.94      0.81      0.87       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 3: 43.77 [%]
F1-score capturado en la ejecución 3: 42.72 [%]

=== EJECUCIÓN 4 ===

--- TRAIN (ejecución 4) ---

--- TEST (ejecución 4) ---
2025-11-07 10:20:41.746726: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:20:41.758172: 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 00:00:1762507241.771676 2380546 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507241.775660 2380546 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507241.785534 2380546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507241.785553 2380546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507241.785555 2380546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507241.785556 2380546 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:20:41.788611: 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:2.69717	validation-mlogloss:2.69684
[1]	train-mlogloss:2.68667	validation-mlogloss:2.68628
[2]	train-mlogloss:2.67639	validation-mlogloss:2.67584
[3]	train-mlogloss:2.66663	validation-mlogloss:2.66579
[4]	train-mlogloss:2.65644	validation-mlogloss:2.65552
[5]	train-mlogloss:2.64618	validation-mlogloss:2.64507
[6]	train-mlogloss:2.63613	validation-mlogloss:2.63502
[7]	train-mlogloss:2.62639	validation-mlogloss:2.62517
[8]	train-mlogloss:2.61698	validation-mlogloss:2.61592
[9]	train-mlogloss:2.60713	validation-mlogloss:2.60604
[10]	train-mlogloss:2.59735	validation-mlogloss:2.59613
[11]	train-mlogloss:2.58722	validation-mlogloss:2.58604
[12]	train-mlogloss:2.57724	validation-mlogloss:2.57609
[13]	train-mlogloss:2.56781	validation-mlogloss:2.56682
[14]	train-mlogloss:2.55822	validation-mlogloss:2.55729
[15]	train-mlogloss:2.54889	validation-mlogloss:2.54784
[16]	train-mlogloss:2.54004	validation-mlogloss:2.53862
[17]	train-mlogloss:2.53072	validation-mlogloss:2.52925
[18]	train-mlogloss:2.52138	validation-mlogloss:2.51985
[19]	train-mlogloss:2.51258	validation-mlogloss:2.51121
[20]	train-mlogloss:2.50332	validation-mlogloss:2.50198
[21]	train-mlogloss:2.49436	validation-mlogloss:2.49298
[22]	train-mlogloss:2.48509	validation-mlogloss:2.48397
[23]	train-mlogloss:2.47618	validation-mlogloss:2.47512
[24]	train-mlogloss:2.46768	validation-mlogloss:2.46653
[25]	train-mlogloss:2.45871	validation-mlogloss:2.45752
[26]	train-mlogloss:2.44987	validation-mlogloss:2.44878
[27]	train-mlogloss:2.44181	validation-mlogloss:2.44052
[28]	train-mlogloss:2.43342	validation-mlogloss:2.43180
[29]	train-mlogloss:2.42515	validation-mlogloss:2.42343
[30]	train-mlogloss:2.41705	validation-mlogloss:2.41533
[31]	train-mlogloss:2.40912	validation-mlogloss:2.40727
[32]	train-mlogloss:2.40129	validation-mlogloss:2.39941
[33]	train-mlogloss:2.39343	validation-mlogloss:2.39153
[34]	train-mlogloss:2.38531	validation-mlogloss:2.38358
[35]	train-mlogloss:2.37784	validation-mlogloss:2.37619
[36]	train-mlogloss:2.37043	validation-mlogloss:2.36885
[37]	train-mlogloss:2.36276	validation-mlogloss:2.36118
[38]	train-mlogloss:2.35510	validation-mlogloss:2.35366
[39]	train-mlogloss:2.34768	validation-mlogloss:2.34612
[40]	train-mlogloss:2.34045	validation-mlogloss:2.33901
[41]	train-mlogloss:2.33329	validation-mlogloss:2.33168
[42]	train-mlogloss:2.32612	validation-mlogloss:2.32460
[43]	train-mlogloss:2.31905	validation-mlogloss:2.31761
[44]	train-mlogloss:2.31205	validation-mlogloss:2.31070
[45]	train-mlogloss:2.30508	validation-mlogloss:2.30385
[46]	train-mlogloss:2.29832	validation-mlogloss:2.29715
[47]	train-mlogloss:2.29151	validation-mlogloss:2.29037
[48]	train-mlogloss:2.28479	validation-mlogloss:2.28364
[49]	train-mlogloss:2.27826	validation-mlogloss:2.27712
[50]	train-mlogloss:2.27170	validation-mlogloss:2.27082
[51]	train-mlogloss:2.26516	validation-mlogloss:2.26415
[52]	train-mlogloss:2.25916	validation-mlogloss:2.25815
[53]	train-mlogloss:2.25307	validation-mlogloss:2.25207
[54]	train-mlogloss:2.24681	validation-mlogloss:2.24574
[55]	train-mlogloss:2.24043	validation-mlogloss:2.23935
[56]	train-mlogloss:2.23439	validation-mlogloss:2.23344
[57]	train-mlogloss:2.22828	validation-mlogloss:2.22741
[58]	train-mlogloss:2.22201	validation-mlogloss:2.22137
[59]	train-mlogloss:2.21633	validation-mlogloss:2.21569
[60]	train-mlogloss:2.21038	validation-mlogloss:2.20982
[61]	train-mlogloss:2.20435	validation-mlogloss:2.20390
[62]	train-mlogloss:2.19851	validation-mlogloss:2.19821
[63]	train-mlogloss:2.19272	validation-mlogloss:2.19260
[64]	train-mlogloss:2.18704	validation-mlogloss:2.18707
[65]	train-mlogloss:2.18130	validation-mlogloss:2.18142
[66]	train-mlogloss:2.17555	validation-mlogloss:2.17588
[67]	train-mlogloss:2.16986	validation-mlogloss:2.17069
[68]	train-mlogloss:2.16401	validation-mlogloss:2.16501
[69]	train-mlogloss:2.15858	validation-mlogloss:2.15993
[70]	train-mlogloss:2.15308	validation-mlogloss:2.15435
[71]	train-mlogloss:2.14771	validation-mlogloss:2.14885
[72]	train-mlogloss:2.14235	validation-mlogloss:2.14382
[73]	train-mlogloss:2.13726	validation-mlogloss:2.13888
[74]	train-mlogloss:2.13203	validation-mlogloss:2.13381
[75]	train-mlogloss:2.12682	validation-mlogloss:2.12879
[76]	train-mlogloss:2.12165	validation-mlogloss:2.12390
[77]	train-mlogloss:2.11663	validation-mlogloss:2.11929
[78]	train-mlogloss:2.11171	validation-mlogloss:2.11442
[79]	train-mlogloss:2.10677	validation-mlogloss:2.10964
[80]	train-mlogloss:2.10188	validation-mlogloss:2.10461
[81]	train-mlogloss:2.09737	validation-mlogloss:2.10015
[82]	train-mlogloss:2.09281	validation-mlogloss:2.09572
[83]	train-mlogloss:2.08800	validation-mlogloss:2.09101
[84]	train-mlogloss:2.08335	validation-mlogloss:2.08662
[85]	train-mlogloss:2.07869	validation-mlogloss:2.08222
[86]	train-mlogloss:2.07421	validation-mlogloss:2.07779
[87]	train-mlogloss:2.06992	validation-mlogloss:2.07366
[88]	train-mlogloss:2.06545	validation-mlogloss:2.06948
[89]	train-mlogloss:2.06104	validation-mlogloss:2.06523
[90]	train-mlogloss:2.05661	validation-mlogloss:2.06092
[91]	train-mlogloss:2.05242	validation-mlogloss:2.05694
[92]	train-mlogloss:2.04808	validation-mlogloss:2.05269
[93]	train-mlogloss:2.04385	validation-mlogloss:2.04853
[94]	train-mlogloss:2.03950	validation-mlogloss:2.04431
[95]	train-mlogloss:2.03515	validation-mlogloss:2.04037
[96]	train-mlogloss:2.03061	validation-mlogloss:2.03625
[97]	train-mlogloss:2.02632	validation-mlogloss:2.03225
[98]	train-mlogloss:2.02198	validation-mlogloss:2.02809
[99]	train-mlogloss:2.01812	validation-mlogloss:2.02446
[100]	train-mlogloss:2.01400	validation-mlogloss:2.02053
[101]	train-mlogloss:2.01002	validation-mlogloss:2.01678
[102]	train-mlogloss:2.00614	validation-mlogloss:2.01306
[103]	train-mlogloss:2.00230	validation-mlogloss:2.00943
[104]	train-mlogloss:1.99837	validation-mlogloss:2.00554
[105]	train-mlogloss:1.99458	validation-mlogloss:2.00189
[106]	train-mlogloss:1.99065	validation-mlogloss:1.99801
[107]	train-mlogloss:1.98680	validation-mlogloss:1.99455
[108]	train-mlogloss:1.98305	validation-mlogloss:1.99108
[109]	train-mlogloss:1.97951	validation-mlogloss:1.98760
[110]	train-mlogloss:1.97573	validation-mlogloss:1.98398
[111]	train-mlogloss:1.97198	validation-mlogloss:1.98035
[112]	train-mlogloss:1.96828	validation-mlogloss:1.97677
[113]	train-mlogloss:1.96478	validation-mlogloss:1.97355
[114]	train-mlogloss:1.96129	validation-mlogloss:1.97019
[115]	train-mlogloss:1.95772	validation-mlogloss:1.96685
[116]	train-mlogloss:1.95417	validation-mlogloss:1.96346
[117]	train-mlogloss:1.95075	validation-mlogloss:1.96027
[118]	train-mlogloss:1.94721	validation-mlogloss:1.95699
[119]	train-mlogloss:1.94378	validation-mlogloss:1.95376
[120]	train-mlogloss:1.94040	validation-mlogloss:1.95050
[121]	train-mlogloss:1.93717	validation-mlogloss:1.94724
[122]	train-mlogloss:1.93392	validation-mlogloss:1.94393
[123]	train-mlogloss:1.93071	validation-mlogloss:1.94066
[124]	train-mlogloss:1.92728	validation-mlogloss:1.93754
[125]	train-mlogloss:1.92414	validation-mlogloss:1.93455
[126]	train-mlogloss:1.92082	validation-mlogloss:1.93140
[127]	train-mlogloss:1.91752	validation-mlogloss:1.92824
[128]	train-mlogloss:1.91440	validation-mlogloss:1.92520
[129]	train-mlogloss:1.91110	validation-mlogloss:1.92198
[130]	train-mlogloss:1.90790	validation-mlogloss:1.91889
[131]	train-mlogloss:1.90484	validation-mlogloss:1.91606
[132]	train-mlogloss:1.90158	validation-mlogloss:1.91312
[133]	train-mlogloss:1.89865	validation-mlogloss:1.91029
[134]	train-mlogloss:1.89562	validation-mlogloss:1.90744
[135]	train-mlogloss:1.89255	validation-mlogloss:1.90469
[136]	train-mlogloss:1.88967	validation-mlogloss:1.90194
[137]	train-mlogloss:1.88656	validation-mlogloss:1.89916
[138]	train-mlogloss:1.88375	validation-mlogloss:1.89643
[139]	train-mlogloss:1.88082	validation-mlogloss:1.89371
[140]	train-mlogloss:1.87783	validation-mlogloss:1.89095
[141]	train-mlogloss:1.87493	validation-mlogloss:1.88810
[142]	train-mlogloss:1.87197	validation-mlogloss:1.88526
[143]	train-mlogloss:1.86911	validation-mlogloss:1.88248
[144]	train-mlogloss:1.86620	validation-mlogloss:1.87970
[145]	train-mlogloss:1.86355	validation-mlogloss:1.87726
[146]	train-mlogloss:1.86057	validation-mlogloss:1.87445
[147]	train-mlogloss:1.85783	validation-mlogloss:1.87181
[148]	train-mlogloss:1.85504	validation-mlogloss:1.86936
[149]	train-mlogloss:1.85223	validation-mlogloss:1.86677
[150]	train-mlogloss:1.84931	validation-mlogloss:1.86420
[151]	train-mlogloss:1.84671	validation-mlogloss:1.86175
[152]	train-mlogloss:1.84397	validation-mlogloss:1.85926
[153]	train-mlogloss:1.84113	validation-mlogloss:1.85666
[154]	train-mlogloss:1.83845	validation-mlogloss:1.85405
[155]	train-mlogloss:1.83596	validation-mlogloss:1.85182
[156]	train-mlogloss:1.83338	validation-mlogloss:1.84933
[157]	train-mlogloss:1.83071	validation-mlogloss:1.84688
[158]	train-mlogloss:1.82816	validation-mlogloss:1.84442
[159]	train-mlogloss:1.82574	validation-mlogloss:1.84220
[160]	train-mlogloss:1.82318	validation-mlogloss:1.83972
[161]	train-mlogloss:1.82069	validation-mlogloss:1.83727
[162]	train-mlogloss:1.81819	validation-mlogloss:1.83492
[163]	train-mlogloss:1.81571	validation-mlogloss:1.83268
[164]	train-mlogloss:1.81333	validation-mlogloss:1.83046
[165]	train-mlogloss:1.81097	validation-mlogloss:1.82821
[166]	train-mlogloss:1.80846	validation-mlogloss:1.82588
[167]	train-mlogloss:1.80614	validation-mlogloss:1.82369
[168]	train-mlogloss:1.80383	validation-mlogloss:1.82144
[169]	train-mlogloss:1.80143	validation-mlogloss:1.81915
[170]	train-mlogloss:1.79907	validation-mlogloss:1.81692
[171]	train-mlogloss:1.79660	validation-mlogloss:1.81472
[172]	train-mlogloss:1.79405	validation-mlogloss:1.81233
[173]	train-mlogloss:1.79180	validation-mlogloss:1.81004
[174]	train-mlogloss:1.78955	validation-mlogloss:1.80801
[175]	train-mlogloss:1.78739	validation-mlogloss:1.80595
[176]	train-mlogloss:1.78522	validation-mlogloss:1.80390
[177]	train-mlogloss:1.78305	validation-mlogloss:1.80194
[178]	train-mlogloss:1.78093	validation-mlogloss:1.79989
[179]	train-mlogloss:1.77865	validation-mlogloss:1.79771
[180]	train-mlogloss:1.77649	validation-mlogloss:1.79575
[181]	train-mlogloss:1.77419	validation-mlogloss:1.79363
[182]	train-mlogloss:1.77204	validation-mlogloss:1.79167
[183]	train-mlogloss:1.76992	validation-mlogloss:1.78963
[184]	train-mlogloss:1.76777	validation-mlogloss:1.78758
[185]	train-mlogloss:1.76557	validation-mlogloss:1.78567
[186]	train-mlogloss:1.76331	validation-mlogloss:1.78358
[187]	train-mlogloss:1.76123	validation-mlogloss:1.78161
[188]	train-mlogloss:1.75930	validation-mlogloss:1.77977
[189]	train-mlogloss:1.75718	validation-mlogloss:1.77777
[190]	train-mlogloss:1.75499	validation-mlogloss:1.77564
[191]	train-mlogloss:1.75289	validation-mlogloss:1.77351
[192]	train-mlogloss:1.75083	validation-mlogloss:1.77155
[193]	train-mlogloss:1.74894	validation-mlogloss:1.76970
[194]	train-mlogloss:1.74703	validation-mlogloss:1.76809
[195]	train-mlogloss:1.74510	validation-mlogloss:1.76621
[196]	train-mlogloss:1.74316	validation-mlogloss:1.76439
[197]	train-mlogloss:1.74109	validation-mlogloss:1.76250
[198]	train-mlogloss:1.73927	validation-mlogloss:1.76069
[199]	train-mlogloss:1.73724	validation-mlogloss:1.75891
[200]	train-mlogloss:1.73541	validation-mlogloss:1.75716
[201]	train-mlogloss:1.73357	validation-mlogloss:1.75547
[202]	train-mlogloss:1.73163	validation-mlogloss:1.75367
[203]	train-mlogloss:1.72961	validation-mlogloss:1.75182
[204]	train-mlogloss:1.72759	validation-mlogloss:1.74994
[205]	train-mlogloss:1.72564	validation-mlogloss:1.74827
[206]	train-mlogloss:1.72382	validation-mlogloss:1.74655
[207]	train-mlogloss:1.72188	validation-mlogloss:1.74469
[208]	train-mlogloss:1.72022	validation-mlogloss:1.74320
[209]	train-mlogloss:1.71832	validation-mlogloss:1.74141
[210]	train-mlogloss:1.71653	validation-mlogloss:1.73982
[211]	train-mlogloss:1.71471	validation-mlogloss:1.73805
[212]	train-mlogloss:1.71292	validation-mlogloss:1.73637
[213]	train-mlogloss:1.71106	validation-mlogloss:1.73458
[214]	train-mlogloss:1.70923	validation-mlogloss:1.73313
[215]	train-mlogloss:1.70749	validation-mlogloss:1.73144
[216]	train-mlogloss:1.70580	validation-mlogloss:1.72976
[217]	train-mlogloss:1.70412	validation-mlogloss:1.72815
[218]	train-mlogloss:1.70243	validation-mlogloss:1.72645
[219]	train-mlogloss:1.70065	validation-mlogloss:1.72473
[220]	train-mlogloss:1.69898	validation-mlogloss:1.72317
[221]	train-mlogloss:1.69734	validation-mlogloss:1.72151
[222]	train-mlogloss:1.69577	validation-mlogloss:1.72015
[223]	train-mlogloss:1.69408	validation-mlogloss:1.71845
[224]	train-mlogloss:1.69236	validation-mlogloss:1.71688
[225]	train-mlogloss:1.69076	validation-mlogloss:1.71539
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[597]	train-mlogloss:1.42150	validation-mlogloss:1.49459
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[601]	train-mlogloss:1.42023	validation-mlogloss:1.49367
[602]	train-mlogloss:1.41995	validation-mlogloss:1.49346
[603]	train-mlogloss:1.41962	validation-mlogloss:1.49327
[604]	train-mlogloss:1.41932	validation-mlogloss:1.49309
[605]	train-mlogloss:1.41906	validation-mlogloss:1.49293
[606]	train-mlogloss:1.41873	validation-mlogloss:1.49282
[607]	train-mlogloss:1.41841	validation-mlogloss:1.49252
[608]	train-mlogloss:1.41812	validation-mlogloss:1.49237
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[610]	train-mlogloss:1.41749	validation-mlogloss:1.49196
[611]	train-mlogloss:1.41718	validation-mlogloss:1.49177
[612]	train-mlogloss:1.41682	validation-mlogloss:1.49158
[613]	train-mlogloss:1.41650	validation-mlogloss:1.49144
[614]	train-mlogloss:1.41623	validation-mlogloss:1.49129
[615]	train-mlogloss:1.41595	validation-mlogloss:1.49108
[616]	train-mlogloss:1.41567	validation-mlogloss:1.49093
[617]	train-mlogloss:1.41533	validation-mlogloss:1.49058
[618]	train-mlogloss:1.41496	validation-mlogloss:1.49041
[619]	train-mlogloss:1.41464	validation-mlogloss:1.49018
[620]	train-mlogloss:1.41433	validation-mlogloss:1.49000
[621]	train-mlogloss:1.41401	validation-mlogloss:1.48980
[622]	train-mlogloss:1.41377	validation-mlogloss:1.48959
[623]	train-mlogloss:1.41352	validation-mlogloss:1.48940
[624]	train-mlogloss:1.41323	validation-mlogloss:1.48920
[625]	train-mlogloss:1.41298	validation-mlogloss:1.48903
[626]	train-mlogloss:1.41273	validation-mlogloss:1.48893
[627]	train-mlogloss:1.41244	validation-mlogloss:1.48873
[628]	train-mlogloss:1.41217	validation-mlogloss:1.48861
Saved model to disk.
2025-11-07 10:21:07.918678: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:21:07.930031: 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 00:00:1762507267.943296 2380965 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507267.947376 2380965 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507267.957669 2380965 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507267.957691 2380965 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507267.957693 2380965 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507267.957695 2380965 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:21:07.960772: 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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[2]	train-mlogloss:2.67590	validation-mlogloss:2.67570
[3]	train-mlogloss:2.66566	validation-mlogloss:2.66531
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[6]	train-mlogloss:2.63525	validation-mlogloss:2.63479
[7]	train-mlogloss:2.62575	validation-mlogloss:2.62526
[8]	train-mlogloss:2.61551	validation-mlogloss:2.61507
[9]	train-mlogloss:2.60566	validation-mlogloss:2.60510
[10]	train-mlogloss:2.59556	validation-mlogloss:2.59484
[11]	train-mlogloss:2.58607	validation-mlogloss:2.58558
[12]	train-mlogloss:2.57593	validation-mlogloss:2.57543
[13]	train-mlogloss:2.56633	validation-mlogloss:2.56575
[14]	train-mlogloss:2.55713	validation-mlogloss:2.55674
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[16]	train-mlogloss:2.53909	validation-mlogloss:2.53826
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[19]	train-mlogloss:2.51145	validation-mlogloss:2.51104
[20]	train-mlogloss:2.50292	validation-mlogloss:2.50251
[21]	train-mlogloss:2.49406	validation-mlogloss:2.49342
[22]	train-mlogloss:2.48549	validation-mlogloss:2.48494
[23]	train-mlogloss:2.47690	validation-mlogloss:2.47622
[24]	train-mlogloss:2.46864	validation-mlogloss:2.46786
[25]	train-mlogloss:2.45983	validation-mlogloss:2.45906
[26]	train-mlogloss:2.45138	validation-mlogloss:2.45049
[27]	train-mlogloss:2.44281	validation-mlogloss:2.44176
[28]	train-mlogloss:2.43420	validation-mlogloss:2.43314
[29]	train-mlogloss:2.42589	validation-mlogloss:2.42472
[30]	train-mlogloss:2.41759	validation-mlogloss:2.41626
[31]	train-mlogloss:2.40940	validation-mlogloss:2.40804
[32]	train-mlogloss:2.40140	validation-mlogloss:2.40007
[33]	train-mlogloss:2.39364	validation-mlogloss:2.39236
[34]	train-mlogloss:2.38558	validation-mlogloss:2.38444
[35]	train-mlogloss:2.37808	validation-mlogloss:2.37691
[36]	train-mlogloss:2.37080	validation-mlogloss:2.36961
[37]	train-mlogloss:2.36376	validation-mlogloss:2.36218
[38]	train-mlogloss:2.35602	validation-mlogloss:2.35453
[39]	train-mlogloss:2.34888	validation-mlogloss:2.34739
[40]	train-mlogloss:2.34164	validation-mlogloss:2.34007
[41]	train-mlogloss:2.33456	validation-mlogloss:2.33321
[42]	train-mlogloss:2.32749	validation-mlogloss:2.32631
[43]	train-mlogloss:2.32033	validation-mlogloss:2.31911
[44]	train-mlogloss:2.31317	validation-mlogloss:2.31192
[45]	train-mlogloss:2.30651	validation-mlogloss:2.30501
[46]	train-mlogloss:2.29978	validation-mlogloss:2.29852
[47]	train-mlogloss:2.29301	validation-mlogloss:2.29190
[48]	train-mlogloss:2.28641	validation-mlogloss:2.28523
[49]	train-mlogloss:2.27950	validation-mlogloss:2.27837
[50]	train-mlogloss:2.27290	validation-mlogloss:2.27170
[51]	train-mlogloss:2.26628	validation-mlogloss:2.26514
[52]	train-mlogloss:2.25994	validation-mlogloss:2.25898
[53]	train-mlogloss:2.25379	validation-mlogloss:2.25284
[54]	train-mlogloss:2.24748	validation-mlogloss:2.24703
[55]	train-mlogloss:2.24101	validation-mlogloss:2.24069
[56]	train-mlogloss:2.23487	validation-mlogloss:2.23457
[57]	train-mlogloss:2.22894	validation-mlogloss:2.22857
[58]	train-mlogloss:2.22282	validation-mlogloss:2.22265
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[60]	train-mlogloss:2.21082	validation-mlogloss:2.21093
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[62]	train-mlogloss:2.19950	validation-mlogloss:2.19974
[63]	train-mlogloss:2.19397	validation-mlogloss:2.19420
[64]	train-mlogloss:2.18825	validation-mlogloss:2.18882
[65]	train-mlogloss:2.18266	validation-mlogloss:2.18357
[66]	train-mlogloss:2.17708	validation-mlogloss:2.17790
[67]	train-mlogloss:2.17153	validation-mlogloss:2.17249
[68]	train-mlogloss:2.16630	validation-mlogloss:2.16771
[69]	train-mlogloss:2.16070	validation-mlogloss:2.16235
[70]	train-mlogloss:2.15540	validation-mlogloss:2.15718
[71]	train-mlogloss:2.14997	validation-mlogloss:2.15207
[72]	train-mlogloss:2.14483	validation-mlogloss:2.14722
[73]	train-mlogloss:2.13973	validation-mlogloss:2.14232
[74]	train-mlogloss:2.13456	validation-mlogloss:2.13711
[75]	train-mlogloss:2.12958	validation-mlogloss:2.13251
[76]	train-mlogloss:2.12453	validation-mlogloss:2.12761
[77]	train-mlogloss:2.11937	validation-mlogloss:2.12276
[78]	train-mlogloss:2.11432	validation-mlogloss:2.11770
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[80]	train-mlogloss:2.10465	validation-mlogloss:2.10800
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[83]	train-mlogloss:2.09057	validation-mlogloss:2.09403
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[85]	train-mlogloss:2.08124	validation-mlogloss:2.08504
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[103]	train-mlogloss:2.00400	validation-mlogloss:2.01059
[104]	train-mlogloss:2.00005	validation-mlogloss:2.00691
[105]	train-mlogloss:1.99609	validation-mlogloss:2.00323
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[107]	train-mlogloss:1.98845	validation-mlogloss:1.99575
[108]	train-mlogloss:1.98471	validation-mlogloss:1.99205
[109]	train-mlogloss:1.98092	validation-mlogloss:1.98845
[110]	train-mlogloss:1.97721	validation-mlogloss:1.98506
[111]	train-mlogloss:1.97347	validation-mlogloss:1.98144
[112]	train-mlogloss:1.96986	validation-mlogloss:1.97803
[113]	train-mlogloss:1.96627	validation-mlogloss:1.97463
[114]	train-mlogloss:1.96252	validation-mlogloss:1.97089
[115]	train-mlogloss:1.95903	validation-mlogloss:1.96773
[116]	train-mlogloss:1.95544	validation-mlogloss:1.96441
[117]	train-mlogloss:1.95179	validation-mlogloss:1.96091
[118]	train-mlogloss:1.94828	validation-mlogloss:1.95756
[119]	train-mlogloss:1.94492	validation-mlogloss:1.95449
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[122]	train-mlogloss:1.93478	validation-mlogloss:1.94452
[123]	train-mlogloss:1.93146	validation-mlogloss:1.94125
[124]	train-mlogloss:1.92817	validation-mlogloss:1.93801
[125]	train-mlogloss:1.92487	validation-mlogloss:1.93476
[126]	train-mlogloss:1.92163	validation-mlogloss:1.93156
[127]	train-mlogloss:1.91850	validation-mlogloss:1.92860
[128]	train-mlogloss:1.91519	validation-mlogloss:1.92559
[129]	train-mlogloss:1.91217	validation-mlogloss:1.92265
[130]	train-mlogloss:1.90891	validation-mlogloss:1.91947
[131]	train-mlogloss:1.90581	validation-mlogloss:1.91647
[132]	train-mlogloss:1.90271	validation-mlogloss:1.91349
[133]	train-mlogloss:1.89966	validation-mlogloss:1.91055
[134]	train-mlogloss:1.89651	validation-mlogloss:1.90748
[135]	train-mlogloss:1.89346	validation-mlogloss:1.90459
[136]	train-mlogloss:1.89044	validation-mlogloss:1.90163
[137]	train-mlogloss:1.88744	validation-mlogloss:1.89875
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[140]	train-mlogloss:1.87899	validation-mlogloss:1.89087
[141]	train-mlogloss:1.87610	validation-mlogloss:1.88822
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[143]	train-mlogloss:1.87051	validation-mlogloss:1.88282
[144]	train-mlogloss:1.86758	validation-mlogloss:1.88011
[145]	train-mlogloss:1.86457	validation-mlogloss:1.87731
[146]	train-mlogloss:1.86167	validation-mlogloss:1.87430
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[148]	train-mlogloss:1.85615	validation-mlogloss:1.86921
[149]	train-mlogloss:1.85321	validation-mlogloss:1.86638
[150]	train-mlogloss:1.85053	validation-mlogloss:1.86374
[151]	train-mlogloss:1.84773	validation-mlogloss:1.86115
[152]	train-mlogloss:1.84502	validation-mlogloss:1.85864
[153]	train-mlogloss:1.84231	validation-mlogloss:1.85608
[154]	train-mlogloss:1.83977	validation-mlogloss:1.85368
[155]	train-mlogloss:1.83698	validation-mlogloss:1.85099
[156]	train-mlogloss:1.83441	validation-mlogloss:1.84855
[157]	train-mlogloss:1.83174	validation-mlogloss:1.84627
[158]	train-mlogloss:1.82926	validation-mlogloss:1.84383
[159]	train-mlogloss:1.82663	validation-mlogloss:1.84127
[160]	train-mlogloss:1.82397	validation-mlogloss:1.83895
[161]	train-mlogloss:1.82136	validation-mlogloss:1.83644
[162]	train-mlogloss:1.81876	validation-mlogloss:1.83426
[163]	train-mlogloss:1.81634	validation-mlogloss:1.83187
[164]	train-mlogloss:1.81375	validation-mlogloss:1.82945
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[169]	train-mlogloss:1.80186	validation-mlogloss:1.81845
[170]	train-mlogloss:1.79944	validation-mlogloss:1.81617
[171]	train-mlogloss:1.79724	validation-mlogloss:1.81423
[172]	train-mlogloss:1.79473	validation-mlogloss:1.81190
[173]	train-mlogloss:1.79251	validation-mlogloss:1.80972
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[611]	train-mlogloss:1.41773	validation-mlogloss:1.49133
[612]	train-mlogloss:1.41737	validation-mlogloss:1.49110
[613]	train-mlogloss:1.41707	validation-mlogloss:1.49101
[614]	train-mlogloss:1.41676	validation-mlogloss:1.49098
[615]	train-mlogloss:1.41640	validation-mlogloss:1.49082
[616]	train-mlogloss:1.41614	validation-mlogloss:1.49063
[617]	train-mlogloss:1.41587	validation-mlogloss:1.49046
[618]	train-mlogloss:1.41553	validation-mlogloss:1.49030
[619]	train-mlogloss:1.41523	validation-mlogloss:1.49011
[620]	train-mlogloss:1.41491	validation-mlogloss:1.48983
[621]	train-mlogloss:1.41459	validation-mlogloss:1.48967
[622]	train-mlogloss:1.41429	validation-mlogloss:1.48943
[623]	train-mlogloss:1.41397	validation-mlogloss:1.48925
[624]	train-mlogloss:1.41364	validation-mlogloss:1.48908
[625]	train-mlogloss:1.41346	validation-mlogloss:1.48896
[626]	train-mlogloss:1.41314	validation-mlogloss:1.48877
[627]	train-mlogloss:1.41282	validation-mlogloss:1.48862
[628]	train-mlogloss:1.41248	validation-mlogloss:1.48843
Saved model to disk.
2025-11-07 10:21:33.761642: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:21:33.773489: 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 00:00:1762507293.787471 2381376 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507293.791653 2381376 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507293.801538 2381376 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507293.801558 2381376 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507293.801561 2381376 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507293.801563 2381376 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:21:33.804656: 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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[7]	train-mlogloss:2.62559	validation-mlogloss:2.62523
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[10]	train-mlogloss:2.59509	validation-mlogloss:2.59468
[11]	train-mlogloss:2.58555	validation-mlogloss:2.58504
[12]	train-mlogloss:2.57624	validation-mlogloss:2.57569
[13]	train-mlogloss:2.56615	validation-mlogloss:2.56550
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[16]	train-mlogloss:2.53878	validation-mlogloss:2.53830
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[19]	train-mlogloss:2.51194	validation-mlogloss:2.51149
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[21]	train-mlogloss:2.49396	validation-mlogloss:2.49345
[22]	train-mlogloss:2.48525	validation-mlogloss:2.48473
[23]	train-mlogloss:2.47615	validation-mlogloss:2.47576
[24]	train-mlogloss:2.46743	validation-mlogloss:2.46698
[25]	train-mlogloss:2.45853	validation-mlogloss:2.45825
[26]	train-mlogloss:2.45024	validation-mlogloss:2.45002
[27]	train-mlogloss:2.44201	validation-mlogloss:2.44175
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[29]	train-mlogloss:2.42570	validation-mlogloss:2.42523
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[31]	train-mlogloss:2.40983	validation-mlogloss:2.40923
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[34]	train-mlogloss:2.38626	validation-mlogloss:2.38531
[35]	train-mlogloss:2.37845	validation-mlogloss:2.37753
[36]	train-mlogloss:2.37075	validation-mlogloss:2.36987
[37]	train-mlogloss:2.36331	validation-mlogloss:2.36245
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[39]	train-mlogloss:2.34894	validation-mlogloss:2.34795
[40]	train-mlogloss:2.34190	validation-mlogloss:2.34071
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[42]	train-mlogloss:2.32779	validation-mlogloss:2.32663
[43]	train-mlogloss:2.32095	validation-mlogloss:2.31975
[44]	train-mlogloss:2.31424	validation-mlogloss:2.31305
[45]	train-mlogloss:2.30715	validation-mlogloss:2.30634
[46]	train-mlogloss:2.30021	validation-mlogloss:2.29958
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[49]	train-mlogloss:2.28041	validation-mlogloss:2.27971
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[54]	train-mlogloss:2.24844	validation-mlogloss:2.24774
[55]	train-mlogloss:2.24194	validation-mlogloss:2.24143
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[57]	train-mlogloss:2.22959	validation-mlogloss:2.22928
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[63]	train-mlogloss:2.19476	validation-mlogloss:2.19531
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[65]	train-mlogloss:2.18370	validation-mlogloss:2.18462
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[71]	train-mlogloss:2.15074	validation-mlogloss:2.15271
[72]	train-mlogloss:2.14540	validation-mlogloss:2.14762
[73]	train-mlogloss:2.14011	validation-mlogloss:2.14247
[74]	train-mlogloss:2.13486	validation-mlogloss:2.13760
[75]	train-mlogloss:2.12974	validation-mlogloss:2.13260
[76]	train-mlogloss:2.12471	validation-mlogloss:2.12773
[77]	train-mlogloss:2.11953	validation-mlogloss:2.12255
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[102]	train-mlogloss:2.00776	validation-mlogloss:2.01514
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[104]	train-mlogloss:1.99986	validation-mlogloss:2.00737
[105]	train-mlogloss:1.99600	validation-mlogloss:2.00367
[106]	train-mlogloss:1.99221	validation-mlogloss:2.00006
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[108]	train-mlogloss:1.98448	validation-mlogloss:1.99284
[109]	train-mlogloss:1.98072	validation-mlogloss:1.98926
[110]	train-mlogloss:1.97704	validation-mlogloss:1.98563
[111]	train-mlogloss:1.97335	validation-mlogloss:1.98201
[112]	train-mlogloss:1.96974	validation-mlogloss:1.97854
[113]	train-mlogloss:1.96613	validation-mlogloss:1.97515
[114]	train-mlogloss:1.96263	validation-mlogloss:1.97182
[115]	train-mlogloss:1.95908	validation-mlogloss:1.96837
[116]	train-mlogloss:1.95538	validation-mlogloss:1.96463
[117]	train-mlogloss:1.95190	validation-mlogloss:1.96125
[118]	train-mlogloss:1.94850	validation-mlogloss:1.95790
[119]	train-mlogloss:1.94503	validation-mlogloss:1.95461
[120]	train-mlogloss:1.94176	validation-mlogloss:1.95149
[121]	train-mlogloss:1.93836	validation-mlogloss:1.94849
[122]	train-mlogloss:1.93482	validation-mlogloss:1.94517
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[423]	train-mlogloss:1.49894	validation-mlogloss:1.54973
[424]	train-mlogloss:1.49841	validation-mlogloss:1.54913
[425]	train-mlogloss:1.49780	validation-mlogloss:1.54872
[426]	train-mlogloss:1.49719	validation-mlogloss:1.54823
[427]	train-mlogloss:1.49662	validation-mlogloss:1.54777
[428]	train-mlogloss:1.49600	validation-mlogloss:1.54754
[429]	train-mlogloss:1.49545	validation-mlogloss:1.54708
[430]	train-mlogloss:1.49492	validation-mlogloss:1.54671
[431]	train-mlogloss:1.49433	validation-mlogloss:1.54614
[432]	train-mlogloss:1.49378	validation-mlogloss:1.54566
[433]	train-mlogloss:1.49324	validation-mlogloss:1.54524
[434]	train-mlogloss:1.49264	validation-mlogloss:1.54466
[435]	train-mlogloss:1.49211	validation-mlogloss:1.54424
[436]	train-mlogloss:1.49153	validation-mlogloss:1.54377
[437]	train-mlogloss:1.49095	validation-mlogloss:1.54334
[438]	train-mlogloss:1.49038	validation-mlogloss:1.54277
[439]	train-mlogloss:1.48977	validation-mlogloss:1.54249
[440]	train-mlogloss:1.48926	validation-mlogloss:1.54208
[441]	train-mlogloss:1.48869	validation-mlogloss:1.54173
[442]	train-mlogloss:1.48811	validation-mlogloss:1.54136
[443]	train-mlogloss:1.48758	validation-mlogloss:1.54092
[444]	train-mlogloss:1.48708	validation-mlogloss:1.54055
[445]	train-mlogloss:1.48651	validation-mlogloss:1.54021
[446]	train-mlogloss:1.48593	validation-mlogloss:1.53987
[447]	train-mlogloss:1.48537	validation-mlogloss:1.53959
[448]	train-mlogloss:1.48491	validation-mlogloss:1.53931
[449]	train-mlogloss:1.48437	validation-mlogloss:1.53893
[450]	train-mlogloss:1.48389	validation-mlogloss:1.53859
[451]	train-mlogloss:1.48333	validation-mlogloss:1.53822
[452]	train-mlogloss:1.48277	validation-mlogloss:1.53785
[453]	train-mlogloss:1.48223	validation-mlogloss:1.53742
[454]	train-mlogloss:1.48174	validation-mlogloss:1.53701
[455]	train-mlogloss:1.48124	validation-mlogloss:1.53657
[456]	train-mlogloss:1.48067	validation-mlogloss:1.53614
[457]	train-mlogloss:1.48018	validation-mlogloss:1.53582
[458]	train-mlogloss:1.47967	validation-mlogloss:1.53532
[459]	train-mlogloss:1.47916	validation-mlogloss:1.53489
[460]	train-mlogloss:1.47869	validation-mlogloss:1.53447
[461]	train-mlogloss:1.47816	validation-mlogloss:1.53407
[462]	train-mlogloss:1.47762	validation-mlogloss:1.53373
[463]	train-mlogloss:1.47711	validation-mlogloss:1.53332
[464]	train-mlogloss:1.47666	validation-mlogloss:1.53303
[465]	train-mlogloss:1.47615	validation-mlogloss:1.53273
[466]	train-mlogloss:1.47563	validation-mlogloss:1.53237
[467]	train-mlogloss:1.47512	validation-mlogloss:1.53193
[468]	train-mlogloss:1.47455	validation-mlogloss:1.53165
[469]	train-mlogloss:1.47399	validation-mlogloss:1.53123
[470]	train-mlogloss:1.47348	validation-mlogloss:1.53093
[471]	train-mlogloss:1.47296	validation-mlogloss:1.53054
[472]	train-mlogloss:1.47240	validation-mlogloss:1.53020
[473]	train-mlogloss:1.47190	validation-mlogloss:1.52991
[474]	train-mlogloss:1.47144	validation-mlogloss:1.52960
[475]	train-mlogloss:1.47102	validation-mlogloss:1.52922
[476]	train-mlogloss:1.47053	validation-mlogloss:1.52884
[477]	train-mlogloss:1.47004	validation-mlogloss:1.52851
[478]	train-mlogloss:1.46951	validation-mlogloss:1.52804
[479]	train-mlogloss:1.46902	validation-mlogloss:1.52770
[480]	train-mlogloss:1.46851	validation-mlogloss:1.52728
[481]	train-mlogloss:1.46810	validation-mlogloss:1.52696
[482]	train-mlogloss:1.46761	validation-mlogloss:1.52668
[483]	train-mlogloss:1.46722	validation-mlogloss:1.52647
[484]	train-mlogloss:1.46671	validation-mlogloss:1.52614
[485]	train-mlogloss:1.46631	validation-mlogloss:1.52578
[486]	train-mlogloss:1.46582	validation-mlogloss:1.52548
[487]	train-mlogloss:1.46540	validation-mlogloss:1.52521
[488]	train-mlogloss:1.46492	validation-mlogloss:1.52479
[489]	train-mlogloss:1.46444	validation-mlogloss:1.52460
[490]	train-mlogloss:1.46392	validation-mlogloss:1.52433
[491]	train-mlogloss:1.46349	validation-mlogloss:1.52392
[492]	train-mlogloss:1.46303	validation-mlogloss:1.52353
[493]	train-mlogloss:1.46249	validation-mlogloss:1.52319
[494]	train-mlogloss:1.46204	validation-mlogloss:1.52275
[495]	train-mlogloss:1.46157	validation-mlogloss:1.52243
[496]	train-mlogloss:1.46116	validation-mlogloss:1.52220
[497]	train-mlogloss:1.46073	validation-mlogloss:1.52193
[498]	train-mlogloss:1.46030	validation-mlogloss:1.52156
[499]	train-mlogloss:1.45988	validation-mlogloss:1.52124
[500]	train-mlogloss:1.45941	validation-mlogloss:1.52087
[501]	train-mlogloss:1.45893	validation-mlogloss:1.52053
[502]	train-mlogloss:1.45848	validation-mlogloss:1.52023
[503]	train-mlogloss:1.45804	validation-mlogloss:1.51994
[504]	train-mlogloss:1.45762	validation-mlogloss:1.51956
[505]	train-mlogloss:1.45717	validation-mlogloss:1.51913
[506]	train-mlogloss:1.45672	validation-mlogloss:1.51874
[507]	train-mlogloss:1.45625	validation-mlogloss:1.51840
[508]	train-mlogloss:1.45587	validation-mlogloss:1.51818
[509]	train-mlogloss:1.45544	validation-mlogloss:1.51790
[510]	train-mlogloss:1.45499	validation-mlogloss:1.51749
[511]	train-mlogloss:1.45449	validation-mlogloss:1.51723
[512]	train-mlogloss:1.45407	validation-mlogloss:1.51696
[513]	train-mlogloss:1.45366	validation-mlogloss:1.51664
[514]	train-mlogloss:1.45326	validation-mlogloss:1.51640
[515]	train-mlogloss:1.45283	validation-mlogloss:1.51608
[516]	train-mlogloss:1.45241	validation-mlogloss:1.51564
[517]	train-mlogloss:1.45202	validation-mlogloss:1.51539
[518]	train-mlogloss:1.45155	validation-mlogloss:1.51513
[519]	train-mlogloss:1.45112	validation-mlogloss:1.51491
[520]	train-mlogloss:1.45069	validation-mlogloss:1.51470
[521]	train-mlogloss:1.45022	validation-mlogloss:1.51444
[522]	train-mlogloss:1.44979	validation-mlogloss:1.51420
[523]	train-mlogloss:1.44942	validation-mlogloss:1.51398
[524]	train-mlogloss:1.44896	validation-mlogloss:1.51365
[525]	train-mlogloss:1.44853	validation-mlogloss:1.51328
[526]	train-mlogloss:1.44812	validation-mlogloss:1.51301
[527]	train-mlogloss:1.44774	validation-mlogloss:1.51271
[528]	train-mlogloss:1.44733	validation-mlogloss:1.51238
[529]	train-mlogloss:1.44697	validation-mlogloss:1.51205
[530]	train-mlogloss:1.44661	validation-mlogloss:1.51181
[531]	train-mlogloss:1.44623	validation-mlogloss:1.51149
[532]	train-mlogloss:1.44581	validation-mlogloss:1.51120
[533]	train-mlogloss:1.44537	validation-mlogloss:1.51094
[534]	train-mlogloss:1.44494	validation-mlogloss:1.51056
[535]	train-mlogloss:1.44452	validation-mlogloss:1.51032
[536]	train-mlogloss:1.44410	validation-mlogloss:1.50996
[537]	train-mlogloss:1.44370	validation-mlogloss:1.50967
[538]	train-mlogloss:1.44329	validation-mlogloss:1.50945
[539]	train-mlogloss:1.44291	validation-mlogloss:1.50924
[540]	train-mlogloss:1.44257	validation-mlogloss:1.50898
[541]	train-mlogloss:1.44218	validation-mlogloss:1.50884
[542]	train-mlogloss:1.44176	validation-mlogloss:1.50856
[543]	train-mlogloss:1.44135	validation-mlogloss:1.50835
[544]	train-mlogloss:1.44100	validation-mlogloss:1.50811
[545]	train-mlogloss:1.44059	validation-mlogloss:1.50775
[546]	train-mlogloss:1.44016	validation-mlogloss:1.50733
[547]	train-mlogloss:1.43978	validation-mlogloss:1.50707
[548]	train-mlogloss:1.43944	validation-mlogloss:1.50683
[549]	train-mlogloss:1.43903	validation-mlogloss:1.50655
[550]	train-mlogloss:1.43862	validation-mlogloss:1.50622
[551]	train-mlogloss:1.43823	validation-mlogloss:1.50595
[552]	train-mlogloss:1.43785	validation-mlogloss:1.50559
[553]	train-mlogloss:1.43746	validation-mlogloss:1.50545
[554]	train-mlogloss:1.43704	validation-mlogloss:1.50524
[555]	train-mlogloss:1.43670	validation-mlogloss:1.50498
[556]	train-mlogloss:1.43630	validation-mlogloss:1.50475
[557]	train-mlogloss:1.43591	validation-mlogloss:1.50446
[558]	train-mlogloss:1.43550	validation-mlogloss:1.50421
[559]	train-mlogloss:1.43521	validation-mlogloss:1.50407
[560]	train-mlogloss:1.43490	validation-mlogloss:1.50388
[561]	train-mlogloss:1.43454	validation-mlogloss:1.50360
[562]	train-mlogloss:1.43418	validation-mlogloss:1.50334
[563]	train-mlogloss:1.43383	validation-mlogloss:1.50306
[564]	train-mlogloss:1.43343	validation-mlogloss:1.50285
[565]	train-mlogloss:1.43306	validation-mlogloss:1.50258
[566]	train-mlogloss:1.43270	validation-mlogloss:1.50228
[567]	train-mlogloss:1.43234	validation-mlogloss:1.50202
[568]	train-mlogloss:1.43200	validation-mlogloss:1.50173
[569]	train-mlogloss:1.43167	validation-mlogloss:1.50144
[570]	train-mlogloss:1.43136	validation-mlogloss:1.50123
[571]	train-mlogloss:1.43100	validation-mlogloss:1.50099
[572]	train-mlogloss:1.43069	validation-mlogloss:1.50078
[573]	train-mlogloss:1.43036	validation-mlogloss:1.50049
[574]	train-mlogloss:1.43001	validation-mlogloss:1.50041
[575]	train-mlogloss:1.42963	validation-mlogloss:1.50018
[576]	train-mlogloss:1.42931	validation-mlogloss:1.50003
[577]	train-mlogloss:1.42893	validation-mlogloss:1.49984
[578]	train-mlogloss:1.42854	validation-mlogloss:1.49952
[579]	train-mlogloss:1.42815	validation-mlogloss:1.49931
[580]	train-mlogloss:1.42778	validation-mlogloss:1.49908
[581]	train-mlogloss:1.42745	validation-mlogloss:1.49893
[582]	train-mlogloss:1.42712	validation-mlogloss:1.49865
[583]	train-mlogloss:1.42676	validation-mlogloss:1.49850
[584]	train-mlogloss:1.42641	validation-mlogloss:1.49824
[585]	train-mlogloss:1.42610	validation-mlogloss:1.49801
[586]	train-mlogloss:1.42575	validation-mlogloss:1.49782
[587]	train-mlogloss:1.42546	validation-mlogloss:1.49756
[588]	train-mlogloss:1.42510	validation-mlogloss:1.49737
[589]	train-mlogloss:1.42473	validation-mlogloss:1.49712
[590]	train-mlogloss:1.42441	validation-mlogloss:1.49697
[591]	train-mlogloss:1.42402	validation-mlogloss:1.49690
[592]	train-mlogloss:1.42370	validation-mlogloss:1.49667
[593]	train-mlogloss:1.42338	validation-mlogloss:1.49647
[594]	train-mlogloss:1.42302	validation-mlogloss:1.49628
[595]	train-mlogloss:1.42264	validation-mlogloss:1.49602
[596]	train-mlogloss:1.42226	validation-mlogloss:1.49579
[597]	train-mlogloss:1.42195	validation-mlogloss:1.49570
[598]	train-mlogloss:1.42163	validation-mlogloss:1.49537
[599]	train-mlogloss:1.42128	validation-mlogloss:1.49517
[600]	train-mlogloss:1.42099	validation-mlogloss:1.49501
[601]	train-mlogloss:1.42064	validation-mlogloss:1.49481
[602]	train-mlogloss:1.42029	validation-mlogloss:1.49460
[603]	train-mlogloss:1.41996	validation-mlogloss:1.49439
[604]	train-mlogloss:1.41967	validation-mlogloss:1.49419
[605]	train-mlogloss:1.41933	validation-mlogloss:1.49388
[606]	train-mlogloss:1.41898	validation-mlogloss:1.49363
[607]	train-mlogloss:1.41863	validation-mlogloss:1.49352
[608]	train-mlogloss:1.41831	validation-mlogloss:1.49330
[609]	train-mlogloss:1.41798	validation-mlogloss:1.49302
[610]	train-mlogloss:1.41767	validation-mlogloss:1.49286
[611]	train-mlogloss:1.41728	validation-mlogloss:1.49260
[612]	train-mlogloss:1.41698	validation-mlogloss:1.49231
[613]	train-mlogloss:1.41668	validation-mlogloss:1.49215
[614]	train-mlogloss:1.41639	validation-mlogloss:1.49190
[615]	train-mlogloss:1.41609	validation-mlogloss:1.49172
[616]	train-mlogloss:1.41575	validation-mlogloss:1.49152
[617]	train-mlogloss:1.41550	validation-mlogloss:1.49132
[618]	train-mlogloss:1.41519	validation-mlogloss:1.49116
[619]	train-mlogloss:1.41489	validation-mlogloss:1.49112
[620]	train-mlogloss:1.41461	validation-mlogloss:1.49106
[621]	train-mlogloss:1.41430	validation-mlogloss:1.49092
[622]	train-mlogloss:1.41402	validation-mlogloss:1.49075
[623]	train-mlogloss:1.41371	validation-mlogloss:1.49054
[624]	train-mlogloss:1.41344	validation-mlogloss:1.49039
[625]	train-mlogloss:1.41308	validation-mlogloss:1.49014
[626]	train-mlogloss:1.41277	validation-mlogloss:1.48994
[627]	train-mlogloss:1.41254	validation-mlogloss:1.48973
[628]	train-mlogloss:1.41223	validation-mlogloss:1.48965
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.86 [%]
Global accuracy score (test) = 43.55 [%]
Global F1 score (train) = 55.71 [%]
Global F1 score (test) = 42.47 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.20      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.33      0.42      0.37       184
       CAMINAR USUAL SPEED       0.24      0.14      0.17       184
            CAMINAR ZIGZAG       0.30      0.29      0.29       184
          DE PIE BARRIENDO       0.43      0.46      0.45       184
   DE PIE DOBLANDO TOALLAS       0.43      0.41      0.42       184
    DE PIE MOVIENDO LIBROS       0.38      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.43      0.82      0.56       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.28      0.24      0.26       184
         SENTADO USANDO PC       0.14      0.05      0.08       184
      SENTADO VIENDO LA TV       0.38      0.35      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.54      0.73      0.62       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 4: 43.55 [%]
F1-score capturado en la ejecución 4: 42.47 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.16 [%]
Global accuracy score (test) = 44.36 [%]
Global F1 score (train) = 56.01 [%]
Global F1 score (test) = 43.37 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.21      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.40      0.35       184
       CAMINAR USUAL SPEED       0.27      0.15      0.19       184
            CAMINAR ZIGZAG       0.31      0.30      0.30       184
          DE PIE BARRIENDO       0.47      0.50      0.49       184
   DE PIE DOBLANDO TOALLAS       0.43      0.43      0.43       184
    DE PIE MOVIENDO LIBROS       0.40      0.39      0.40       184
          DE PIE USANDO PC       0.53      0.65      0.59       184
        FASE REPOSO CON K5       0.43      0.82      0.56       184
INCREMENTAL CICLOERGOMETRO       0.91      0.60      0.73       184
           SENTADO LEYENDO       0.32      0.27      0.29       184
         SENTADO USANDO PC       0.17      0.07      0.09       184
      SENTADO VIENDO LA TV       0.39      0.37      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.55      0.73      0.62       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.45      0.43      2737
              weighted avg       0.44      0.44      0.43      2737


Accuracy capturado en la ejecución 5: 44.36 [%]
F1-score capturado en la ejecución 5: 43.37 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.03 [%]
Global accuracy score (test) = 43.73 [%]
Global F1 score (train) = 55.87 [%]
Global F1 score (test) = 42.79 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.22      0.23       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.41      0.36       184
       CAMINAR USUAL SPEED       0.25      0.14      0.18       184
            CAMINAR ZIGZAG       0.26      0.26      0.26       184
          DE PIE BARRIENDO       0.47      0.48      0.47       184
   DE PIE DOBLANDO TOALLAS       0.45      0.45      0.45       184
    DE PIE MOVIENDO LIBROS       0.41      0.40      0.40       184
          DE PIE USANDO PC       0.54      0.66      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.91      0.60      0.72       184
           SENTADO LEYENDO       0.26      0.22      0.24       184
         SENTADO USANDO PC       0.19      0.08      0.11       184
      SENTADO VIENDO LA TV       0.37      0.34      0.35       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 6: 43.73 [%]
F1-score capturado en la ejecución 6: 42.79 [%]

=== EJECUCIÓN 7 ===

--- TRAIN (ejecución 7) ---

--- TEST (ejecución 7) ---
2025-11-07 10:21:59.921049: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:21:59.932258: 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 00:00:1762507319.945313 2381818 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507319.949266 2381818 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507319.959420 2381818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507319.959438 2381818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507319.959440 2381818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507319.959441 2381818 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:21:59.962673: 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:2.69725	validation-mlogloss:2.69707
[1]	train-mlogloss:2.68614	validation-mlogloss:2.68579
[2]	train-mlogloss:2.67552	validation-mlogloss:2.67529
[3]	train-mlogloss:2.66471	validation-mlogloss:2.66438
[4]	train-mlogloss:2.65485	validation-mlogloss:2.65466
[5]	train-mlogloss:2.64476	validation-mlogloss:2.64441
[6]	train-mlogloss:2.63433	validation-mlogloss:2.63370
[7]	train-mlogloss:2.62388	validation-mlogloss:2.62330
[8]	train-mlogloss:2.61455	validation-mlogloss:2.61399
[9]	train-mlogloss:2.60449	validation-mlogloss:2.60375
[10]	train-mlogloss:2.59417	validation-mlogloss:2.59341
[11]	train-mlogloss:2.58455	validation-mlogloss:2.58382
[12]	train-mlogloss:2.57480	validation-mlogloss:2.57384
[13]	train-mlogloss:2.56525	validation-mlogloss:2.56427
[14]	train-mlogloss:2.55594	validation-mlogloss:2.55484
[15]	train-mlogloss:2.54663	validation-mlogloss:2.54562
[16]	train-mlogloss:2.53768	validation-mlogloss:2.53680
[17]	train-mlogloss:2.52879	validation-mlogloss:2.52801
[18]	train-mlogloss:2.52000	validation-mlogloss:2.51909
[19]	train-mlogloss:2.51094	validation-mlogloss:2.51025
[20]	train-mlogloss:2.50176	validation-mlogloss:2.50109
[21]	train-mlogloss:2.49286	validation-mlogloss:2.49210
[22]	train-mlogloss:2.48407	validation-mlogloss:2.48322
[23]	train-mlogloss:2.47509	validation-mlogloss:2.47425
[24]	train-mlogloss:2.46690	validation-mlogloss:2.46585
[25]	train-mlogloss:2.45822	validation-mlogloss:2.45709
[26]	train-mlogloss:2.44960	validation-mlogloss:2.44838
[27]	train-mlogloss:2.44154	validation-mlogloss:2.44037
[28]	train-mlogloss:2.43350	validation-mlogloss:2.43235
[29]	train-mlogloss:2.42553	validation-mlogloss:2.42442
[30]	train-mlogloss:2.41713	validation-mlogloss:2.41618
[31]	train-mlogloss:2.40868	validation-mlogloss:2.40806
[32]	train-mlogloss:2.40057	validation-mlogloss:2.40026
[33]	train-mlogloss:2.39296	validation-mlogloss:2.39271
[34]	train-mlogloss:2.38574	validation-mlogloss:2.38530
[35]	train-mlogloss:2.37818	validation-mlogloss:2.37752
[36]	train-mlogloss:2.37079	validation-mlogloss:2.36998
[37]	train-mlogloss:2.36326	validation-mlogloss:2.36220
[38]	train-mlogloss:2.35561	validation-mlogloss:2.35467
[39]	train-mlogloss:2.34853	validation-mlogloss:2.34772
[40]	train-mlogloss:2.34128	validation-mlogloss:2.34052
[41]	train-mlogloss:2.33437	validation-mlogloss:2.33355
[42]	train-mlogloss:2.32730	validation-mlogloss:2.32643
[43]	train-mlogloss:2.32055	validation-mlogloss:2.31978
[44]	train-mlogloss:2.31356	validation-mlogloss:2.31268
[45]	train-mlogloss:2.30682	validation-mlogloss:2.30616
[46]	train-mlogloss:2.29976	validation-mlogloss:2.29905
[47]	train-mlogloss:2.29263	validation-mlogloss:2.29201
[48]	train-mlogloss:2.28580	validation-mlogloss:2.28533
[49]	train-mlogloss:2.27910	validation-mlogloss:2.27876
[50]	train-mlogloss:2.27244	validation-mlogloss:2.27208
[51]	train-mlogloss:2.26596	validation-mlogloss:2.26567
[52]	train-mlogloss:2.25972	validation-mlogloss:2.25950
[53]	train-mlogloss:2.25346	validation-mlogloss:2.25343
[54]	train-mlogloss:2.24712	validation-mlogloss:2.24702
[55]	train-mlogloss:2.24086	validation-mlogloss:2.24093
[56]	train-mlogloss:2.23454	validation-mlogloss:2.23466
[57]	train-mlogloss:2.22858	validation-mlogloss:2.22875
[58]	train-mlogloss:2.22269	validation-mlogloss:2.22282
[59]	train-mlogloss:2.21651	validation-mlogloss:2.21676
[60]	train-mlogloss:2.21063	validation-mlogloss:2.21078
[61]	train-mlogloss:2.20482	validation-mlogloss:2.20501
[62]	train-mlogloss:2.19929	validation-mlogloss:2.19957
[63]	train-mlogloss:2.19329	validation-mlogloss:2.19405
[64]	train-mlogloss:2.18793	validation-mlogloss:2.18879
[65]	train-mlogloss:2.18208	validation-mlogloss:2.18295
[66]	train-mlogloss:2.17637	validation-mlogloss:2.17756
[67]	train-mlogloss:2.17092	validation-mlogloss:2.17222
[68]	train-mlogloss:2.16570	validation-mlogloss:2.16702
[69]	train-mlogloss:2.16017	validation-mlogloss:2.16159
[70]	train-mlogloss:2.15475	validation-mlogloss:2.15647
[71]	train-mlogloss:2.14943	validation-mlogloss:2.15095
[72]	train-mlogloss:2.14400	validation-mlogloss:2.14556
[73]	train-mlogloss:2.13898	validation-mlogloss:2.14091
[74]	train-mlogloss:2.13356	validation-mlogloss:2.13560
[75]	train-mlogloss:2.12849	validation-mlogloss:2.13057
[76]	train-mlogloss:2.12339	validation-mlogloss:2.12551
[77]	train-mlogloss:2.11846	validation-mlogloss:2.12057
[78]	train-mlogloss:2.11316	validation-mlogloss:2.11545
[79]	train-mlogloss:2.10830	validation-mlogloss:2.11088
[80]	train-mlogloss:2.10325	validation-mlogloss:2.10617
[81]	train-mlogloss:2.09823	validation-mlogloss:2.10121
[82]	train-mlogloss:2.09340	validation-mlogloss:2.09657
[83]	train-mlogloss:2.08874	validation-mlogloss:2.09223
[84]	train-mlogloss:2.08392	validation-mlogloss:2.08764
[85]	train-mlogloss:2.07936	validation-mlogloss:2.08319
[86]	train-mlogloss:2.07460	validation-mlogloss:2.07864
[87]	train-mlogloss:2.07021	validation-mlogloss:2.07433
[88]	train-mlogloss:2.06589	validation-mlogloss:2.07010
[89]	train-mlogloss:2.06106	validation-mlogloss:2.06565
[90]	train-mlogloss:2.05658	validation-mlogloss:2.06156
[91]	train-mlogloss:2.05218	validation-mlogloss:2.05740
[92]	train-mlogloss:2.04803	validation-mlogloss:2.05317
[93]	train-mlogloss:2.04360	validation-mlogloss:2.04922
[94]	train-mlogloss:2.03939	validation-mlogloss:2.04535
[95]	train-mlogloss:2.03525	validation-mlogloss:2.04152
[96]	train-mlogloss:2.03106	validation-mlogloss:2.03729
[97]	train-mlogloss:2.02691	validation-mlogloss:2.03333
[98]	train-mlogloss:2.02266	validation-mlogloss:2.02918
[99]	train-mlogloss:2.01873	validation-mlogloss:2.02522
[100]	train-mlogloss:2.01485	validation-mlogloss:2.02134
[101]	train-mlogloss:2.01075	validation-mlogloss:2.01738
[102]	train-mlogloss:2.00684	validation-mlogloss:2.01372
[103]	train-mlogloss:2.00296	validation-mlogloss:2.00999
[104]	train-mlogloss:1.99892	validation-mlogloss:2.00609
[105]	train-mlogloss:1.99501	validation-mlogloss:2.00245
[106]	train-mlogloss:1.99108	validation-mlogloss:1.99891
[107]	train-mlogloss:1.98723	validation-mlogloss:1.99514
[108]	train-mlogloss:1.98354	validation-mlogloss:1.99164
[109]	train-mlogloss:1.97985	validation-mlogloss:1.98805
[110]	train-mlogloss:1.97620	validation-mlogloss:1.98462
[111]	train-mlogloss:1.97235	validation-mlogloss:1.98087
[112]	train-mlogloss:1.96866	validation-mlogloss:1.97729
[113]	train-mlogloss:1.96503	validation-mlogloss:1.97389
[114]	train-mlogloss:1.96145	validation-mlogloss:1.97048
[115]	train-mlogloss:1.95791	validation-mlogloss:1.96729
[116]	train-mlogloss:1.95463	validation-mlogloss:1.96418
[117]	train-mlogloss:1.95117	validation-mlogloss:1.96087
[118]	train-mlogloss:1.94795	validation-mlogloss:1.95781
[119]	train-mlogloss:1.94447	validation-mlogloss:1.95464
[120]	train-mlogloss:1.94083	validation-mlogloss:1.95115
[121]	train-mlogloss:1.93744	validation-mlogloss:1.94791
[122]	train-mlogloss:1.93406	validation-mlogloss:1.94471
[123]	train-mlogloss:1.93072	validation-mlogloss:1.94145
[124]	train-mlogloss:1.92736	validation-mlogloss:1.93827
[125]	train-mlogloss:1.92414	validation-mlogloss:1.93512
[126]	train-mlogloss:1.92086	validation-mlogloss:1.93206
[127]	train-mlogloss:1.91775	validation-mlogloss:1.92913
[128]	train-mlogloss:1.91449	validation-mlogloss:1.92608
[129]	train-mlogloss:1.91122	validation-mlogloss:1.92300
[130]	train-mlogloss:1.90819	validation-mlogloss:1.92020
[131]	train-mlogloss:1.90493	validation-mlogloss:1.91714
[132]	train-mlogloss:1.90189	validation-mlogloss:1.91426
[133]	train-mlogloss:1.89862	validation-mlogloss:1.91128
[134]	train-mlogloss:1.89562	validation-mlogloss:1.90843
[135]	train-mlogloss:1.89246	validation-mlogloss:1.90522
[136]	train-mlogloss:1.88941	validation-mlogloss:1.90231
[137]	train-mlogloss:1.88648	validation-mlogloss:1.89949
[138]	train-mlogloss:1.88347	validation-mlogloss:1.89665
[139]	train-mlogloss:1.88057	validation-mlogloss:1.89385
[140]	train-mlogloss:1.87770	validation-mlogloss:1.89113
[141]	train-mlogloss:1.87474	validation-mlogloss:1.88816
[142]	train-mlogloss:1.87191	validation-mlogloss:1.88552
[143]	train-mlogloss:1.86905	validation-mlogloss:1.88267
[144]	train-mlogloss:1.86603	validation-mlogloss:1.87988
[145]	train-mlogloss:1.86318	validation-mlogloss:1.87705
[146]	train-mlogloss:1.86043	validation-mlogloss:1.87447
[147]	train-mlogloss:1.85759	validation-mlogloss:1.87190
[148]	train-mlogloss:1.85484	validation-mlogloss:1.86915
[149]	train-mlogloss:1.85225	validation-mlogloss:1.86648
[150]	train-mlogloss:1.84958	validation-mlogloss:1.86399
[151]	train-mlogloss:1.84679	validation-mlogloss:1.86129
[152]	train-mlogloss:1.84409	validation-mlogloss:1.85880
[153]	train-mlogloss:1.84141	validation-mlogloss:1.85621
[154]	train-mlogloss:1.83879	validation-mlogloss:1.85394
[155]	train-mlogloss:1.83610	validation-mlogloss:1.85143
[156]	train-mlogloss:1.83342	validation-mlogloss:1.84893
[157]	train-mlogloss:1.83088	validation-mlogloss:1.84649
[158]	train-mlogloss:1.82825	validation-mlogloss:1.84403
[159]	train-mlogloss:1.82556	validation-mlogloss:1.84152
[160]	train-mlogloss:1.82304	validation-mlogloss:1.83917
[161]	train-mlogloss:1.82053	validation-mlogloss:1.83669
[162]	train-mlogloss:1.81789	validation-mlogloss:1.83419
[163]	train-mlogloss:1.81556	validation-mlogloss:1.83193
[164]	train-mlogloss:1.81311	validation-mlogloss:1.82951
[165]	train-mlogloss:1.81071	validation-mlogloss:1.82707
[166]	train-mlogloss:1.80831	validation-mlogloss:1.82482
[167]	train-mlogloss:1.80602	validation-mlogloss:1.82255
[168]	train-mlogloss:1.80364	validation-mlogloss:1.82031
[169]	train-mlogloss:1.80122	validation-mlogloss:1.81786
[170]	train-mlogloss:1.79888	validation-mlogloss:1.81557
[171]	train-mlogloss:1.79648	validation-mlogloss:1.81322
[172]	train-mlogloss:1.79410	validation-mlogloss:1.81091
[173]	train-mlogloss:1.79187	validation-mlogloss:1.80895
[174]	train-mlogloss:1.78964	validation-mlogloss:1.80694
[175]	train-mlogloss:1.78723	validation-mlogloss:1.80479
[176]	train-mlogloss:1.78487	validation-mlogloss:1.80265
[177]	train-mlogloss:1.78273	validation-mlogloss:1.80063
[178]	train-mlogloss:1.78054	validation-mlogloss:1.79862
[179]	train-mlogloss:1.77837	validation-mlogloss:1.79654
[180]	train-mlogloss:1.77627	validation-mlogloss:1.79451
[181]	train-mlogloss:1.77408	validation-mlogloss:1.79249
[182]	train-mlogloss:1.77184	validation-mlogloss:1.79038
[183]	train-mlogloss:1.76971	validation-mlogloss:1.78842
[184]	train-mlogloss:1.76739	validation-mlogloss:1.78630
[185]	train-mlogloss:1.76531	validation-mlogloss:1.78447
[186]	train-mlogloss:1.76319	validation-mlogloss:1.78247
[187]	train-mlogloss:1.76116	validation-mlogloss:1.78054
[188]	train-mlogloss:1.75912	validation-mlogloss:1.77865
[189]	train-mlogloss:1.75711	validation-mlogloss:1.77679
[190]	train-mlogloss:1.75511	validation-mlogloss:1.77491
[191]	train-mlogloss:1.75309	validation-mlogloss:1.77286
[192]	train-mlogloss:1.75109	validation-mlogloss:1.77105
[193]	train-mlogloss:1.74911	validation-mlogloss:1.76920
[194]	train-mlogloss:1.74715	validation-mlogloss:1.76729
[195]	train-mlogloss:1.74512	validation-mlogloss:1.76558
[196]	train-mlogloss:1.74318	validation-mlogloss:1.76388
[197]	train-mlogloss:1.74114	validation-mlogloss:1.76193
[198]	train-mlogloss:1.73929	validation-mlogloss:1.76030
[199]	train-mlogloss:1.73735	validation-mlogloss:1.75861
[200]	train-mlogloss:1.73547	validation-mlogloss:1.75678
[201]	train-mlogloss:1.73356	validation-mlogloss:1.75505
[202]	train-mlogloss:1.73172	validation-mlogloss:1.75327
[203]	train-mlogloss:1.72984	validation-mlogloss:1.75154
[204]	train-mlogloss:1.72803	validation-mlogloss:1.74982
[205]	train-mlogloss:1.72627	validation-mlogloss:1.74815
[206]	train-mlogloss:1.72436	validation-mlogloss:1.74651
[207]	train-mlogloss:1.72258	validation-mlogloss:1.74484
[208]	train-mlogloss:1.72073	validation-mlogloss:1.74334
[209]	train-mlogloss:1.71897	validation-mlogloss:1.74169
[210]	train-mlogloss:1.71715	validation-mlogloss:1.74005
[211]	train-mlogloss:1.71535	validation-mlogloss:1.73833
[212]	train-mlogloss:1.71365	validation-mlogloss:1.73668
[213]	train-mlogloss:1.71186	validation-mlogloss:1.73498
[214]	train-mlogloss:1.71020	validation-mlogloss:1.73341
[215]	train-mlogloss:1.70836	validation-mlogloss:1.73173
[216]	train-mlogloss:1.70665	validation-mlogloss:1.73023
[217]	train-mlogloss:1.70484	validation-mlogloss:1.72856
[218]	train-mlogloss:1.70315	validation-mlogloss:1.72698
[219]	train-mlogloss:1.70145	validation-mlogloss:1.72551
[220]	train-mlogloss:1.69980	validation-mlogloss:1.72383
[221]	train-mlogloss:1.69825	validation-mlogloss:1.72242
[222]	train-mlogloss:1.69657	validation-mlogloss:1.72088
[223]	train-mlogloss:1.69495	validation-mlogloss:1.71929
[224]	train-mlogloss:1.69337	validation-mlogloss:1.71776
[225]	train-mlogloss:1.69184	validation-mlogloss:1.71626
[226]	train-mlogloss:1.69030	validation-mlogloss:1.71482
[227]	train-mlogloss:1.68866	validation-mlogloss:1.71336
[228]	train-mlogloss:1.68705	validation-mlogloss:1.71184
[229]	train-mlogloss:1.68551	validation-mlogloss:1.71037
[230]	train-mlogloss:1.68385	validation-mlogloss:1.70900
[231]	train-mlogloss:1.68229	validation-mlogloss:1.70760
[232]	train-mlogloss:1.68066	validation-mlogloss:1.70598
[233]	train-mlogloss:1.67907	validation-mlogloss:1.70454
[234]	train-mlogloss:1.67732	validation-mlogloss:1.70309
[235]	train-mlogloss:1.67577	validation-mlogloss:1.70177
[236]	train-mlogloss:1.67429	validation-mlogloss:1.70047
[237]	train-mlogloss:1.67278	validation-mlogloss:1.69906
[238]	train-mlogloss:1.67132	validation-mlogloss:1.69769
[239]	train-mlogloss:1.66978	validation-mlogloss:1.69639
[240]	train-mlogloss:1.66827	validation-mlogloss:1.69504
[241]	train-mlogloss:1.66686	validation-mlogloss:1.69392
[242]	train-mlogloss:1.66539	validation-mlogloss:1.69245
[243]	train-mlogloss:1.66398	validation-mlogloss:1.69116
[244]	train-mlogloss:1.66262	validation-mlogloss:1.68981
[245]	train-mlogloss:1.66123	validation-mlogloss:1.68855
[246]	train-mlogloss:1.65985	validation-mlogloss:1.68720
[247]	train-mlogloss:1.65859	validation-mlogloss:1.68598
[248]	train-mlogloss:1.65707	validation-mlogloss:1.68467
[249]	train-mlogloss:1.65572	validation-mlogloss:1.68352
[250]	train-mlogloss:1.65432	validation-mlogloss:1.68234
[251]	train-mlogloss:1.65285	validation-mlogloss:1.68119
[252]	train-mlogloss:1.65155	validation-mlogloss:1.67999
[253]	train-mlogloss:1.65012	validation-mlogloss:1.67864
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[556]	train-mlogloss:1.43702	validation-mlogloss:1.50401
[557]	train-mlogloss:1.43665	validation-mlogloss:1.50374
[558]	train-mlogloss:1.43630	validation-mlogloss:1.50354
[559]	train-mlogloss:1.43597	validation-mlogloss:1.50337
[560]	train-mlogloss:1.43554	validation-mlogloss:1.50314
[561]	train-mlogloss:1.43516	validation-mlogloss:1.50289
[562]	train-mlogloss:1.43485	validation-mlogloss:1.50263
[563]	train-mlogloss:1.43449	validation-mlogloss:1.50244
[564]	train-mlogloss:1.43409	validation-mlogloss:1.50222
[565]	train-mlogloss:1.43377	validation-mlogloss:1.50206
[566]	train-mlogloss:1.43334	validation-mlogloss:1.50180
[567]	train-mlogloss:1.43299	validation-mlogloss:1.50161
[568]	train-mlogloss:1.43267	validation-mlogloss:1.50133
[569]	train-mlogloss:1.43233	validation-mlogloss:1.50108
[570]	train-mlogloss:1.43192	validation-mlogloss:1.50078
[571]	train-mlogloss:1.43155	validation-mlogloss:1.50050
[572]	train-mlogloss:1.43124	validation-mlogloss:1.50030
[573]	train-mlogloss:1.43087	validation-mlogloss:1.50005
[574]	train-mlogloss:1.43048	validation-mlogloss:1.49987
[575]	train-mlogloss:1.43011	validation-mlogloss:1.49970
[576]	train-mlogloss:1.42977	validation-mlogloss:1.49951
[577]	train-mlogloss:1.42944	validation-mlogloss:1.49933
[578]	train-mlogloss:1.42903	validation-mlogloss:1.49911
[579]	train-mlogloss:1.42868	validation-mlogloss:1.49880
[580]	train-mlogloss:1.42837	validation-mlogloss:1.49852
[581]	train-mlogloss:1.42802	validation-mlogloss:1.49823
[582]	train-mlogloss:1.42764	validation-mlogloss:1.49809
[583]	train-mlogloss:1.42729	validation-mlogloss:1.49784
[584]	train-mlogloss:1.42701	validation-mlogloss:1.49756
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[587]	train-mlogloss:1.42597	validation-mlogloss:1.49708
[588]	train-mlogloss:1.42563	validation-mlogloss:1.49689
[589]	train-mlogloss:1.42530	validation-mlogloss:1.49676
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[591]	train-mlogloss:1.42465	validation-mlogloss:1.49632
[592]	train-mlogloss:1.42432	validation-mlogloss:1.49612
[593]	train-mlogloss:1.42394	validation-mlogloss:1.49595
[594]	train-mlogloss:1.42361	validation-mlogloss:1.49578
[595]	train-mlogloss:1.42322	validation-mlogloss:1.49572
[596]	train-mlogloss:1.42286	validation-mlogloss:1.49551
[597]	train-mlogloss:1.42254	validation-mlogloss:1.49529
[598]	train-mlogloss:1.42227	validation-mlogloss:1.49508
[599]	train-mlogloss:1.42191	validation-mlogloss:1.49489
[600]	train-mlogloss:1.42159	validation-mlogloss:1.49467
[601]	train-mlogloss:1.42130	validation-mlogloss:1.49446
[602]	train-mlogloss:1.42101	validation-mlogloss:1.49429
[603]	train-mlogloss:1.42070	validation-mlogloss:1.49413
[604]	train-mlogloss:1.42040	validation-mlogloss:1.49403
[605]	train-mlogloss:1.42008	validation-mlogloss:1.49375
[606]	train-mlogloss:1.41979	validation-mlogloss:1.49344
[607]	train-mlogloss:1.41948	validation-mlogloss:1.49317
[608]	train-mlogloss:1.41915	validation-mlogloss:1.49301
[609]	train-mlogloss:1.41889	validation-mlogloss:1.49279
[610]	train-mlogloss:1.41862	validation-mlogloss:1.49261
[611]	train-mlogloss:1.41833	validation-mlogloss:1.49241
[612]	train-mlogloss:1.41800	validation-mlogloss:1.49219
[613]	train-mlogloss:1.41764	validation-mlogloss:1.49198
[614]	train-mlogloss:1.41730	validation-mlogloss:1.49171
[615]	train-mlogloss:1.41692	validation-mlogloss:1.49154
[616]	train-mlogloss:1.41659	validation-mlogloss:1.49127
[617]	train-mlogloss:1.41628	validation-mlogloss:1.49111
[618]	train-mlogloss:1.41597	validation-mlogloss:1.49092
[619]	train-mlogloss:1.41571	validation-mlogloss:1.49068
[620]	train-mlogloss:1.41543	validation-mlogloss:1.49053
[621]	train-mlogloss:1.41506	validation-mlogloss:1.49038
[622]	train-mlogloss:1.41475	validation-mlogloss:1.49019
[623]	train-mlogloss:1.41445	validation-mlogloss:1.49000
[624]	train-mlogloss:1.41408	validation-mlogloss:1.48979
[625]	train-mlogloss:1.41376	validation-mlogloss:1.48965
[626]	train-mlogloss:1.41343	validation-mlogloss:1.48938
[627]	train-mlogloss:1.41314	validation-mlogloss:1.48919
[628]	train-mlogloss:1.41284	validation-mlogloss:1.48895
Saved model to disk.
2025-11-07 10:22:26.101911: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:22:26.113254: 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 00:00:1762507346.126226 2382236 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507346.130372 2382236 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507346.140054 2382236 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507346.140073 2382236 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507346.140075 2382236 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507346.140076 2382236 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:22:26.143107: 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:2.69722	validation-mlogloss:2.69717
[1]	train-mlogloss:2.68648	validation-mlogloss:2.68612
[2]	train-mlogloss:2.67623	validation-mlogloss:2.67578
[3]	train-mlogloss:2.66630	validation-mlogloss:2.66579
[4]	train-mlogloss:2.65640	validation-mlogloss:2.65582
[5]	train-mlogloss:2.64637	validation-mlogloss:2.64589
[6]	train-mlogloss:2.63618	validation-mlogloss:2.63584
[7]	train-mlogloss:2.62570	validation-mlogloss:2.62520
[8]	train-mlogloss:2.61603	validation-mlogloss:2.61560
[9]	train-mlogloss:2.60615	validation-mlogloss:2.60565
[10]	train-mlogloss:2.59616	validation-mlogloss:2.59546
[11]	train-mlogloss:2.58633	validation-mlogloss:2.58563
[12]	train-mlogloss:2.57681	validation-mlogloss:2.57629
[13]	train-mlogloss:2.56750	validation-mlogloss:2.56695
[14]	train-mlogloss:2.55820	validation-mlogloss:2.55751
[15]	train-mlogloss:2.54869	validation-mlogloss:2.54804
[16]	train-mlogloss:2.53963	validation-mlogloss:2.53894
[17]	train-mlogloss:2.53043	validation-mlogloss:2.52964
[18]	train-mlogloss:2.52121	validation-mlogloss:2.52054
[19]	train-mlogloss:2.51201	validation-mlogloss:2.51124
[20]	train-mlogloss:2.50322	validation-mlogloss:2.50235
[21]	train-mlogloss:2.49406	validation-mlogloss:2.49312
[22]	train-mlogloss:2.48511	validation-mlogloss:2.48433
[23]	train-mlogloss:2.47660	validation-mlogloss:2.47572
[24]	train-mlogloss:2.46807	validation-mlogloss:2.46708
[25]	train-mlogloss:2.45956	validation-mlogloss:2.45833
[26]	train-mlogloss:2.45073	validation-mlogloss:2.44927
[27]	train-mlogloss:2.44234	validation-mlogloss:2.44083
[28]	train-mlogloss:2.43407	validation-mlogloss:2.43247
[29]	train-mlogloss:2.42536	validation-mlogloss:2.42367
[30]	train-mlogloss:2.41708	validation-mlogloss:2.41533
[31]	train-mlogloss:2.40920	validation-mlogloss:2.40744
[32]	train-mlogloss:2.40093	validation-mlogloss:2.39938
[33]	train-mlogloss:2.39304	validation-mlogloss:2.39147
[34]	train-mlogloss:2.38508	validation-mlogloss:2.38358
[35]	train-mlogloss:2.37724	validation-mlogloss:2.37578
[36]	train-mlogloss:2.36983	validation-mlogloss:2.36841
[37]	train-mlogloss:2.36224	validation-mlogloss:2.36070
[38]	train-mlogloss:2.35479	validation-mlogloss:2.35321
[39]	train-mlogloss:2.34729	validation-mlogloss:2.34560
[40]	train-mlogloss:2.34054	validation-mlogloss:2.33880
[41]	train-mlogloss:2.33332	validation-mlogloss:2.33160
[42]	train-mlogloss:2.32664	validation-mlogloss:2.32496
[43]	train-mlogloss:2.32016	validation-mlogloss:2.31846
[44]	train-mlogloss:2.31325	validation-mlogloss:2.31150
[45]	train-mlogloss:2.30629	validation-mlogloss:2.30468
[46]	train-mlogloss:2.29994	validation-mlogloss:2.29851
[47]	train-mlogloss:2.29311	validation-mlogloss:2.29165
[48]	train-mlogloss:2.28639	validation-mlogloss:2.28483
[49]	train-mlogloss:2.27975	validation-mlogloss:2.27835
[50]	train-mlogloss:2.27323	validation-mlogloss:2.27190
[51]	train-mlogloss:2.26699	validation-mlogloss:2.26583
[52]	train-mlogloss:2.26034	validation-mlogloss:2.25909
[53]	train-mlogloss:2.25413	validation-mlogloss:2.25297
[54]	train-mlogloss:2.24791	validation-mlogloss:2.24649
[55]	train-mlogloss:2.24161	validation-mlogloss:2.24052
[56]	train-mlogloss:2.23533	validation-mlogloss:2.23434
[57]	train-mlogloss:2.22914	validation-mlogloss:2.22817
[58]	train-mlogloss:2.22301	validation-mlogloss:2.22213
[59]	train-mlogloss:2.21692	validation-mlogloss:2.21601
[60]	train-mlogloss:2.21125	validation-mlogloss:2.21033
[61]	train-mlogloss:2.20521	validation-mlogloss:2.20439
[62]	train-mlogloss:2.19939	validation-mlogloss:2.19876
[63]	train-mlogloss:2.19366	validation-mlogloss:2.19313
[64]	train-mlogloss:2.18805	validation-mlogloss:2.18747
[65]	train-mlogloss:2.18234	validation-mlogloss:2.18198
[66]	train-mlogloss:2.17646	validation-mlogloss:2.17646
[67]	train-mlogloss:2.17094	validation-mlogloss:2.17113
[68]	train-mlogloss:2.16543	validation-mlogloss:2.16579
[69]	train-mlogloss:2.15992	validation-mlogloss:2.16014
[70]	train-mlogloss:2.15438	validation-mlogloss:2.15456
[71]	train-mlogloss:2.14918	validation-mlogloss:2.14957
[72]	train-mlogloss:2.14418	validation-mlogloss:2.14477
[73]	train-mlogloss:2.13893	validation-mlogloss:2.13984
[74]	train-mlogloss:2.13372	validation-mlogloss:2.13499
[75]	train-mlogloss:2.12881	validation-mlogloss:2.13034
[76]	train-mlogloss:2.12380	validation-mlogloss:2.12562
[77]	train-mlogloss:2.11898	validation-mlogloss:2.12091
[78]	train-mlogloss:2.11399	validation-mlogloss:2.11593
[79]	train-mlogloss:2.10909	validation-mlogloss:2.11130
[80]	train-mlogloss:2.10404	validation-mlogloss:2.10634
[81]	train-mlogloss:2.09931	validation-mlogloss:2.10156
[82]	train-mlogloss:2.09436	validation-mlogloss:2.09689
[83]	train-mlogloss:2.08964	validation-mlogloss:2.09219
[84]	train-mlogloss:2.08500	validation-mlogloss:2.08750
[85]	train-mlogloss:2.08020	validation-mlogloss:2.08287
[86]	train-mlogloss:2.07577	validation-mlogloss:2.07849
[87]	train-mlogloss:2.07105	validation-mlogloss:2.07392
[88]	train-mlogloss:2.06647	validation-mlogloss:2.06950
[89]	train-mlogloss:2.06201	validation-mlogloss:2.06530
[90]	train-mlogloss:2.05762	validation-mlogloss:2.06115
[91]	train-mlogloss:2.05328	validation-mlogloss:2.05692
[92]	train-mlogloss:2.04907	validation-mlogloss:2.05284
[93]	train-mlogloss:2.04481	validation-mlogloss:2.04881
[94]	train-mlogloss:2.04051	validation-mlogloss:2.04495
[95]	train-mlogloss:2.03644	validation-mlogloss:2.04108
[96]	train-mlogloss:2.03202	validation-mlogloss:2.03677
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[98]	train-mlogloss:2.02374	validation-mlogloss:2.02877
[99]	train-mlogloss:2.01958	validation-mlogloss:2.02498
[100]	train-mlogloss:2.01555	validation-mlogloss:2.02112
[101]	train-mlogloss:2.01166	validation-mlogloss:2.01750
[102]	train-mlogloss:2.00772	validation-mlogloss:2.01366
[103]	train-mlogloss:2.00371	validation-mlogloss:2.00970
[104]	train-mlogloss:1.99990	validation-mlogloss:2.00607
[105]	train-mlogloss:1.99602	validation-mlogloss:2.00222
[106]	train-mlogloss:1.99220	validation-mlogloss:1.99851
[107]	train-mlogloss:1.98826	validation-mlogloss:1.99509
[108]	train-mlogloss:1.98452	validation-mlogloss:1.99166
[109]	train-mlogloss:1.98074	validation-mlogloss:1.98822
[110]	train-mlogloss:1.97682	validation-mlogloss:1.98442
[111]	train-mlogloss:1.97326	validation-mlogloss:1.98086
[112]	train-mlogloss:1.96961	validation-mlogloss:1.97754
[113]	train-mlogloss:1.96591	validation-mlogloss:1.97396
[114]	train-mlogloss:1.96229	validation-mlogloss:1.97050
[115]	train-mlogloss:1.95865	validation-mlogloss:1.96699
[116]	train-mlogloss:1.95518	validation-mlogloss:1.96367
[117]	train-mlogloss:1.95164	validation-mlogloss:1.96041
[118]	train-mlogloss:1.94813	validation-mlogloss:1.95715
[119]	train-mlogloss:1.94459	validation-mlogloss:1.95396
[120]	train-mlogloss:1.94107	validation-mlogloss:1.95049
[121]	train-mlogloss:1.93768	validation-mlogloss:1.94745
[122]	train-mlogloss:1.93436	validation-mlogloss:1.94439
[123]	train-mlogloss:1.93103	validation-mlogloss:1.94112
[124]	train-mlogloss:1.92760	validation-mlogloss:1.93772
[125]	train-mlogloss:1.92441	validation-mlogloss:1.93467
[126]	train-mlogloss:1.92117	validation-mlogloss:1.93160
[127]	train-mlogloss:1.91778	validation-mlogloss:1.92831
[128]	train-mlogloss:1.91457	validation-mlogloss:1.92526
[129]	train-mlogloss:1.91140	validation-mlogloss:1.92224
[130]	train-mlogloss:1.90827	validation-mlogloss:1.91945
[131]	train-mlogloss:1.90515	validation-mlogloss:1.91634
[132]	train-mlogloss:1.90189	validation-mlogloss:1.91325
[133]	train-mlogloss:1.89885	validation-mlogloss:1.91051
[134]	train-mlogloss:1.89565	validation-mlogloss:1.90751
[135]	train-mlogloss:1.89274	validation-mlogloss:1.90454
[136]	train-mlogloss:1.88976	validation-mlogloss:1.90173
[137]	train-mlogloss:1.88686	validation-mlogloss:1.89904
[138]	train-mlogloss:1.88379	validation-mlogloss:1.89589
[139]	train-mlogloss:1.88098	validation-mlogloss:1.89322
[140]	train-mlogloss:1.87809	validation-mlogloss:1.89056
[141]	train-mlogloss:1.87524	validation-mlogloss:1.88792
[142]	train-mlogloss:1.87241	validation-mlogloss:1.88526
[143]	train-mlogloss:1.86949	validation-mlogloss:1.88264
[144]	train-mlogloss:1.86649	validation-mlogloss:1.87978
[145]	train-mlogloss:1.86361	validation-mlogloss:1.87709
[146]	train-mlogloss:1.86077	validation-mlogloss:1.87443
[147]	train-mlogloss:1.85799	validation-mlogloss:1.87184
[148]	train-mlogloss:1.85528	validation-mlogloss:1.86928
[149]	train-mlogloss:1.85245	validation-mlogloss:1.86655
[150]	train-mlogloss:1.84974	validation-mlogloss:1.86407
[151]	train-mlogloss:1.84709	validation-mlogloss:1.86155
[152]	train-mlogloss:1.84444	validation-mlogloss:1.85901
[153]	train-mlogloss:1.84177	validation-mlogloss:1.85647
[154]	train-mlogloss:1.83917	validation-mlogloss:1.85391
[155]	train-mlogloss:1.83656	validation-mlogloss:1.85151
[156]	train-mlogloss:1.83382	validation-mlogloss:1.84888
[157]	train-mlogloss:1.83126	validation-mlogloss:1.84637
[158]	train-mlogloss:1.82861	validation-mlogloss:1.84377
[159]	train-mlogloss:1.82610	validation-mlogloss:1.84129
[160]	train-mlogloss:1.82353	validation-mlogloss:1.83908
[161]	train-mlogloss:1.82100	validation-mlogloss:1.83664
[162]	train-mlogloss:1.81855	validation-mlogloss:1.83414
[163]	train-mlogloss:1.81614	validation-mlogloss:1.83178
[164]	train-mlogloss:1.81377	validation-mlogloss:1.82959
[165]	train-mlogloss:1.81134	validation-mlogloss:1.82729
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[169]	train-mlogloss:1.80166	validation-mlogloss:1.81830
[170]	train-mlogloss:1.79927	validation-mlogloss:1.81609
[171]	train-mlogloss:1.79683	validation-mlogloss:1.81387
[172]	train-mlogloss:1.79454	validation-mlogloss:1.81179
[173]	train-mlogloss:1.79226	validation-mlogloss:1.80970
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[176]	train-mlogloss:1.78539	validation-mlogloss:1.80280
[177]	train-mlogloss:1.78302	validation-mlogloss:1.80059
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[179]	train-mlogloss:1.77854	validation-mlogloss:1.79628
[180]	train-mlogloss:1.77632	validation-mlogloss:1.79432
[181]	train-mlogloss:1.77417	validation-mlogloss:1.79241
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[183]	train-mlogloss:1.77002	validation-mlogloss:1.78851
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[185]	train-mlogloss:1.76572	validation-mlogloss:1.78440
[186]	train-mlogloss:1.76359	validation-mlogloss:1.78225
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[189]	train-mlogloss:1.75723	validation-mlogloss:1.77652
[190]	train-mlogloss:1.75513	validation-mlogloss:1.77454
[191]	train-mlogloss:1.75298	validation-mlogloss:1.77257
[192]	train-mlogloss:1.75107	validation-mlogloss:1.77085
[193]	train-mlogloss:1.74910	validation-mlogloss:1.76893
[194]	train-mlogloss:1.74695	validation-mlogloss:1.76706
[195]	train-mlogloss:1.74487	validation-mlogloss:1.76512
[196]	train-mlogloss:1.74295	validation-mlogloss:1.76330
[197]	train-mlogloss:1.74111	validation-mlogloss:1.76157
[198]	train-mlogloss:1.73920	validation-mlogloss:1.75996
[199]	train-mlogloss:1.73736	validation-mlogloss:1.75827
[200]	train-mlogloss:1.73538	validation-mlogloss:1.75628
[201]	train-mlogloss:1.73360	validation-mlogloss:1.75452
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[604]	train-mlogloss:1.41961	validation-mlogloss:1.49338
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[606]	train-mlogloss:1.41893	validation-mlogloss:1.49310
[607]	train-mlogloss:1.41857	validation-mlogloss:1.49294
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[610]	train-mlogloss:1.41749	validation-mlogloss:1.49227
[611]	train-mlogloss:1.41718	validation-mlogloss:1.49211
[612]	train-mlogloss:1.41684	validation-mlogloss:1.49185
[613]	train-mlogloss:1.41652	validation-mlogloss:1.49166
[614]	train-mlogloss:1.41621	validation-mlogloss:1.49152
[615]	train-mlogloss:1.41593	validation-mlogloss:1.49134
[616]	train-mlogloss:1.41560	validation-mlogloss:1.49111
[617]	train-mlogloss:1.41531	validation-mlogloss:1.49098
[618]	train-mlogloss:1.41506	validation-mlogloss:1.49078
[619]	train-mlogloss:1.41470	validation-mlogloss:1.49052
[620]	train-mlogloss:1.41437	validation-mlogloss:1.49047
[621]	train-mlogloss:1.41409	validation-mlogloss:1.49024
[622]	train-mlogloss:1.41380	validation-mlogloss:1.49017
[623]	train-mlogloss:1.41348	validation-mlogloss:1.48991
[624]	train-mlogloss:1.41319	validation-mlogloss:1.48965
[625]	train-mlogloss:1.41292	validation-mlogloss:1.48944
[626]	train-mlogloss:1.41266	validation-mlogloss:1.48924
[627]	train-mlogloss:1.41239	validation-mlogloss:1.48911
[628]	train-mlogloss:1.41209	validation-mlogloss:1.48889
Saved model to disk.
2025-11-07 10:22:52.549836: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:22:52.561494: 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 00:00:1762507372.575527 2382661 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507372.579720 2382661 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507372.590155 2382661 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507372.590174 2382661 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507372.590177 2382661 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507372.590179 2382661 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:22:52.593342: 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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[2]	train-mlogloss:2.67629	validation-mlogloss:2.67593
[3]	train-mlogloss:2.66566	validation-mlogloss:2.66524
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[7]	train-mlogloss:2.62440	validation-mlogloss:2.62395
[8]	train-mlogloss:2.61468	validation-mlogloss:2.61416
[9]	train-mlogloss:2.60503	validation-mlogloss:2.60466
[10]	train-mlogloss:2.59508	validation-mlogloss:2.59466
[11]	train-mlogloss:2.58469	validation-mlogloss:2.58422
[12]	train-mlogloss:2.57478	validation-mlogloss:2.57415
[13]	train-mlogloss:2.56515	validation-mlogloss:2.56456
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[16]	train-mlogloss:2.53770	validation-mlogloss:2.53698
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[23]	train-mlogloss:2.47537	validation-mlogloss:2.47425
[24]	train-mlogloss:2.46641	validation-mlogloss:2.46536
[25]	train-mlogloss:2.45776	validation-mlogloss:2.45639
[26]	train-mlogloss:2.44943	validation-mlogloss:2.44815
[27]	train-mlogloss:2.44077	validation-mlogloss:2.43963
[28]	train-mlogloss:2.43205	validation-mlogloss:2.43107
[29]	train-mlogloss:2.42391	validation-mlogloss:2.42287
[30]	train-mlogloss:2.41552	validation-mlogloss:2.41475
[31]	train-mlogloss:2.40765	validation-mlogloss:2.40681
[32]	train-mlogloss:2.40000	validation-mlogloss:2.39903
[33]	train-mlogloss:2.39188	validation-mlogloss:2.39080
[34]	train-mlogloss:2.38437	validation-mlogloss:2.38318
[35]	train-mlogloss:2.37684	validation-mlogloss:2.37561
[36]	train-mlogloss:2.36930	validation-mlogloss:2.36827
[37]	train-mlogloss:2.36177	validation-mlogloss:2.36105
[38]	train-mlogloss:2.35449	validation-mlogloss:2.35398
[39]	train-mlogloss:2.34711	validation-mlogloss:2.34648
[40]	train-mlogloss:2.33971	validation-mlogloss:2.33908
[41]	train-mlogloss:2.33261	validation-mlogloss:2.33201
[42]	train-mlogloss:2.32546	validation-mlogloss:2.32497
[43]	train-mlogloss:2.31857	validation-mlogloss:2.31827
[44]	train-mlogloss:2.31176	validation-mlogloss:2.31155
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[55]	train-mlogloss:2.24092	validation-mlogloss:2.24106
[56]	train-mlogloss:2.23480	validation-mlogloss:2.23494
[57]	train-mlogloss:2.22866	validation-mlogloss:2.22879
[58]	train-mlogloss:2.22300	validation-mlogloss:2.22312
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[63]	train-mlogloss:2.19353	validation-mlogloss:2.19416
[64]	train-mlogloss:2.18795	validation-mlogloss:2.18865
[65]	train-mlogloss:2.18224	validation-mlogloss:2.18307
[66]	train-mlogloss:2.17678	validation-mlogloss:2.17760
[67]	train-mlogloss:2.17147	validation-mlogloss:2.17232
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[69]	train-mlogloss:2.16028	validation-mlogloss:2.16149
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[72]	train-mlogloss:2.14401	validation-mlogloss:2.14617
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[74]	train-mlogloss:2.13360	validation-mlogloss:2.13624
[75]	train-mlogloss:2.12860	validation-mlogloss:2.13141
[76]	train-mlogloss:2.12368	validation-mlogloss:2.12681
[77]	train-mlogloss:2.11855	validation-mlogloss:2.12187
[78]	train-mlogloss:2.11368	validation-mlogloss:2.11719
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[104]	train-mlogloss:1.99990	validation-mlogloss:2.00799
[105]	train-mlogloss:1.99588	validation-mlogloss:2.00421
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[110]	train-mlogloss:1.97710	validation-mlogloss:1.98665
[111]	train-mlogloss:1.97330	validation-mlogloss:1.98303
[112]	train-mlogloss:1.96965	validation-mlogloss:1.97962
[113]	train-mlogloss:1.96595	validation-mlogloss:1.97609
[114]	train-mlogloss:1.96236	validation-mlogloss:1.97266
[115]	train-mlogloss:1.95892	validation-mlogloss:1.96931
[116]	train-mlogloss:1.95533	validation-mlogloss:1.96589
[117]	train-mlogloss:1.95191	validation-mlogloss:1.96270
[118]	train-mlogloss:1.94831	validation-mlogloss:1.95937
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[121]	train-mlogloss:1.93821	validation-mlogloss:1.94946
[122]	train-mlogloss:1.93490	validation-mlogloss:1.94623
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[124]	train-mlogloss:1.92811	validation-mlogloss:1.93949
[125]	train-mlogloss:1.92493	validation-mlogloss:1.93649
[126]	train-mlogloss:1.92156	validation-mlogloss:1.93330
[127]	train-mlogloss:1.91840	validation-mlogloss:1.93008
[128]	train-mlogloss:1.91517	validation-mlogloss:1.92692
[129]	train-mlogloss:1.91200	validation-mlogloss:1.92389
[130]	train-mlogloss:1.90889	validation-mlogloss:1.92102
[131]	train-mlogloss:1.90579	validation-mlogloss:1.91808
[132]	train-mlogloss:1.90264	validation-mlogloss:1.91513
[133]	train-mlogloss:1.89943	validation-mlogloss:1.91219
[134]	train-mlogloss:1.89642	validation-mlogloss:1.90932
[135]	train-mlogloss:1.89340	validation-mlogloss:1.90646
[136]	train-mlogloss:1.89030	validation-mlogloss:1.90349
[137]	train-mlogloss:1.88740	validation-mlogloss:1.90073
[138]	train-mlogloss:1.88431	validation-mlogloss:1.89780
[139]	train-mlogloss:1.88141	validation-mlogloss:1.89498
[140]	train-mlogloss:1.87865	validation-mlogloss:1.89246
[141]	train-mlogloss:1.87559	validation-mlogloss:1.88976
[142]	train-mlogloss:1.87278	validation-mlogloss:1.88701
[143]	train-mlogloss:1.86982	validation-mlogloss:1.88428
[144]	train-mlogloss:1.86678	validation-mlogloss:1.88159
[145]	train-mlogloss:1.86369	validation-mlogloss:1.87871
[146]	train-mlogloss:1.86102	validation-mlogloss:1.87617
[147]	train-mlogloss:1.85828	validation-mlogloss:1.87369
[148]	train-mlogloss:1.85569	validation-mlogloss:1.87120
[149]	train-mlogloss:1.85286	validation-mlogloss:1.86859
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[451]	train-mlogloss:1.48276	validation-mlogloss:1.53814
[452]	train-mlogloss:1.48223	validation-mlogloss:1.53781
[453]	train-mlogloss:1.48169	validation-mlogloss:1.53739
[454]	train-mlogloss:1.48115	validation-mlogloss:1.53694
[455]	train-mlogloss:1.48063	validation-mlogloss:1.53664
[456]	train-mlogloss:1.48009	validation-mlogloss:1.53631
[457]	train-mlogloss:1.47961	validation-mlogloss:1.53594
[458]	train-mlogloss:1.47904	validation-mlogloss:1.53550
[459]	train-mlogloss:1.47851	validation-mlogloss:1.53510
[460]	train-mlogloss:1.47795	validation-mlogloss:1.53467
[461]	train-mlogloss:1.47742	validation-mlogloss:1.53423
[462]	train-mlogloss:1.47695	validation-mlogloss:1.53379
[463]	train-mlogloss:1.47641	validation-mlogloss:1.53333
[464]	train-mlogloss:1.47586	validation-mlogloss:1.53295
[465]	train-mlogloss:1.47533	validation-mlogloss:1.53265
[466]	train-mlogloss:1.47481	validation-mlogloss:1.53230
[467]	train-mlogloss:1.47438	validation-mlogloss:1.53198
[468]	train-mlogloss:1.47385	validation-mlogloss:1.53149
[469]	train-mlogloss:1.47337	validation-mlogloss:1.53114
[470]	train-mlogloss:1.47289	validation-mlogloss:1.53086
[471]	train-mlogloss:1.47238	validation-mlogloss:1.53058
[472]	train-mlogloss:1.47186	validation-mlogloss:1.53028
[473]	train-mlogloss:1.47137	validation-mlogloss:1.52995
[474]	train-mlogloss:1.47088	validation-mlogloss:1.52964
[475]	train-mlogloss:1.47038	validation-mlogloss:1.52930
[476]	train-mlogloss:1.46987	validation-mlogloss:1.52893
[477]	train-mlogloss:1.46936	validation-mlogloss:1.52857
[478]	train-mlogloss:1.46887	validation-mlogloss:1.52822
[479]	train-mlogloss:1.46841	validation-mlogloss:1.52788
[480]	train-mlogloss:1.46805	validation-mlogloss:1.52754
[481]	train-mlogloss:1.46760	validation-mlogloss:1.52723
[482]	train-mlogloss:1.46712	validation-mlogloss:1.52684
[483]	train-mlogloss:1.46668	validation-mlogloss:1.52657
[484]	train-mlogloss:1.46616	validation-mlogloss:1.52618
[485]	train-mlogloss:1.46563	validation-mlogloss:1.52587
[486]	train-mlogloss:1.46515	validation-mlogloss:1.52541
[487]	train-mlogloss:1.46471	validation-mlogloss:1.52502
[488]	train-mlogloss:1.46425	validation-mlogloss:1.52457
[489]	train-mlogloss:1.46380	validation-mlogloss:1.52423
[490]	train-mlogloss:1.46335	validation-mlogloss:1.52403
[491]	train-mlogloss:1.46286	validation-mlogloss:1.52365
[492]	train-mlogloss:1.46234	validation-mlogloss:1.52321
[493]	train-mlogloss:1.46188	validation-mlogloss:1.52287
[494]	train-mlogloss:1.46145	validation-mlogloss:1.52268
[495]	train-mlogloss:1.46106	validation-mlogloss:1.52243
[496]	train-mlogloss:1.46061	validation-mlogloss:1.52196
[497]	train-mlogloss:1.46017	validation-mlogloss:1.52173
[498]	train-mlogloss:1.45973	validation-mlogloss:1.52134
[499]	train-mlogloss:1.45922	validation-mlogloss:1.52096
[500]	train-mlogloss:1.45876	validation-mlogloss:1.52068
[501]	train-mlogloss:1.45837	validation-mlogloss:1.52039
[502]	train-mlogloss:1.45791	validation-mlogloss:1.52006
[503]	train-mlogloss:1.45745	validation-mlogloss:1.51976
[504]	train-mlogloss:1.45702	validation-mlogloss:1.51957
[505]	train-mlogloss:1.45656	validation-mlogloss:1.51933
[506]	train-mlogloss:1.45611	validation-mlogloss:1.51901
[507]	train-mlogloss:1.45566	validation-mlogloss:1.51875
[508]	train-mlogloss:1.45523	validation-mlogloss:1.51842
[509]	train-mlogloss:1.45478	validation-mlogloss:1.51812
[510]	train-mlogloss:1.45436	validation-mlogloss:1.51790
[511]	train-mlogloss:1.45395	validation-mlogloss:1.51752
[512]	train-mlogloss:1.45348	validation-mlogloss:1.51721
[513]	train-mlogloss:1.45305	validation-mlogloss:1.51701
[514]	train-mlogloss:1.45258	validation-mlogloss:1.51672
[515]	train-mlogloss:1.45214	validation-mlogloss:1.51632
[516]	train-mlogloss:1.45165	validation-mlogloss:1.51606
[517]	train-mlogloss:1.45123	validation-mlogloss:1.51571
[518]	train-mlogloss:1.45082	validation-mlogloss:1.51549
[519]	train-mlogloss:1.45035	validation-mlogloss:1.51512
[520]	train-mlogloss:1.44997	validation-mlogloss:1.51475
[521]	train-mlogloss:1.44957	validation-mlogloss:1.51447
[522]	train-mlogloss:1.44916	validation-mlogloss:1.51418
[523]	train-mlogloss:1.44879	validation-mlogloss:1.51393
[524]	train-mlogloss:1.44834	validation-mlogloss:1.51365
[525]	train-mlogloss:1.44794	validation-mlogloss:1.51332
[526]	train-mlogloss:1.44755	validation-mlogloss:1.51304
[527]	train-mlogloss:1.44713	validation-mlogloss:1.51286
[528]	train-mlogloss:1.44672	validation-mlogloss:1.51257
[529]	train-mlogloss:1.44633	validation-mlogloss:1.51229
[530]	train-mlogloss:1.44593	validation-mlogloss:1.51205
[531]	train-mlogloss:1.44550	validation-mlogloss:1.51170
[532]	train-mlogloss:1.44508	validation-mlogloss:1.51145
[533]	train-mlogloss:1.44465	validation-mlogloss:1.51116
[534]	train-mlogloss:1.44422	validation-mlogloss:1.51088
[535]	train-mlogloss:1.44384	validation-mlogloss:1.51056
[536]	train-mlogloss:1.44350	validation-mlogloss:1.51029
[537]	train-mlogloss:1.44309	validation-mlogloss:1.51004
[538]	train-mlogloss:1.44274	validation-mlogloss:1.50986
[539]	train-mlogloss:1.44239	validation-mlogloss:1.50959
[540]	train-mlogloss:1.44199	validation-mlogloss:1.50931
[541]	train-mlogloss:1.44161	validation-mlogloss:1.50907
[542]	train-mlogloss:1.44121	validation-mlogloss:1.50881
[543]	train-mlogloss:1.44082	validation-mlogloss:1.50852
[544]	train-mlogloss:1.44037	validation-mlogloss:1.50834
[545]	train-mlogloss:1.43996	validation-mlogloss:1.50805
[546]	train-mlogloss:1.43963	validation-mlogloss:1.50784
[547]	train-mlogloss:1.43927	validation-mlogloss:1.50758
[548]	train-mlogloss:1.43889	validation-mlogloss:1.50733
[549]	train-mlogloss:1.43856	validation-mlogloss:1.50706
[550]	train-mlogloss:1.43824	validation-mlogloss:1.50681
[551]	train-mlogloss:1.43787	validation-mlogloss:1.50653
[552]	train-mlogloss:1.43746	validation-mlogloss:1.50628
[553]	train-mlogloss:1.43713	validation-mlogloss:1.50606
[554]	train-mlogloss:1.43674	validation-mlogloss:1.50591
[555]	train-mlogloss:1.43636	validation-mlogloss:1.50554
[556]	train-mlogloss:1.43600	validation-mlogloss:1.50528
[557]	train-mlogloss:1.43559	validation-mlogloss:1.50505
[558]	train-mlogloss:1.43521	validation-mlogloss:1.50476
[559]	train-mlogloss:1.43476	validation-mlogloss:1.50436
[560]	train-mlogloss:1.43440	validation-mlogloss:1.50407
[561]	train-mlogloss:1.43402	validation-mlogloss:1.50375
[562]	train-mlogloss:1.43361	validation-mlogloss:1.50361
[563]	train-mlogloss:1.43325	validation-mlogloss:1.50344
[564]	train-mlogloss:1.43286	validation-mlogloss:1.50317
[565]	train-mlogloss:1.43249	validation-mlogloss:1.50300
[566]	train-mlogloss:1.43220	validation-mlogloss:1.50280
[567]	train-mlogloss:1.43185	validation-mlogloss:1.50254
[568]	train-mlogloss:1.43150	validation-mlogloss:1.50227
[569]	train-mlogloss:1.43114	validation-mlogloss:1.50197
[570]	train-mlogloss:1.43080	validation-mlogloss:1.50179
[571]	train-mlogloss:1.43053	validation-mlogloss:1.50162
[572]	train-mlogloss:1.43016	validation-mlogloss:1.50130
[573]	train-mlogloss:1.42976	validation-mlogloss:1.50111
[574]	train-mlogloss:1.42940	validation-mlogloss:1.50086
[575]	train-mlogloss:1.42907	validation-mlogloss:1.50068
[576]	train-mlogloss:1.42875	validation-mlogloss:1.50058
[577]	train-mlogloss:1.42843	validation-mlogloss:1.50031
[578]	train-mlogloss:1.42806	validation-mlogloss:1.50007
[579]	train-mlogloss:1.42765	validation-mlogloss:1.49981
[580]	train-mlogloss:1.42734	validation-mlogloss:1.49960
[581]	train-mlogloss:1.42698	validation-mlogloss:1.49937
[582]	train-mlogloss:1.42666	validation-mlogloss:1.49915
[583]	train-mlogloss:1.42634	validation-mlogloss:1.49897
[584]	train-mlogloss:1.42604	validation-mlogloss:1.49877
[585]	train-mlogloss:1.42565	validation-mlogloss:1.49845
[586]	train-mlogloss:1.42530	validation-mlogloss:1.49836
[587]	train-mlogloss:1.42495	validation-mlogloss:1.49815
[588]	train-mlogloss:1.42456	validation-mlogloss:1.49797
[589]	train-mlogloss:1.42424	validation-mlogloss:1.49770
[590]	train-mlogloss:1.42389	validation-mlogloss:1.49750
[591]	train-mlogloss:1.42355	validation-mlogloss:1.49733
[592]	train-mlogloss:1.42323	validation-mlogloss:1.49718
[593]	train-mlogloss:1.42291	validation-mlogloss:1.49702
[594]	train-mlogloss:1.42263	validation-mlogloss:1.49686
[595]	train-mlogloss:1.42237	validation-mlogloss:1.49665
[596]	train-mlogloss:1.42205	validation-mlogloss:1.49645
[597]	train-mlogloss:1.42177	validation-mlogloss:1.49625
[598]	train-mlogloss:1.42146	validation-mlogloss:1.49608
[599]	train-mlogloss:1.42111	validation-mlogloss:1.49584
[600]	train-mlogloss:1.42080	validation-mlogloss:1.49563
[601]	train-mlogloss:1.42045	validation-mlogloss:1.49534
[602]	train-mlogloss:1.42016	validation-mlogloss:1.49510
[603]	train-mlogloss:1.41981	validation-mlogloss:1.49489
[604]	train-mlogloss:1.41947	validation-mlogloss:1.49470
[605]	train-mlogloss:1.41919	validation-mlogloss:1.49444
[606]	train-mlogloss:1.41882	validation-mlogloss:1.49418
[607]	train-mlogloss:1.41847	validation-mlogloss:1.49399
[608]	train-mlogloss:1.41814	validation-mlogloss:1.49372
[609]	train-mlogloss:1.41782	validation-mlogloss:1.49352
[610]	train-mlogloss:1.41743	validation-mlogloss:1.49332
[611]	train-mlogloss:1.41719	validation-mlogloss:1.49322
[612]	train-mlogloss:1.41689	validation-mlogloss:1.49308
[613]	train-mlogloss:1.41656	validation-mlogloss:1.49293
[614]	train-mlogloss:1.41622	validation-mlogloss:1.49275
[615]	train-mlogloss:1.41589	validation-mlogloss:1.49253
[616]	train-mlogloss:1.41562	validation-mlogloss:1.49225
[617]	train-mlogloss:1.41535	validation-mlogloss:1.49210
[618]	train-mlogloss:1.41505	validation-mlogloss:1.49196
[619]	train-mlogloss:1.41469	validation-mlogloss:1.49183
[620]	train-mlogloss:1.41441	validation-mlogloss:1.49164
[621]	train-mlogloss:1.41410	validation-mlogloss:1.49145
[622]	train-mlogloss:1.41379	validation-mlogloss:1.49119
[623]	train-mlogloss:1.41350	validation-mlogloss:1.49105
[624]	train-mlogloss:1.41318	validation-mlogloss:1.49084
[625]	train-mlogloss:1.41286	validation-mlogloss:1.49063
[626]	train-mlogloss:1.41252	validation-mlogloss:1.49047
[627]	train-mlogloss:1.41224	validation-mlogloss:1.49035
[628]	train-mlogloss:1.41184	validation-mlogloss:1.49012
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.75 [%]
Global accuracy score (test) = 43.55 [%]
Global F1 score (train) = 55.57 [%]
Global F1 score (test) = 42.59 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.21      0.20       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.39      0.34       184
       CAMINAR USUAL SPEED       0.27      0.14      0.18       184
            CAMINAR ZIGZAG       0.27      0.28      0.28       184
          DE PIE BARRIENDO       0.46      0.47      0.46       184
   DE PIE DOBLANDO TOALLAS       0.42      0.40      0.41       184
    DE PIE MOVIENDO LIBROS       0.37      0.39      0.38       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.28      0.23      0.25       184
         SENTADO USANDO PC       0.16      0.06      0.09       184
      SENTADO VIENDO LA TV       0.42      0.40      0.41       184
   SUBIR Y BAJAR ESCALERAS       0.58      0.73      0.65       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 7: 43.55 [%]
F1-score capturado en la ejecución 7: 42.59 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.76 [%]
Global accuracy score (test) = 43.66 [%]
Global F1 score (train) = 55.59 [%]
Global F1 score (test) = 42.58 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.19      0.20       184
 CAMINAR CON MÓVIL O LIBRO       0.33      0.43      0.37       184
       CAMINAR USUAL SPEED       0.23      0.13      0.17       184
            CAMINAR ZIGZAG       0.27      0.27      0.27       184
          DE PIE BARRIENDO       0.45      0.47      0.46       184
   DE PIE DOBLANDO TOALLAS       0.45      0.45      0.45       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.61      0.73       184
           SENTADO LEYENDO       0.27      0.22      0.24       184
         SENTADO USANDO PC       0.16      0.06      0.09       184
      SENTADO VIENDO LA TV       0.39      0.38      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.55      0.72      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 8: 43.66 [%]
F1-score capturado en la ejecución 8: 42.58 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.0 [%]
Global accuracy score (test) = 43.15 [%]
Global F1 score (train) = 55.83 [%]
Global F1 score (test) = 42.21 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.19      0.20       184
 CAMINAR CON MÓVIL O LIBRO       0.32      0.42      0.36       184
       CAMINAR USUAL SPEED       0.25      0.14      0.18       184
            CAMINAR ZIGZAG       0.29      0.32      0.31       184
          DE PIE BARRIENDO       0.46      0.48      0.47       184
   DE PIE DOBLANDO TOALLAS       0.44      0.39      0.41       184
    DE PIE MOVIENDO LIBROS       0.38      0.39      0.38       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.41      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.27      0.23      0.25       184
         SENTADO USANDO PC       0.15      0.06      0.09       184
      SENTADO VIENDO LA TV       0.35      0.31      0.33       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.71      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.43      0.42      2737
              weighted avg       0.43      0.43      0.42      2737


Accuracy capturado en la ejecución 9: 43.15 [%]
F1-score capturado en la ejecución 9: 42.21 [%]

=== EJECUCIÓN 10 ===

--- TRAIN (ejecución 10) ---

--- TEST (ejecución 10) ---
2025-11-07 10:23:18.598162: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:23:18.609206: 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 00:00:1762507398.622311 2383073 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507398.626301 2383073 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507398.636786 2383073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507398.636804 2383073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507398.636807 2383073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507398.636808 2383073 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:23:18.639877: 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:2.69728	validation-mlogloss:2.69712
[1]	train-mlogloss:2.68680	validation-mlogloss:2.68635
[2]	train-mlogloss:2.67593	validation-mlogloss:2.67539
[3]	train-mlogloss:2.66505	validation-mlogloss:2.66445
[4]	train-mlogloss:2.65493	validation-mlogloss:2.65447
[5]	train-mlogloss:2.64485	validation-mlogloss:2.64453
[6]	train-mlogloss:2.63448	validation-mlogloss:2.63418
[7]	train-mlogloss:2.62441	validation-mlogloss:2.62406
[8]	train-mlogloss:2.61444	validation-mlogloss:2.61417
[9]	train-mlogloss:2.60449	validation-mlogloss:2.60417
[10]	train-mlogloss:2.59491	validation-mlogloss:2.59453
[11]	train-mlogloss:2.58550	validation-mlogloss:2.58491
[12]	train-mlogloss:2.57573	validation-mlogloss:2.57500
[13]	train-mlogloss:2.56601	validation-mlogloss:2.56518
[14]	train-mlogloss:2.55675	validation-mlogloss:2.55599
[15]	train-mlogloss:2.54758	validation-mlogloss:2.54679
[16]	train-mlogloss:2.53826	validation-mlogloss:2.53749
[17]	train-mlogloss:2.52899	validation-mlogloss:2.52851
[18]	train-mlogloss:2.51989	validation-mlogloss:2.51937
[19]	train-mlogloss:2.51068	validation-mlogloss:2.51006
[20]	train-mlogloss:2.50153	validation-mlogloss:2.50088
[21]	train-mlogloss:2.49285	validation-mlogloss:2.49231
[22]	train-mlogloss:2.48412	validation-mlogloss:2.48355
[23]	train-mlogloss:2.47537	validation-mlogloss:2.47475
[24]	train-mlogloss:2.46678	validation-mlogloss:2.46608
[25]	train-mlogloss:2.45854	validation-mlogloss:2.45794
[26]	train-mlogloss:2.45027	validation-mlogloss:2.44958
[27]	train-mlogloss:2.44205	validation-mlogloss:2.44118
[28]	train-mlogloss:2.43383	validation-mlogloss:2.43306
[29]	train-mlogloss:2.42581	validation-mlogloss:2.42490
[30]	train-mlogloss:2.41741	validation-mlogloss:2.41663
[31]	train-mlogloss:2.40970	validation-mlogloss:2.40880
[32]	train-mlogloss:2.40197	validation-mlogloss:2.40088
[33]	train-mlogloss:2.39383	validation-mlogloss:2.39282
[34]	train-mlogloss:2.38608	validation-mlogloss:2.38512
[35]	train-mlogloss:2.37846	validation-mlogloss:2.37746
[36]	train-mlogloss:2.37085	validation-mlogloss:2.36979
[37]	train-mlogloss:2.36312	validation-mlogloss:2.36226
[38]	train-mlogloss:2.35601	validation-mlogloss:2.35510
[39]	train-mlogloss:2.34880	validation-mlogloss:2.34799
[40]	train-mlogloss:2.34145	validation-mlogloss:2.34070
[41]	train-mlogloss:2.33416	validation-mlogloss:2.33349
[42]	train-mlogloss:2.32705	validation-mlogloss:2.32620
[43]	train-mlogloss:2.31992	validation-mlogloss:2.31913
[44]	train-mlogloss:2.31283	validation-mlogloss:2.31207
[45]	train-mlogloss:2.30613	validation-mlogloss:2.30547
[46]	train-mlogloss:2.29935	validation-mlogloss:2.29894
[47]	train-mlogloss:2.29276	validation-mlogloss:2.29252
[48]	train-mlogloss:2.28597	validation-mlogloss:2.28597
[49]	train-mlogloss:2.27958	validation-mlogloss:2.27975
[50]	train-mlogloss:2.27287	validation-mlogloss:2.27306
[51]	train-mlogloss:2.26650	validation-mlogloss:2.26671
[52]	train-mlogloss:2.26022	validation-mlogloss:2.26038
[53]	train-mlogloss:2.25408	validation-mlogloss:2.25421
[54]	train-mlogloss:2.24827	validation-mlogloss:2.24840
[55]	train-mlogloss:2.24209	validation-mlogloss:2.24246
[56]	train-mlogloss:2.23606	validation-mlogloss:2.23661
[57]	train-mlogloss:2.23002	validation-mlogloss:2.23069
[58]	train-mlogloss:2.22416	validation-mlogloss:2.22491
[59]	train-mlogloss:2.21815	validation-mlogloss:2.21887
[60]	train-mlogloss:2.21197	validation-mlogloss:2.21298
[61]	train-mlogloss:2.20632	validation-mlogloss:2.20727
[62]	train-mlogloss:2.20052	validation-mlogloss:2.20144
[63]	train-mlogloss:2.19466	validation-mlogloss:2.19587
[64]	train-mlogloss:2.18887	validation-mlogloss:2.19015
[65]	train-mlogloss:2.18330	validation-mlogloss:2.18487
[66]	train-mlogloss:2.17774	validation-mlogloss:2.17963
[67]	train-mlogloss:2.17205	validation-mlogloss:2.17412
[68]	train-mlogloss:2.16638	validation-mlogloss:2.16870
[69]	train-mlogloss:2.16104	validation-mlogloss:2.16353
[70]	train-mlogloss:2.15572	validation-mlogloss:2.15849
[71]	train-mlogloss:2.15035	validation-mlogloss:2.15307
[72]	train-mlogloss:2.14508	validation-mlogloss:2.14786
[73]	train-mlogloss:2.13960	validation-mlogloss:2.14228
[74]	train-mlogloss:2.13414	validation-mlogloss:2.13689
[75]	train-mlogloss:2.12910	validation-mlogloss:2.13188
[76]	train-mlogloss:2.12391	validation-mlogloss:2.12684
[77]	train-mlogloss:2.11895	validation-mlogloss:2.12207
[78]	train-mlogloss:2.11402	validation-mlogloss:2.11749
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[81]	train-mlogloss:2.09955	validation-mlogloss:2.10311
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[83]	train-mlogloss:2.09020	validation-mlogloss:2.09418
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[92]	train-mlogloss:2.04956	validation-mlogloss:2.05443
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[94]	train-mlogloss:2.04110	validation-mlogloss:2.04611
[95]	train-mlogloss:2.03686	validation-mlogloss:2.04195
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[98]	train-mlogloss:2.02444	validation-mlogloss:2.02985
[99]	train-mlogloss:2.02028	validation-mlogloss:2.02596
[100]	train-mlogloss:2.01634	validation-mlogloss:2.02211
[101]	train-mlogloss:2.01233	validation-mlogloss:2.01833
[102]	train-mlogloss:2.00818	validation-mlogloss:2.01444
[103]	train-mlogloss:2.00439	validation-mlogloss:2.01071
[104]	train-mlogloss:2.00045	validation-mlogloss:2.00682
[105]	train-mlogloss:1.99647	validation-mlogloss:2.00314
[106]	train-mlogloss:1.99273	validation-mlogloss:1.99955
[107]	train-mlogloss:1.98889	validation-mlogloss:1.99604
[108]	train-mlogloss:1.98527	validation-mlogloss:1.99256
[109]	train-mlogloss:1.98149	validation-mlogloss:1.98881
[110]	train-mlogloss:1.97763	validation-mlogloss:1.98522
[111]	train-mlogloss:1.97389	validation-mlogloss:1.98161
[112]	train-mlogloss:1.97024	validation-mlogloss:1.97815
[113]	train-mlogloss:1.96661	validation-mlogloss:1.97474
[114]	train-mlogloss:1.96307	validation-mlogloss:1.97130
[115]	train-mlogloss:1.95965	validation-mlogloss:1.96815
[116]	train-mlogloss:1.95599	validation-mlogloss:1.96448
[117]	train-mlogloss:1.95256	validation-mlogloss:1.96137
[118]	train-mlogloss:1.94910	validation-mlogloss:1.95820
[119]	train-mlogloss:1.94549	validation-mlogloss:1.95503
[120]	train-mlogloss:1.94208	validation-mlogloss:1.95168
[121]	train-mlogloss:1.93868	validation-mlogloss:1.94842
[122]	train-mlogloss:1.93514	validation-mlogloss:1.94502
[123]	train-mlogloss:1.93178	validation-mlogloss:1.94196
[124]	train-mlogloss:1.92844	validation-mlogloss:1.93866
[125]	train-mlogloss:1.92526	validation-mlogloss:1.93561
[126]	train-mlogloss:1.92196	validation-mlogloss:1.93248
[127]	train-mlogloss:1.91854	validation-mlogloss:1.92933
[128]	train-mlogloss:1.91539	validation-mlogloss:1.92629
[129]	train-mlogloss:1.91210	validation-mlogloss:1.92342
[130]	train-mlogloss:1.90882	validation-mlogloss:1.92038
[131]	train-mlogloss:1.90560	validation-mlogloss:1.91722
[132]	train-mlogloss:1.90254	validation-mlogloss:1.91438
[133]	train-mlogloss:1.89950	validation-mlogloss:1.91147
[134]	train-mlogloss:1.89649	validation-mlogloss:1.90837
[135]	train-mlogloss:1.89345	validation-mlogloss:1.90547
[136]	train-mlogloss:1.89030	validation-mlogloss:1.90257
[137]	train-mlogloss:1.88727	validation-mlogloss:1.89970
[138]	train-mlogloss:1.88451	validation-mlogloss:1.89706
[139]	train-mlogloss:1.88171	validation-mlogloss:1.89423
[140]	train-mlogloss:1.87874	validation-mlogloss:1.89136
[141]	train-mlogloss:1.87589	validation-mlogloss:1.88861
[142]	train-mlogloss:1.87304	validation-mlogloss:1.88589
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[144]	train-mlogloss:1.86740	validation-mlogloss:1.88037
[145]	train-mlogloss:1.86449	validation-mlogloss:1.87752
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[150]	train-mlogloss:1.85049	validation-mlogloss:1.86437
[151]	train-mlogloss:1.84791	validation-mlogloss:1.86194
[152]	train-mlogloss:1.84503	validation-mlogloss:1.85926
[153]	train-mlogloss:1.84241	validation-mlogloss:1.85683
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[160]	train-mlogloss:1.82450	validation-mlogloss:1.83978
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[191]	train-mlogloss:1.75357	validation-mlogloss:1.77304
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[579]	train-mlogloss:1.42761	validation-mlogloss:1.49778
[580]	train-mlogloss:1.42727	validation-mlogloss:1.49755
[581]	train-mlogloss:1.42696	validation-mlogloss:1.49724
[582]	train-mlogloss:1.42661	validation-mlogloss:1.49710
[583]	train-mlogloss:1.42625	validation-mlogloss:1.49691
[584]	train-mlogloss:1.42594	validation-mlogloss:1.49676
[585]	train-mlogloss:1.42562	validation-mlogloss:1.49663
[586]	train-mlogloss:1.42528	validation-mlogloss:1.49633
[587]	train-mlogloss:1.42498	validation-mlogloss:1.49612
[588]	train-mlogloss:1.42462	validation-mlogloss:1.49595
[589]	train-mlogloss:1.42426	validation-mlogloss:1.49567
[590]	train-mlogloss:1.42392	validation-mlogloss:1.49548
[591]	train-mlogloss:1.42356	validation-mlogloss:1.49530
[592]	train-mlogloss:1.42317	validation-mlogloss:1.49516
[593]	train-mlogloss:1.42290	validation-mlogloss:1.49504
[594]	train-mlogloss:1.42253	validation-mlogloss:1.49486
[595]	train-mlogloss:1.42218	validation-mlogloss:1.49460
[596]	train-mlogloss:1.42179	validation-mlogloss:1.49429
[597]	train-mlogloss:1.42146	validation-mlogloss:1.49422
[598]	train-mlogloss:1.42119	validation-mlogloss:1.49412
[599]	train-mlogloss:1.42088	validation-mlogloss:1.49390
[600]	train-mlogloss:1.42059	validation-mlogloss:1.49368
[601]	train-mlogloss:1.42028	validation-mlogloss:1.49345
[602]	train-mlogloss:1.41995	validation-mlogloss:1.49317
[603]	train-mlogloss:1.41959	validation-mlogloss:1.49299
[604]	train-mlogloss:1.41926	validation-mlogloss:1.49278
[605]	train-mlogloss:1.41888	validation-mlogloss:1.49259
[606]	train-mlogloss:1.41861	validation-mlogloss:1.49243
[607]	train-mlogloss:1.41829	validation-mlogloss:1.49225
[608]	train-mlogloss:1.41794	validation-mlogloss:1.49205
[609]	train-mlogloss:1.41764	validation-mlogloss:1.49183
[610]	train-mlogloss:1.41731	validation-mlogloss:1.49154
[611]	train-mlogloss:1.41701	validation-mlogloss:1.49134
[612]	train-mlogloss:1.41670	validation-mlogloss:1.49105
[613]	train-mlogloss:1.41636	validation-mlogloss:1.49078
[614]	train-mlogloss:1.41600	validation-mlogloss:1.49053
[615]	train-mlogloss:1.41561	validation-mlogloss:1.49039
[616]	train-mlogloss:1.41530	validation-mlogloss:1.49026
[617]	train-mlogloss:1.41495	validation-mlogloss:1.49009
[618]	train-mlogloss:1.41467	validation-mlogloss:1.48994
[619]	train-mlogloss:1.41432	validation-mlogloss:1.48969
[620]	train-mlogloss:1.41399	validation-mlogloss:1.48958
[621]	train-mlogloss:1.41366	validation-mlogloss:1.48939
[622]	train-mlogloss:1.41333	validation-mlogloss:1.48925
[623]	train-mlogloss:1.41308	validation-mlogloss:1.48910
[624]	train-mlogloss:1.41277	validation-mlogloss:1.48888
[625]	train-mlogloss:1.41248	validation-mlogloss:1.48860
[626]	train-mlogloss:1.41219	validation-mlogloss:1.48850
[627]	train-mlogloss:1.41185	validation-mlogloss:1.48825
[628]	train-mlogloss:1.41163	validation-mlogloss:1.48810
Saved model to disk.
2025-11-07 10:23:45.340061: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:23:45.351691: 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 00:00:1762507425.365953 2383499 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507425.370346 2383499 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507425.380920 2383499 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507425.380941 2383499 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507425.380943 2383499 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507425.380945 2383499 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:23:45.384179: 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:2.69752	validation-mlogloss:2.69747
[1]	train-mlogloss:2.68691	validation-mlogloss:2.68645
[2]	train-mlogloss:2.67614	validation-mlogloss:2.67569
[3]	train-mlogloss:2.66539	validation-mlogloss:2.66483
[4]	train-mlogloss:2.65499	validation-mlogloss:2.65459
[5]	train-mlogloss:2.64474	validation-mlogloss:2.64426
[6]	train-mlogloss:2.63515	validation-mlogloss:2.63429
[7]	train-mlogloss:2.62551	validation-mlogloss:2.62434
[8]	train-mlogloss:2.61571	validation-mlogloss:2.61448
[9]	train-mlogloss:2.60547	validation-mlogloss:2.60436
[10]	train-mlogloss:2.59536	validation-mlogloss:2.59425
[11]	train-mlogloss:2.58593	validation-mlogloss:2.58475
[12]	train-mlogloss:2.57640	validation-mlogloss:2.57522
[13]	train-mlogloss:2.56708	validation-mlogloss:2.56587
[14]	train-mlogloss:2.55733	validation-mlogloss:2.55609
[15]	train-mlogloss:2.54755	validation-mlogloss:2.54611
[16]	train-mlogloss:2.53852	validation-mlogloss:2.53699
[17]	train-mlogloss:2.52951	validation-mlogloss:2.52773
[18]	train-mlogloss:2.51991	validation-mlogloss:2.51801
[19]	train-mlogloss:2.51116	validation-mlogloss:2.50928
[20]	train-mlogloss:2.50224	validation-mlogloss:2.50044
[21]	train-mlogloss:2.49341	validation-mlogloss:2.49150
[22]	train-mlogloss:2.48435	validation-mlogloss:2.48238
[23]	train-mlogloss:2.47554	validation-mlogloss:2.47379
[24]	train-mlogloss:2.46679	validation-mlogloss:2.46509
[25]	train-mlogloss:2.45813	validation-mlogloss:2.45653
[26]	train-mlogloss:2.44951	validation-mlogloss:2.44783
[27]	train-mlogloss:2.44081	validation-mlogloss:2.43932
[28]	train-mlogloss:2.43267	validation-mlogloss:2.43114
[29]	train-mlogloss:2.42471	validation-mlogloss:2.42329
[30]	train-mlogloss:2.41653	validation-mlogloss:2.41497
[31]	train-mlogloss:2.40874	validation-mlogloss:2.40709
[32]	train-mlogloss:2.40066	validation-mlogloss:2.39905
[33]	train-mlogloss:2.39312	validation-mlogloss:2.39162
[34]	train-mlogloss:2.38530	validation-mlogloss:2.38404
[35]	train-mlogloss:2.37775	validation-mlogloss:2.37639
[36]	train-mlogloss:2.36994	validation-mlogloss:2.36879
[37]	train-mlogloss:2.36224	validation-mlogloss:2.36120
[38]	train-mlogloss:2.35484	validation-mlogloss:2.35353
[39]	train-mlogloss:2.34748	validation-mlogloss:2.34587
[40]	train-mlogloss:2.34044	validation-mlogloss:2.33859
[41]	train-mlogloss:2.33327	validation-mlogloss:2.33142
[42]	train-mlogloss:2.32642	validation-mlogloss:2.32441
[43]	train-mlogloss:2.31955	validation-mlogloss:2.31774
[44]	train-mlogloss:2.31273	validation-mlogloss:2.31087
[45]	train-mlogloss:2.30594	validation-mlogloss:2.30415
[46]	train-mlogloss:2.29906	validation-mlogloss:2.29725
[47]	train-mlogloss:2.29261	validation-mlogloss:2.29081
[48]	train-mlogloss:2.28576	validation-mlogloss:2.28414
[49]	train-mlogloss:2.27909	validation-mlogloss:2.27761
[50]	train-mlogloss:2.27268	validation-mlogloss:2.27117
[51]	train-mlogloss:2.26637	validation-mlogloss:2.26483
[52]	train-mlogloss:2.26031	validation-mlogloss:2.25896
[53]	train-mlogloss:2.25394	validation-mlogloss:2.25249
[54]	train-mlogloss:2.24735	validation-mlogloss:2.24619
[55]	train-mlogloss:2.24112	validation-mlogloss:2.24006
[56]	train-mlogloss:2.23505	validation-mlogloss:2.23413
[57]	train-mlogloss:2.22896	validation-mlogloss:2.22804
[58]	train-mlogloss:2.22275	validation-mlogloss:2.22174
[59]	train-mlogloss:2.21702	validation-mlogloss:2.21609
[60]	train-mlogloss:2.21082	validation-mlogloss:2.20998
[61]	train-mlogloss:2.20516	validation-mlogloss:2.20470
[62]	train-mlogloss:2.19899	validation-mlogloss:2.19865
[63]	train-mlogloss:2.19310	validation-mlogloss:2.19289
[64]	train-mlogloss:2.18725	validation-mlogloss:2.18724
[65]	train-mlogloss:2.18179	validation-mlogloss:2.18171
[66]	train-mlogloss:2.17613	validation-mlogloss:2.17619
[67]	train-mlogloss:2.17058	validation-mlogloss:2.17062
[68]	train-mlogloss:2.16510	validation-mlogloss:2.16492
[69]	train-mlogloss:2.15968	validation-mlogloss:2.15987
[70]	train-mlogloss:2.15434	validation-mlogloss:2.15446
[71]	train-mlogloss:2.14896	validation-mlogloss:2.14908
[72]	train-mlogloss:2.14363	validation-mlogloss:2.14400
[73]	train-mlogloss:2.13821	validation-mlogloss:2.13868
[74]	train-mlogloss:2.13318	validation-mlogloss:2.13397
[75]	train-mlogloss:2.12818	validation-mlogloss:2.12941
[76]	train-mlogloss:2.12303	validation-mlogloss:2.12442
[77]	train-mlogloss:2.11809	validation-mlogloss:2.11949
[78]	train-mlogloss:2.11333	validation-mlogloss:2.11478
[79]	train-mlogloss:2.10831	validation-mlogloss:2.10988
[80]	train-mlogloss:2.10351	validation-mlogloss:2.10504
[81]	train-mlogloss:2.09880	validation-mlogloss:2.10035
[82]	train-mlogloss:2.09398	validation-mlogloss:2.09574
[83]	train-mlogloss:2.08915	validation-mlogloss:2.09103
[84]	train-mlogloss:2.08462	validation-mlogloss:2.08668
[85]	train-mlogloss:2.07982	validation-mlogloss:2.08215
[86]	train-mlogloss:2.07511	validation-mlogloss:2.07757
[87]	train-mlogloss:2.07064	validation-mlogloss:2.07323
[88]	train-mlogloss:2.06621	validation-mlogloss:2.06907
[89]	train-mlogloss:2.06173	validation-mlogloss:2.06478
[90]	train-mlogloss:2.05721	validation-mlogloss:2.06056
[91]	train-mlogloss:2.05282	validation-mlogloss:2.05631
[92]	train-mlogloss:2.04864	validation-mlogloss:2.05210
[93]	train-mlogloss:2.04444	validation-mlogloss:2.04795
[94]	train-mlogloss:2.04015	validation-mlogloss:2.04373
[95]	train-mlogloss:2.03603	validation-mlogloss:2.03947
[96]	train-mlogloss:2.03179	validation-mlogloss:2.03559
[97]	train-mlogloss:2.02781	validation-mlogloss:2.03188
[98]	train-mlogloss:2.02369	validation-mlogloss:2.02788
[99]	train-mlogloss:2.01953	validation-mlogloss:2.02403
[100]	train-mlogloss:2.01547	validation-mlogloss:2.02006
[101]	train-mlogloss:2.01143	validation-mlogloss:2.01620
[102]	train-mlogloss:2.00733	validation-mlogloss:2.01239
[103]	train-mlogloss:2.00353	validation-mlogloss:2.00877
[104]	train-mlogloss:1.99967	validation-mlogloss:2.00515
[105]	train-mlogloss:1.99582	validation-mlogloss:2.00139
[106]	train-mlogloss:1.99223	validation-mlogloss:1.99817
[107]	train-mlogloss:1.98840	validation-mlogloss:1.99461
[108]	train-mlogloss:1.98464	validation-mlogloss:1.99096
[109]	train-mlogloss:1.98090	validation-mlogloss:1.98750
[110]	train-mlogloss:1.97711	validation-mlogloss:1.98390
[111]	train-mlogloss:1.97328	validation-mlogloss:1.98028
[112]	train-mlogloss:1.96956	validation-mlogloss:1.97679
[113]	train-mlogloss:1.96592	validation-mlogloss:1.97322
[114]	train-mlogloss:1.96229	validation-mlogloss:1.96988
[115]	train-mlogloss:1.95886	validation-mlogloss:1.96665
[116]	train-mlogloss:1.95532	validation-mlogloss:1.96326
[117]	train-mlogloss:1.95182	validation-mlogloss:1.95978
[118]	train-mlogloss:1.94828	validation-mlogloss:1.95636
[119]	train-mlogloss:1.94469	validation-mlogloss:1.95299
[120]	train-mlogloss:1.94145	validation-mlogloss:1.94997
[121]	train-mlogloss:1.93813	validation-mlogloss:1.94701
[122]	train-mlogloss:1.93474	validation-mlogloss:1.94388
[123]	train-mlogloss:1.93146	validation-mlogloss:1.94102
[124]	train-mlogloss:1.92790	validation-mlogloss:1.93765
[125]	train-mlogloss:1.92466	validation-mlogloss:1.93453
[126]	train-mlogloss:1.92148	validation-mlogloss:1.93155
[127]	train-mlogloss:1.91816	validation-mlogloss:1.92854
[128]	train-mlogloss:1.91515	validation-mlogloss:1.92571
[129]	train-mlogloss:1.91193	validation-mlogloss:1.92267
[130]	train-mlogloss:1.90888	validation-mlogloss:1.91981
[131]	train-mlogloss:1.90555	validation-mlogloss:1.91660
[132]	train-mlogloss:1.90239	validation-mlogloss:1.91369
[133]	train-mlogloss:1.89934	validation-mlogloss:1.91074
[134]	train-mlogloss:1.89634	validation-mlogloss:1.90774
[135]	train-mlogloss:1.89323	validation-mlogloss:1.90477
[136]	train-mlogloss:1.89011	validation-mlogloss:1.90174
[137]	train-mlogloss:1.88710	validation-mlogloss:1.89881
[138]	train-mlogloss:1.88397	validation-mlogloss:1.89588
[139]	train-mlogloss:1.88091	validation-mlogloss:1.89310
[140]	train-mlogloss:1.87795	validation-mlogloss:1.89024
[141]	train-mlogloss:1.87474	validation-mlogloss:1.88706
[142]	train-mlogloss:1.87185	validation-mlogloss:1.88420
[143]	train-mlogloss:1.86910	validation-mlogloss:1.88152
[144]	train-mlogloss:1.86625	validation-mlogloss:1.87876
[145]	train-mlogloss:1.86346	validation-mlogloss:1.87619
[146]	train-mlogloss:1.86067	validation-mlogloss:1.87350
[147]	train-mlogloss:1.85784	validation-mlogloss:1.87091
[148]	train-mlogloss:1.85499	validation-mlogloss:1.86817
[149]	train-mlogloss:1.85221	validation-mlogloss:1.86565
[150]	train-mlogloss:1.84952	validation-mlogloss:1.86299
[151]	train-mlogloss:1.84678	validation-mlogloss:1.86045
[152]	train-mlogloss:1.84403	validation-mlogloss:1.85783
[153]	train-mlogloss:1.84118	validation-mlogloss:1.85505
[154]	train-mlogloss:1.83864	validation-mlogloss:1.85275
[155]	train-mlogloss:1.83604	validation-mlogloss:1.85014
[156]	train-mlogloss:1.83356	validation-mlogloss:1.84785
[157]	train-mlogloss:1.83111	validation-mlogloss:1.84560
[158]	train-mlogloss:1.82856	validation-mlogloss:1.84326
[159]	train-mlogloss:1.82604	validation-mlogloss:1.84075
[160]	train-mlogloss:1.82358	validation-mlogloss:1.83843
[161]	train-mlogloss:1.82105	validation-mlogloss:1.83598
[162]	train-mlogloss:1.81846	validation-mlogloss:1.83346
[163]	train-mlogloss:1.81598	validation-mlogloss:1.83116
[164]	train-mlogloss:1.81358	validation-mlogloss:1.82892
[165]	train-mlogloss:1.81099	validation-mlogloss:1.82657
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[168]	train-mlogloss:1.80389	validation-mlogloss:1.81987
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[171]	train-mlogloss:1.79669	validation-mlogloss:1.81336
[172]	train-mlogloss:1.79437	validation-mlogloss:1.81129
[173]	train-mlogloss:1.79193	validation-mlogloss:1.80896
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[177]	train-mlogloss:1.78294	validation-mlogloss:1.80079
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[181]	train-mlogloss:1.77404	validation-mlogloss:1.79228
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[189]	train-mlogloss:1.75737	validation-mlogloss:1.77683
[190]	train-mlogloss:1.75536	validation-mlogloss:1.77498
[191]	train-mlogloss:1.75342	validation-mlogloss:1.77305
[192]	train-mlogloss:1.75133	validation-mlogloss:1.77114
[193]	train-mlogloss:1.74937	validation-mlogloss:1.76936
[194]	train-mlogloss:1.74732	validation-mlogloss:1.76731
[195]	train-mlogloss:1.74532	validation-mlogloss:1.76536
[196]	train-mlogloss:1.74336	validation-mlogloss:1.76340
[197]	train-mlogloss:1.74144	validation-mlogloss:1.76151
[198]	train-mlogloss:1.73952	validation-mlogloss:1.75968
[199]	train-mlogloss:1.73762	validation-mlogloss:1.75777
[200]	train-mlogloss:1.73579	validation-mlogloss:1.75602
[201]	train-mlogloss:1.73399	validation-mlogloss:1.75430
[202]	train-mlogloss:1.73206	validation-mlogloss:1.75252
[203]	train-mlogloss:1.73020	validation-mlogloss:1.75088
[204]	train-mlogloss:1.72844	validation-mlogloss:1.74922
[205]	train-mlogloss:1.72659	validation-mlogloss:1.74756
[206]	train-mlogloss:1.72469	validation-mlogloss:1.74593
[207]	train-mlogloss:1.72286	validation-mlogloss:1.74420
[208]	train-mlogloss:1.72111	validation-mlogloss:1.74260
[209]	train-mlogloss:1.71930	validation-mlogloss:1.74101
[210]	train-mlogloss:1.71746	validation-mlogloss:1.73927
[211]	train-mlogloss:1.71566	validation-mlogloss:1.73767
[212]	train-mlogloss:1.71403	validation-mlogloss:1.73603
[213]	train-mlogloss:1.71229	validation-mlogloss:1.73443
[214]	train-mlogloss:1.71052	validation-mlogloss:1.73286
[215]	train-mlogloss:1.70868	validation-mlogloss:1.73117
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[218]	train-mlogloss:1.70362	validation-mlogloss:1.72664
[219]	train-mlogloss:1.70194	validation-mlogloss:1.72515
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[221]	train-mlogloss:1.69858	validation-mlogloss:1.72191
[222]	train-mlogloss:1.69698	validation-mlogloss:1.72046
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[597]	train-mlogloss:1.42291	validation-mlogloss:1.49387
[598]	train-mlogloss:1.42255	validation-mlogloss:1.49367
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[600]	train-mlogloss:1.42189	validation-mlogloss:1.49328
[601]	train-mlogloss:1.42159	validation-mlogloss:1.49303
[602]	train-mlogloss:1.42124	validation-mlogloss:1.49284
[603]	train-mlogloss:1.42090	validation-mlogloss:1.49257
[604]	train-mlogloss:1.42058	validation-mlogloss:1.49234
[605]	train-mlogloss:1.42029	validation-mlogloss:1.49216
[606]	train-mlogloss:1.42001	validation-mlogloss:1.49200
[607]	train-mlogloss:1.41969	validation-mlogloss:1.49172
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[610]	train-mlogloss:1.41877	validation-mlogloss:1.49099
[611]	train-mlogloss:1.41849	validation-mlogloss:1.49080
[612]	train-mlogloss:1.41820	validation-mlogloss:1.49060
[613]	train-mlogloss:1.41786	validation-mlogloss:1.49040
[614]	train-mlogloss:1.41754	validation-mlogloss:1.49017
[615]	train-mlogloss:1.41725	validation-mlogloss:1.49007
[616]	train-mlogloss:1.41694	validation-mlogloss:1.48985
[617]	train-mlogloss:1.41663	validation-mlogloss:1.48974
[618]	train-mlogloss:1.41628	validation-mlogloss:1.48962
[619]	train-mlogloss:1.41598	validation-mlogloss:1.48950
[620]	train-mlogloss:1.41571	validation-mlogloss:1.48935
[621]	train-mlogloss:1.41534	validation-mlogloss:1.48918
[622]	train-mlogloss:1.41503	validation-mlogloss:1.48901
[623]	train-mlogloss:1.41470	validation-mlogloss:1.48882
[624]	train-mlogloss:1.41445	validation-mlogloss:1.48862
[625]	train-mlogloss:1.41416	validation-mlogloss:1.48845
[626]	train-mlogloss:1.41391	validation-mlogloss:1.48823
[627]	train-mlogloss:1.41359	validation-mlogloss:1.48802
[628]	train-mlogloss:1.41329	validation-mlogloss:1.48780
Saved model to disk.
2025-11-07 10:24:11.873414: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:24:11.884812: 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 00:00:1762507451.898520 2383939 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507451.902470 2383939 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507451.912434 2383939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507451.912455 2383939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507451.912465 2383939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507451.912466 2383939 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:24:11.915601: 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:2.68680	validation-mlogloss:2.68643
[2]	train-mlogloss:2.67658	validation-mlogloss:2.67626
[3]	train-mlogloss:2.66620	validation-mlogloss:2.66577
[4]	train-mlogloss:2.65566	validation-mlogloss:2.65530
[5]	train-mlogloss:2.64536	validation-mlogloss:2.64508
[6]	train-mlogloss:2.63509	validation-mlogloss:2.63473
[7]	train-mlogloss:2.62490	validation-mlogloss:2.62446
[8]	train-mlogloss:2.61465	validation-mlogloss:2.61418
[9]	train-mlogloss:2.60554	validation-mlogloss:2.60507
[10]	train-mlogloss:2.59593	validation-mlogloss:2.59549
[11]	train-mlogloss:2.58646	validation-mlogloss:2.58594
[12]	train-mlogloss:2.57642	validation-mlogloss:2.57583
[13]	train-mlogloss:2.56662	validation-mlogloss:2.56584
[14]	train-mlogloss:2.55697	validation-mlogloss:2.55603
[15]	train-mlogloss:2.54772	validation-mlogloss:2.54648
[16]	train-mlogloss:2.53843	validation-mlogloss:2.53693
[17]	train-mlogloss:2.52966	validation-mlogloss:2.52827
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[19]	train-mlogloss:2.51109	validation-mlogloss:2.50981
[20]	train-mlogloss:2.50215	validation-mlogloss:2.50090
[21]	train-mlogloss:2.49330	validation-mlogloss:2.49200
[22]	train-mlogloss:2.48455	validation-mlogloss:2.48320
[23]	train-mlogloss:2.47569	validation-mlogloss:2.47441
[24]	train-mlogloss:2.46685	validation-mlogloss:2.46547
[25]	train-mlogloss:2.45870	validation-mlogloss:2.45708
[26]	train-mlogloss:2.45052	validation-mlogloss:2.44886
[27]	train-mlogloss:2.44206	validation-mlogloss:2.44027
[28]	train-mlogloss:2.43368	validation-mlogloss:2.43182
[29]	train-mlogloss:2.42562	validation-mlogloss:2.42352
[30]	train-mlogloss:2.41791	validation-mlogloss:2.41575
[31]	train-mlogloss:2.40995	validation-mlogloss:2.40768
[32]	train-mlogloss:2.40179	validation-mlogloss:2.39953
[33]	train-mlogloss:2.39391	validation-mlogloss:2.39165
[34]	train-mlogloss:2.38615	validation-mlogloss:2.38407
[35]	train-mlogloss:2.37824	validation-mlogloss:2.37613
[36]	train-mlogloss:2.37085	validation-mlogloss:2.36889
[37]	train-mlogloss:2.36339	validation-mlogloss:2.36142
[38]	train-mlogloss:2.35616	validation-mlogloss:2.35416
[39]	train-mlogloss:2.34892	validation-mlogloss:2.34698
[40]	train-mlogloss:2.34173	validation-mlogloss:2.34000
[41]	train-mlogloss:2.33457	validation-mlogloss:2.33305
[42]	train-mlogloss:2.32780	validation-mlogloss:2.32630
[43]	train-mlogloss:2.32083	validation-mlogloss:2.31913
[44]	train-mlogloss:2.31368	validation-mlogloss:2.31180
[45]	train-mlogloss:2.30707	validation-mlogloss:2.30513
[46]	train-mlogloss:2.30022	validation-mlogloss:2.29821
[47]	train-mlogloss:2.29341	validation-mlogloss:2.29153
[48]	train-mlogloss:2.28702	validation-mlogloss:2.28518
[49]	train-mlogloss:2.28022	validation-mlogloss:2.27851
[50]	train-mlogloss:2.27364	validation-mlogloss:2.27196
[51]	train-mlogloss:2.26721	validation-mlogloss:2.26577
[52]	train-mlogloss:2.26075	validation-mlogloss:2.25951
[53]	train-mlogloss:2.25472	validation-mlogloss:2.25359
[54]	train-mlogloss:2.24827	validation-mlogloss:2.24730
[55]	train-mlogloss:2.24189	validation-mlogloss:2.24095
[56]	train-mlogloss:2.23584	validation-mlogloss:2.23490
[57]	train-mlogloss:2.22962	validation-mlogloss:2.22884
[58]	train-mlogloss:2.22319	validation-mlogloss:2.22284
[59]	train-mlogloss:2.21717	validation-mlogloss:2.21705
[60]	train-mlogloss:2.21106	validation-mlogloss:2.21108
[61]	train-mlogloss:2.20529	validation-mlogloss:2.20548
[62]	train-mlogloss:2.19940	validation-mlogloss:2.19946
[63]	train-mlogloss:2.19359	validation-mlogloss:2.19380
[64]	train-mlogloss:2.18762	validation-mlogloss:2.18787
[65]	train-mlogloss:2.18203	validation-mlogloss:2.18270
[66]	train-mlogloss:2.17654	validation-mlogloss:2.17716
[67]	train-mlogloss:2.17113	validation-mlogloss:2.17222
[68]	train-mlogloss:2.16556	validation-mlogloss:2.16668
[69]	train-mlogloss:2.16027	validation-mlogloss:2.16140
[70]	train-mlogloss:2.15478	validation-mlogloss:2.15631
[71]	train-mlogloss:2.14948	validation-mlogloss:2.15124
[72]	train-mlogloss:2.14418	validation-mlogloss:2.14593
[73]	train-mlogloss:2.13874	validation-mlogloss:2.14040
[74]	train-mlogloss:2.13357	validation-mlogloss:2.13516
[75]	train-mlogloss:2.12840	validation-mlogloss:2.13032
[76]	train-mlogloss:2.12336	validation-mlogloss:2.12559
[77]	train-mlogloss:2.11822	validation-mlogloss:2.12075
[78]	train-mlogloss:2.11305	validation-mlogloss:2.11583
[79]	train-mlogloss:2.10807	validation-mlogloss:2.11115
[80]	train-mlogloss:2.10328	validation-mlogloss:2.10655
[81]	train-mlogloss:2.09827	validation-mlogloss:2.10161
[82]	train-mlogloss:2.09334	validation-mlogloss:2.09683
[83]	train-mlogloss:2.08876	validation-mlogloss:2.09219
[84]	train-mlogloss:2.08407	validation-mlogloss:2.08768
[85]	train-mlogloss:2.07935	validation-mlogloss:2.08294
[86]	train-mlogloss:2.07485	validation-mlogloss:2.07843
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[88]	train-mlogloss:2.06584	validation-mlogloss:2.06991
[89]	train-mlogloss:2.06151	validation-mlogloss:2.06587
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[91]	train-mlogloss:2.05274	validation-mlogloss:2.05745
[92]	train-mlogloss:2.04834	validation-mlogloss:2.05308
[93]	train-mlogloss:2.04411	validation-mlogloss:2.04883
[94]	train-mlogloss:2.03999	validation-mlogloss:2.04502
[95]	train-mlogloss:2.03581	validation-mlogloss:2.04095
[96]	train-mlogloss:2.03156	validation-mlogloss:2.03683
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[98]	train-mlogloss:2.02318	validation-mlogloss:2.02882
[99]	train-mlogloss:2.01912	validation-mlogloss:2.02510
[100]	train-mlogloss:2.01524	validation-mlogloss:2.02144
[101]	train-mlogloss:2.01137	validation-mlogloss:2.01767
[102]	train-mlogloss:2.00736	validation-mlogloss:2.01366
[103]	train-mlogloss:2.00344	validation-mlogloss:2.01001
[104]	train-mlogloss:1.99949	validation-mlogloss:2.00623
[105]	train-mlogloss:1.99540	validation-mlogloss:2.00221
[106]	train-mlogloss:1.99135	validation-mlogloss:1.99834
[107]	train-mlogloss:1.98755	validation-mlogloss:1.99467
[108]	train-mlogloss:1.98377	validation-mlogloss:1.99111
[109]	train-mlogloss:1.98009	validation-mlogloss:1.98763
[110]	train-mlogloss:1.97634	validation-mlogloss:1.98405
[111]	train-mlogloss:1.97262	validation-mlogloss:1.98044
[112]	train-mlogloss:1.96893	validation-mlogloss:1.97697
[113]	train-mlogloss:1.96537	validation-mlogloss:1.97359
[114]	train-mlogloss:1.96183	validation-mlogloss:1.97010
[115]	train-mlogloss:1.95818	validation-mlogloss:1.96679
[116]	train-mlogloss:1.95457	validation-mlogloss:1.96325
[117]	train-mlogloss:1.95112	validation-mlogloss:1.95994
[118]	train-mlogloss:1.94759	validation-mlogloss:1.95642
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[120]	train-mlogloss:1.94070	validation-mlogloss:1.94995
[121]	train-mlogloss:1.93736	validation-mlogloss:1.94680
[122]	train-mlogloss:1.93415	validation-mlogloss:1.94386
[123]	train-mlogloss:1.93061	validation-mlogloss:1.94069
[124]	train-mlogloss:1.92733	validation-mlogloss:1.93758
[125]	train-mlogloss:1.92414	validation-mlogloss:1.93459
[126]	train-mlogloss:1.92091	validation-mlogloss:1.93150
[127]	train-mlogloss:1.91759	validation-mlogloss:1.92835
[128]	train-mlogloss:1.91426	validation-mlogloss:1.92504
[129]	train-mlogloss:1.91115	validation-mlogloss:1.92198
[130]	train-mlogloss:1.90775	validation-mlogloss:1.91868
[131]	train-mlogloss:1.90464	validation-mlogloss:1.91576
[132]	train-mlogloss:1.90159	validation-mlogloss:1.91298
[133]	train-mlogloss:1.89864	validation-mlogloss:1.91020
[134]	train-mlogloss:1.89566	validation-mlogloss:1.90738
[135]	train-mlogloss:1.89263	validation-mlogloss:1.90445
[136]	train-mlogloss:1.88958	validation-mlogloss:1.90158
[137]	train-mlogloss:1.88658	validation-mlogloss:1.89867
[138]	train-mlogloss:1.88376	validation-mlogloss:1.89592
[139]	train-mlogloss:1.88084	validation-mlogloss:1.89308
[140]	train-mlogloss:1.87799	validation-mlogloss:1.89043
[141]	train-mlogloss:1.87492	validation-mlogloss:1.88754
[142]	train-mlogloss:1.87201	validation-mlogloss:1.88492
[143]	train-mlogloss:1.86931	validation-mlogloss:1.88235
[144]	train-mlogloss:1.86657	validation-mlogloss:1.87985
[145]	train-mlogloss:1.86365	validation-mlogloss:1.87718
[146]	train-mlogloss:1.86084	validation-mlogloss:1.87454
[147]	train-mlogloss:1.85799	validation-mlogloss:1.87187
[148]	train-mlogloss:1.85527	validation-mlogloss:1.86920
[149]	train-mlogloss:1.85242	validation-mlogloss:1.86640
[150]	train-mlogloss:1.84957	validation-mlogloss:1.86369
[151]	train-mlogloss:1.84709	validation-mlogloss:1.86141
[152]	train-mlogloss:1.84427	validation-mlogloss:1.85876
[153]	train-mlogloss:1.84159	validation-mlogloss:1.85629
[154]	train-mlogloss:1.83899	validation-mlogloss:1.85382
[155]	train-mlogloss:1.83619	validation-mlogloss:1.85125
[156]	train-mlogloss:1.83355	validation-mlogloss:1.84888
[157]	train-mlogloss:1.83099	validation-mlogloss:1.84640
[158]	train-mlogloss:1.82840	validation-mlogloss:1.84388
[159]	train-mlogloss:1.82599	validation-mlogloss:1.84153
[160]	train-mlogloss:1.82339	validation-mlogloss:1.83900
[161]	train-mlogloss:1.82091	validation-mlogloss:1.83690
[162]	train-mlogloss:1.81850	validation-mlogloss:1.83459
[163]	train-mlogloss:1.81608	validation-mlogloss:1.83233
[164]	train-mlogloss:1.81369	validation-mlogloss:1.83005
[165]	train-mlogloss:1.81124	validation-mlogloss:1.82783
[166]	train-mlogloss:1.80887	validation-mlogloss:1.82532
[167]	train-mlogloss:1.80643	validation-mlogloss:1.82321
[168]	train-mlogloss:1.80415	validation-mlogloss:1.82105
[169]	train-mlogloss:1.80178	validation-mlogloss:1.81901
[170]	train-mlogloss:1.79953	validation-mlogloss:1.81681
[171]	train-mlogloss:1.79722	validation-mlogloss:1.81439
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[474]	train-mlogloss:1.47186	validation-mlogloss:1.52747
[475]	train-mlogloss:1.47140	validation-mlogloss:1.52715
[476]	train-mlogloss:1.47091	validation-mlogloss:1.52667
[477]	train-mlogloss:1.47042	validation-mlogloss:1.52641
[478]	train-mlogloss:1.46996	validation-mlogloss:1.52608
[479]	train-mlogloss:1.46941	validation-mlogloss:1.52568
[480]	train-mlogloss:1.46898	validation-mlogloss:1.52546
[481]	train-mlogloss:1.46853	validation-mlogloss:1.52507
[482]	train-mlogloss:1.46808	validation-mlogloss:1.52479
[483]	train-mlogloss:1.46759	validation-mlogloss:1.52444
[484]	train-mlogloss:1.46706	validation-mlogloss:1.52407
[485]	train-mlogloss:1.46661	validation-mlogloss:1.52364
[486]	train-mlogloss:1.46619	validation-mlogloss:1.52325
[487]	train-mlogloss:1.46569	validation-mlogloss:1.52297
[488]	train-mlogloss:1.46522	validation-mlogloss:1.52273
[489]	train-mlogloss:1.46475	validation-mlogloss:1.52242
[490]	train-mlogloss:1.46423	validation-mlogloss:1.52207
[491]	train-mlogloss:1.46373	validation-mlogloss:1.52176
[492]	train-mlogloss:1.46326	validation-mlogloss:1.52136
[493]	train-mlogloss:1.46281	validation-mlogloss:1.52100
[494]	train-mlogloss:1.46229	validation-mlogloss:1.52069
[495]	train-mlogloss:1.46188	validation-mlogloss:1.52024
[496]	train-mlogloss:1.46141	validation-mlogloss:1.51993
[497]	train-mlogloss:1.46100	validation-mlogloss:1.51967
[498]	train-mlogloss:1.46057	validation-mlogloss:1.51928
[499]	train-mlogloss:1.46011	validation-mlogloss:1.51896
[500]	train-mlogloss:1.45964	validation-mlogloss:1.51855
[501]	train-mlogloss:1.45916	validation-mlogloss:1.51820
[502]	train-mlogloss:1.45872	validation-mlogloss:1.51781
[503]	train-mlogloss:1.45826	validation-mlogloss:1.51758
[504]	train-mlogloss:1.45783	validation-mlogloss:1.51723
[505]	train-mlogloss:1.45735	validation-mlogloss:1.51691
[506]	train-mlogloss:1.45687	validation-mlogloss:1.51663
[507]	train-mlogloss:1.45641	validation-mlogloss:1.51629
[508]	train-mlogloss:1.45598	validation-mlogloss:1.51592
[509]	train-mlogloss:1.45562	validation-mlogloss:1.51565
[510]	train-mlogloss:1.45528	validation-mlogloss:1.51551
[511]	train-mlogloss:1.45488	validation-mlogloss:1.51528
[512]	train-mlogloss:1.45446	validation-mlogloss:1.51497
[513]	train-mlogloss:1.45399	validation-mlogloss:1.51473
[514]	train-mlogloss:1.45356	validation-mlogloss:1.51443
[515]	train-mlogloss:1.45313	validation-mlogloss:1.51416
[516]	train-mlogloss:1.45270	validation-mlogloss:1.51389
[517]	train-mlogloss:1.45225	validation-mlogloss:1.51361
[518]	train-mlogloss:1.45188	validation-mlogloss:1.51337
[519]	train-mlogloss:1.45144	validation-mlogloss:1.51308
[520]	train-mlogloss:1.45099	validation-mlogloss:1.51282
[521]	train-mlogloss:1.45064	validation-mlogloss:1.51244
[522]	train-mlogloss:1.45022	validation-mlogloss:1.51215
[523]	train-mlogloss:1.44982	validation-mlogloss:1.51187
[524]	train-mlogloss:1.44938	validation-mlogloss:1.51158
[525]	train-mlogloss:1.44899	validation-mlogloss:1.51132
[526]	train-mlogloss:1.44860	validation-mlogloss:1.51113
[527]	train-mlogloss:1.44819	validation-mlogloss:1.51083
[528]	train-mlogloss:1.44779	validation-mlogloss:1.51048
[529]	train-mlogloss:1.44736	validation-mlogloss:1.51030
[530]	train-mlogloss:1.44696	validation-mlogloss:1.50998
[531]	train-mlogloss:1.44652	validation-mlogloss:1.50964
[532]	train-mlogloss:1.44611	validation-mlogloss:1.50937
[533]	train-mlogloss:1.44571	validation-mlogloss:1.50918
[534]	train-mlogloss:1.44532	validation-mlogloss:1.50890
[535]	train-mlogloss:1.44493	validation-mlogloss:1.50860
[536]	train-mlogloss:1.44460	validation-mlogloss:1.50824
[537]	train-mlogloss:1.44421	validation-mlogloss:1.50798
[538]	train-mlogloss:1.44381	validation-mlogloss:1.50765
[539]	train-mlogloss:1.44342	validation-mlogloss:1.50737
[540]	train-mlogloss:1.44297	validation-mlogloss:1.50713
[541]	train-mlogloss:1.44258	validation-mlogloss:1.50690
[542]	train-mlogloss:1.44225	validation-mlogloss:1.50672
[543]	train-mlogloss:1.44188	validation-mlogloss:1.50650
[544]	train-mlogloss:1.44150	validation-mlogloss:1.50628
[545]	train-mlogloss:1.44114	validation-mlogloss:1.50596
[546]	train-mlogloss:1.44077	validation-mlogloss:1.50566
[547]	train-mlogloss:1.44038	validation-mlogloss:1.50539
[548]	train-mlogloss:1.44002	validation-mlogloss:1.50503
[549]	train-mlogloss:1.43959	validation-mlogloss:1.50479
[550]	train-mlogloss:1.43922	validation-mlogloss:1.50448
[551]	train-mlogloss:1.43885	validation-mlogloss:1.50420
[552]	train-mlogloss:1.43847	validation-mlogloss:1.50393
[553]	train-mlogloss:1.43807	validation-mlogloss:1.50351
[554]	train-mlogloss:1.43768	validation-mlogloss:1.50350
[555]	train-mlogloss:1.43730	validation-mlogloss:1.50319
[556]	train-mlogloss:1.43692	validation-mlogloss:1.50303
[557]	train-mlogloss:1.43650	validation-mlogloss:1.50276
[558]	train-mlogloss:1.43609	validation-mlogloss:1.50261
[559]	train-mlogloss:1.43569	validation-mlogloss:1.50228
[560]	train-mlogloss:1.43534	validation-mlogloss:1.50203
[561]	train-mlogloss:1.43494	validation-mlogloss:1.50181
[562]	train-mlogloss:1.43459	validation-mlogloss:1.50168
[563]	train-mlogloss:1.43422	validation-mlogloss:1.50140
[564]	train-mlogloss:1.43384	validation-mlogloss:1.50120
[565]	train-mlogloss:1.43353	validation-mlogloss:1.50108
[566]	train-mlogloss:1.43314	validation-mlogloss:1.50097
[567]	train-mlogloss:1.43281	validation-mlogloss:1.50078
[568]	train-mlogloss:1.43247	validation-mlogloss:1.50064
[569]	train-mlogloss:1.43209	validation-mlogloss:1.50043
[570]	train-mlogloss:1.43174	validation-mlogloss:1.50015
[571]	train-mlogloss:1.43143	validation-mlogloss:1.49993
[572]	train-mlogloss:1.43102	validation-mlogloss:1.49976
[573]	train-mlogloss:1.43069	validation-mlogloss:1.49953
[574]	train-mlogloss:1.43033	validation-mlogloss:1.49922
[575]	train-mlogloss:1.42992	validation-mlogloss:1.49904
[576]	train-mlogloss:1.42960	validation-mlogloss:1.49879
[577]	train-mlogloss:1.42924	validation-mlogloss:1.49845
[578]	train-mlogloss:1.42887	validation-mlogloss:1.49825
[579]	train-mlogloss:1.42854	validation-mlogloss:1.49813
[580]	train-mlogloss:1.42822	validation-mlogloss:1.49794
[581]	train-mlogloss:1.42789	validation-mlogloss:1.49763
[582]	train-mlogloss:1.42752	validation-mlogloss:1.49759
[583]	train-mlogloss:1.42717	validation-mlogloss:1.49749
[584]	train-mlogloss:1.42682	validation-mlogloss:1.49735
[585]	train-mlogloss:1.42649	validation-mlogloss:1.49704
[586]	train-mlogloss:1.42613	validation-mlogloss:1.49673
[587]	train-mlogloss:1.42579	validation-mlogloss:1.49659
[588]	train-mlogloss:1.42547	validation-mlogloss:1.49643
[589]	train-mlogloss:1.42516	validation-mlogloss:1.49629
[590]	train-mlogloss:1.42489	validation-mlogloss:1.49604
[591]	train-mlogloss:1.42447	validation-mlogloss:1.49577
[592]	train-mlogloss:1.42416	validation-mlogloss:1.49557
[593]	train-mlogloss:1.42382	validation-mlogloss:1.49531
[594]	train-mlogloss:1.42348	validation-mlogloss:1.49506
[595]	train-mlogloss:1.42316	validation-mlogloss:1.49492
[596]	train-mlogloss:1.42280	validation-mlogloss:1.49465
[597]	train-mlogloss:1.42237	validation-mlogloss:1.49447
[598]	train-mlogloss:1.42204	validation-mlogloss:1.49429
[599]	train-mlogloss:1.42171	validation-mlogloss:1.49402
[600]	train-mlogloss:1.42137	validation-mlogloss:1.49383
[601]	train-mlogloss:1.42102	validation-mlogloss:1.49362
[602]	train-mlogloss:1.42070	validation-mlogloss:1.49343
[603]	train-mlogloss:1.42036	validation-mlogloss:1.49334
[604]	train-mlogloss:1.42004	validation-mlogloss:1.49319
[605]	train-mlogloss:1.41972	validation-mlogloss:1.49297
[606]	train-mlogloss:1.41944	validation-mlogloss:1.49278
[607]	train-mlogloss:1.41912	validation-mlogloss:1.49263
[608]	train-mlogloss:1.41884	validation-mlogloss:1.49241
[609]	train-mlogloss:1.41844	validation-mlogloss:1.49227
[610]	train-mlogloss:1.41803	validation-mlogloss:1.49210
[611]	train-mlogloss:1.41775	validation-mlogloss:1.49188
[612]	train-mlogloss:1.41742	validation-mlogloss:1.49168
[613]	train-mlogloss:1.41707	validation-mlogloss:1.49146
[614]	train-mlogloss:1.41681	validation-mlogloss:1.49131
[615]	train-mlogloss:1.41652	validation-mlogloss:1.49116
[616]	train-mlogloss:1.41615	validation-mlogloss:1.49107
[617]	train-mlogloss:1.41582	validation-mlogloss:1.49089
[618]	train-mlogloss:1.41548	validation-mlogloss:1.49070
[619]	train-mlogloss:1.41518	validation-mlogloss:1.49049
[620]	train-mlogloss:1.41487	validation-mlogloss:1.49030
[621]	train-mlogloss:1.41456	validation-mlogloss:1.49013
[622]	train-mlogloss:1.41429	validation-mlogloss:1.48996
[623]	train-mlogloss:1.41398	validation-mlogloss:1.48981
[624]	train-mlogloss:1.41370	validation-mlogloss:1.48978
[625]	train-mlogloss:1.41337	validation-mlogloss:1.48965
[626]	train-mlogloss:1.41303	validation-mlogloss:1.48955
[627]	train-mlogloss:1.41275	validation-mlogloss:1.48941
[628]	train-mlogloss:1.41245	validation-mlogloss:1.48926
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.89 [%]
Global accuracy score (test) = 43.33 [%]
Global F1 score (train) = 55.75 [%]
Global F1 score (test) = 42.33 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.22      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.38      0.34       184
       CAMINAR USUAL SPEED       0.27      0.14      0.19       184
            CAMINAR ZIGZAG       0.30      0.27      0.28       184
          DE PIE BARRIENDO       0.47      0.49      0.48       184
   DE PIE DOBLANDO TOALLAS       0.42      0.41      0.41       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.56       184
INCREMENTAL CICLOERGOMETRO       0.90      0.59      0.71       184
           SENTADO LEYENDO       0.26      0.22      0.24       184
         SENTADO USANDO PC       0.17      0.07      0.09       184
      SENTADO VIENDO LA TV       0.38      0.36      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.54      0.74      0.62       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.43      0.42      2737


Accuracy capturado en la ejecución 10: 43.33 [%]
F1-score capturado en la ejecución 10: 42.33 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.0 [%]
Global accuracy score (test) = 43.44 [%]
Global F1 score (train) = 55.83 [%]
Global F1 score (test) = 42.46 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.20      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.32      0.41      0.36       184
       CAMINAR USUAL SPEED       0.25      0.14      0.18       184
            CAMINAR ZIGZAG       0.29      0.30      0.30       184
          DE PIE BARRIENDO       0.45      0.48      0.46       184
   DE PIE DOBLANDO TOALLAS       0.44      0.39      0.41       184
    DE PIE MOVIENDO LIBROS       0.37      0.39      0.38       184
          DE PIE USANDO PC       0.52      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.83      0.56       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.24      0.19      0.21       184
         SENTADO USANDO PC       0.21      0.08      0.12       184
      SENTADO VIENDO LA TV       0.38      0.36      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.57      0.73      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.43      0.42      2737


Accuracy capturado en la ejecución 11: 43.44 [%]
F1-score capturado en la ejecución 11: 42.46 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.89 [%]
Global accuracy score (test) = 43.77 [%]
Global F1 score (train) = 55.71 [%]
Global F1 score (test) = 42.88 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.20      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.40      0.35       184
       CAMINAR USUAL SPEED       0.25      0.13      0.17       184
            CAMINAR ZIGZAG       0.25      0.28      0.27       184
          DE PIE BARRIENDO       0.45      0.48      0.46       184
   DE PIE DOBLANDO TOALLAS       0.46      0.43      0.45       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.54      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.91      0.59      0.72       184
           SENTADO LEYENDO       0.30      0.26      0.27       184
         SENTADO USANDO PC       0.19      0.08      0.11       184
      SENTADO VIENDO LA TV       0.38      0.34      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.58      0.73      0.65       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.44      0.44      0.43      2737


Accuracy capturado en la ejecución 12: 43.77 [%]
F1-score capturado en la ejecución 12: 42.88 [%]

=== EJECUCIÓN 13 ===

--- TRAIN (ejecución 13) ---

--- TEST (ejecución 13) ---
2025-11-07 10:24:38.403200: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:24:38.415843: 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 00:00:1762507478.428978 2384381 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507478.432955 2384381 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507478.442691 2384381 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507478.442710 2384381 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507478.442712 2384381 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507478.442713 2384381 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:24:38.445689: 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:2.69720	validation-mlogloss:2.69728
[1]	train-mlogloss:2.68651	validation-mlogloss:2.68648
[2]	train-mlogloss:2.67594	validation-mlogloss:2.67568
[3]	train-mlogloss:2.66569	validation-mlogloss:2.66528
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[605]	train-mlogloss:1.41914	validation-mlogloss:1.49298
[606]	train-mlogloss:1.41879	validation-mlogloss:1.49283
[607]	train-mlogloss:1.41839	validation-mlogloss:1.49253
[608]	train-mlogloss:1.41809	validation-mlogloss:1.49231
[609]	train-mlogloss:1.41778	validation-mlogloss:1.49218
[610]	train-mlogloss:1.41743	validation-mlogloss:1.49200
[611]	train-mlogloss:1.41711	validation-mlogloss:1.49176
[612]	train-mlogloss:1.41677	validation-mlogloss:1.49168
[613]	train-mlogloss:1.41646	validation-mlogloss:1.49155
[614]	train-mlogloss:1.41615	validation-mlogloss:1.49131
[615]	train-mlogloss:1.41589	validation-mlogloss:1.49109
[616]	train-mlogloss:1.41557	validation-mlogloss:1.49086
[617]	train-mlogloss:1.41523	validation-mlogloss:1.49071
[618]	train-mlogloss:1.41490	validation-mlogloss:1.49048
[619]	train-mlogloss:1.41455	validation-mlogloss:1.49028
[620]	train-mlogloss:1.41421	validation-mlogloss:1.49010
[621]	train-mlogloss:1.41387	validation-mlogloss:1.48990
[622]	train-mlogloss:1.41357	validation-mlogloss:1.48969
[623]	train-mlogloss:1.41326	validation-mlogloss:1.48952
[624]	train-mlogloss:1.41299	validation-mlogloss:1.48933
[625]	train-mlogloss:1.41271	validation-mlogloss:1.48912
[626]	train-mlogloss:1.41240	validation-mlogloss:1.48907
[627]	train-mlogloss:1.41212	validation-mlogloss:1.48891
[628]	train-mlogloss:1.41174	validation-mlogloss:1.48867
Saved model to disk.
2025-11-07 10:25:04.871255: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:25:04.882703: 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 00:00:1762507504.896116 2384802 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507504.900310 2384802 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507504.910343 2384802 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507504.910367 2384802 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507504.910370 2384802 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507504.910371 2384802 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:25:04.913567: 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:2.69718	validation-mlogloss:2.69709
[1]	train-mlogloss:2.68675	validation-mlogloss:2.68691
[2]	train-mlogloss:2.67659	validation-mlogloss:2.67669
[3]	train-mlogloss:2.66625	validation-mlogloss:2.66620
[4]	train-mlogloss:2.65581	validation-mlogloss:2.65559
[5]	train-mlogloss:2.64547	validation-mlogloss:2.64513
[6]	train-mlogloss:2.63506	validation-mlogloss:2.63442
[7]	train-mlogloss:2.62482	validation-mlogloss:2.62402
[8]	train-mlogloss:2.61500	validation-mlogloss:2.61422
[9]	train-mlogloss:2.60557	validation-mlogloss:2.60476
[10]	train-mlogloss:2.59584	validation-mlogloss:2.59475
[11]	train-mlogloss:2.58603	validation-mlogloss:2.58463
[12]	train-mlogloss:2.57669	validation-mlogloss:2.57529
[13]	train-mlogloss:2.56730	validation-mlogloss:2.56584
[14]	train-mlogloss:2.55776	validation-mlogloss:2.55620
[15]	train-mlogloss:2.54860	validation-mlogloss:2.54680
[16]	train-mlogloss:2.53943	validation-mlogloss:2.53753
[17]	train-mlogloss:2.53004	validation-mlogloss:2.52825
[18]	train-mlogloss:2.52097	validation-mlogloss:2.51909
[19]	train-mlogloss:2.51214	validation-mlogloss:2.51010
[20]	train-mlogloss:2.50297	validation-mlogloss:2.50091
[21]	train-mlogloss:2.49374	validation-mlogloss:2.49187
[22]	train-mlogloss:2.48548	validation-mlogloss:2.48355
[23]	train-mlogloss:2.47622	validation-mlogloss:2.47434
[24]	train-mlogloss:2.46744	validation-mlogloss:2.46577
[25]	train-mlogloss:2.45913	validation-mlogloss:2.45729
[26]	train-mlogloss:2.45018	validation-mlogloss:2.44848
[27]	train-mlogloss:2.44163	validation-mlogloss:2.43986
[28]	train-mlogloss:2.43306	validation-mlogloss:2.43104
[29]	train-mlogloss:2.42495	validation-mlogloss:2.42291
[30]	train-mlogloss:2.41671	validation-mlogloss:2.41450
[31]	train-mlogloss:2.40912	validation-mlogloss:2.40678
[32]	train-mlogloss:2.40095	validation-mlogloss:2.39875
[33]	train-mlogloss:2.39320	validation-mlogloss:2.39098
[34]	train-mlogloss:2.38545	validation-mlogloss:2.38331
[35]	train-mlogloss:2.37754	validation-mlogloss:2.37545
[36]	train-mlogloss:2.36982	validation-mlogloss:2.36772
[37]	train-mlogloss:2.36279	validation-mlogloss:2.36056
[38]	train-mlogloss:2.35528	validation-mlogloss:2.35305
[39]	train-mlogloss:2.34785	validation-mlogloss:2.34577
[40]	train-mlogloss:2.34089	validation-mlogloss:2.33879
[41]	train-mlogloss:2.33344	validation-mlogloss:2.33155
[42]	train-mlogloss:2.32613	validation-mlogloss:2.32442
[43]	train-mlogloss:2.31938	validation-mlogloss:2.31763
[44]	train-mlogloss:2.31259	validation-mlogloss:2.31070
[45]	train-mlogloss:2.30586	validation-mlogloss:2.30391
[46]	train-mlogloss:2.29905	validation-mlogloss:2.29727
[47]	train-mlogloss:2.29232	validation-mlogloss:2.29063
[48]	train-mlogloss:2.28561	validation-mlogloss:2.28388
[49]	train-mlogloss:2.27905	validation-mlogloss:2.27733
[50]	train-mlogloss:2.27258	validation-mlogloss:2.27077
[51]	train-mlogloss:2.26619	validation-mlogloss:2.26432
[52]	train-mlogloss:2.25996	validation-mlogloss:2.25806
[53]	train-mlogloss:2.25339	validation-mlogloss:2.25175
[54]	train-mlogloss:2.24745	validation-mlogloss:2.24596
[55]	train-mlogloss:2.24112	validation-mlogloss:2.23957
[56]	train-mlogloss:2.23512	validation-mlogloss:2.23339
[57]	train-mlogloss:2.22874	validation-mlogloss:2.22730
[58]	train-mlogloss:2.22255	validation-mlogloss:2.22137
[59]	train-mlogloss:2.21650	validation-mlogloss:2.21542
[60]	train-mlogloss:2.21065	validation-mlogloss:2.20957
[61]	train-mlogloss:2.20479	validation-mlogloss:2.20376
[62]	train-mlogloss:2.19910	validation-mlogloss:2.19820
[63]	train-mlogloss:2.19339	validation-mlogloss:2.19263
[64]	train-mlogloss:2.18773	validation-mlogloss:2.18702
[65]	train-mlogloss:2.18214	validation-mlogloss:2.18161
[66]	train-mlogloss:2.17646	validation-mlogloss:2.17614
[67]	train-mlogloss:2.17076	validation-mlogloss:2.17062
[68]	train-mlogloss:2.16527	validation-mlogloss:2.16526
[69]	train-mlogloss:2.15982	validation-mlogloss:2.16014
[70]	train-mlogloss:2.15438	validation-mlogloss:2.15484
[71]	train-mlogloss:2.14882	validation-mlogloss:2.14933
[72]	train-mlogloss:2.14358	validation-mlogloss:2.14436
[73]	train-mlogloss:2.13828	validation-mlogloss:2.13939
[74]	train-mlogloss:2.13322	validation-mlogloss:2.13434
[75]	train-mlogloss:2.12818	validation-mlogloss:2.12953
[76]	train-mlogloss:2.12307	validation-mlogloss:2.12453
[77]	train-mlogloss:2.11796	validation-mlogloss:2.11939
[78]	train-mlogloss:2.11328	validation-mlogloss:2.11479
[79]	train-mlogloss:2.10859	validation-mlogloss:2.11032
[80]	train-mlogloss:2.10354	validation-mlogloss:2.10571
[81]	train-mlogloss:2.09877	validation-mlogloss:2.10106
[82]	train-mlogloss:2.09374	validation-mlogloss:2.09628
[83]	train-mlogloss:2.08914	validation-mlogloss:2.09175
[84]	train-mlogloss:2.08471	validation-mlogloss:2.08737
[85]	train-mlogloss:2.08010	validation-mlogloss:2.08280
[86]	train-mlogloss:2.07550	validation-mlogloss:2.07836
[87]	train-mlogloss:2.07097	validation-mlogloss:2.07389
[88]	train-mlogloss:2.06640	validation-mlogloss:2.06966
[89]	train-mlogloss:2.06200	validation-mlogloss:2.06536
[90]	train-mlogloss:2.05752	validation-mlogloss:2.06118
[91]	train-mlogloss:2.05299	validation-mlogloss:2.05685
[92]	train-mlogloss:2.04850	validation-mlogloss:2.05256
[93]	train-mlogloss:2.04448	validation-mlogloss:2.04854
[94]	train-mlogloss:2.04031	validation-mlogloss:2.04460
[95]	train-mlogloss:2.03640	validation-mlogloss:2.04095
[96]	train-mlogloss:2.03218	validation-mlogloss:2.03694
[97]	train-mlogloss:2.02807	validation-mlogloss:2.03302
[98]	train-mlogloss:2.02395	validation-mlogloss:2.02913
[99]	train-mlogloss:2.01996	validation-mlogloss:2.02538
[100]	train-mlogloss:2.01581	validation-mlogloss:2.02137
[101]	train-mlogloss:2.01183	validation-mlogloss:2.01731
[102]	train-mlogloss:2.00784	validation-mlogloss:2.01348
[103]	train-mlogloss:2.00377	validation-mlogloss:2.00972
[104]	train-mlogloss:1.99991	validation-mlogloss:2.00618
[105]	train-mlogloss:1.99600	validation-mlogloss:2.00244
[106]	train-mlogloss:1.99213	validation-mlogloss:1.99878
[107]	train-mlogloss:1.98844	validation-mlogloss:1.99522
[108]	train-mlogloss:1.98462	validation-mlogloss:1.99189
[109]	train-mlogloss:1.98103	validation-mlogloss:1.98861
[110]	train-mlogloss:1.97738	validation-mlogloss:1.98500
[111]	train-mlogloss:1.97380	validation-mlogloss:1.98180
[112]	train-mlogloss:1.97012	validation-mlogloss:1.97827
[113]	train-mlogloss:1.96642	validation-mlogloss:1.97476
[114]	train-mlogloss:1.96282	validation-mlogloss:1.97130
[115]	train-mlogloss:1.95916	validation-mlogloss:1.96790
[116]	train-mlogloss:1.95556	validation-mlogloss:1.96445
[117]	train-mlogloss:1.95202	validation-mlogloss:1.96105
[118]	train-mlogloss:1.94854	validation-mlogloss:1.95754
[119]	train-mlogloss:1.94507	validation-mlogloss:1.95443
[120]	train-mlogloss:1.94156	validation-mlogloss:1.95106
[121]	train-mlogloss:1.93820	validation-mlogloss:1.94797
[122]	train-mlogloss:1.93476	validation-mlogloss:1.94490
[123]	train-mlogloss:1.93134	validation-mlogloss:1.94156
[124]	train-mlogloss:1.92796	validation-mlogloss:1.93842
[125]	train-mlogloss:1.92470	validation-mlogloss:1.93510
[126]	train-mlogloss:1.92162	validation-mlogloss:1.93224
[127]	train-mlogloss:1.91836	validation-mlogloss:1.92901
[128]	train-mlogloss:1.91494	validation-mlogloss:1.92577
[129]	train-mlogloss:1.91156	validation-mlogloss:1.92259
[130]	train-mlogloss:1.90844	validation-mlogloss:1.91947
[131]	train-mlogloss:1.90504	validation-mlogloss:1.91639
[132]	train-mlogloss:1.90186	validation-mlogloss:1.91346
[133]	train-mlogloss:1.89879	validation-mlogloss:1.91061
[134]	train-mlogloss:1.89579	validation-mlogloss:1.90779
[135]	train-mlogloss:1.89254	validation-mlogloss:1.90480
[136]	train-mlogloss:1.88972	validation-mlogloss:1.90208
[137]	train-mlogloss:1.88658	validation-mlogloss:1.89902
[138]	train-mlogloss:1.88371	validation-mlogloss:1.89631
[139]	train-mlogloss:1.88066	validation-mlogloss:1.89339
[140]	train-mlogloss:1.87786	validation-mlogloss:1.89068
[141]	train-mlogloss:1.87495	validation-mlogloss:1.88786
[142]	train-mlogloss:1.87221	validation-mlogloss:1.88522
[143]	train-mlogloss:1.86935	validation-mlogloss:1.88259
[144]	train-mlogloss:1.86654	validation-mlogloss:1.87991
[145]	train-mlogloss:1.86371	validation-mlogloss:1.87742
[146]	train-mlogloss:1.86086	validation-mlogloss:1.87467
[147]	train-mlogloss:1.85809	validation-mlogloss:1.87207
[148]	train-mlogloss:1.85521	validation-mlogloss:1.86916
[149]	train-mlogloss:1.85253	validation-mlogloss:1.86663
[150]	train-mlogloss:1.84974	validation-mlogloss:1.86423
[151]	train-mlogloss:1.84704	validation-mlogloss:1.86173
[152]	train-mlogloss:1.84427	validation-mlogloss:1.85913
[153]	train-mlogloss:1.84165	validation-mlogloss:1.85675
[154]	train-mlogloss:1.83886	validation-mlogloss:1.85407
[155]	train-mlogloss:1.83615	validation-mlogloss:1.85153
[156]	train-mlogloss:1.83365	validation-mlogloss:1.84914
[157]	train-mlogloss:1.83104	validation-mlogloss:1.84657
[158]	train-mlogloss:1.82851	validation-mlogloss:1.84427
[159]	train-mlogloss:1.82614	validation-mlogloss:1.84193
[160]	train-mlogloss:1.82376	validation-mlogloss:1.83971
[161]	train-mlogloss:1.82128	validation-mlogloss:1.83732
[162]	train-mlogloss:1.81863	validation-mlogloss:1.83500
[163]	train-mlogloss:1.81630	validation-mlogloss:1.83291
[164]	train-mlogloss:1.81368	validation-mlogloss:1.83044
[165]	train-mlogloss:1.81124	validation-mlogloss:1.82811
[166]	train-mlogloss:1.80876	validation-mlogloss:1.82564
[167]	train-mlogloss:1.80650	validation-mlogloss:1.82339
[168]	train-mlogloss:1.80415	validation-mlogloss:1.82104
[169]	train-mlogloss:1.80172	validation-mlogloss:1.81884
[170]	train-mlogloss:1.79926	validation-mlogloss:1.81661
[171]	train-mlogloss:1.79680	validation-mlogloss:1.81423
[172]	train-mlogloss:1.79445	validation-mlogloss:1.81219
[173]	train-mlogloss:1.79221	validation-mlogloss:1.81003
[174]	train-mlogloss:1.78987	validation-mlogloss:1.80767
[175]	train-mlogloss:1.78763	validation-mlogloss:1.80547
[176]	train-mlogloss:1.78521	validation-mlogloss:1.80339
[177]	train-mlogloss:1.78293	validation-mlogloss:1.80126
[178]	train-mlogloss:1.78082	validation-mlogloss:1.79927
[179]	train-mlogloss:1.77845	validation-mlogloss:1.79714
[180]	train-mlogloss:1.77630	validation-mlogloss:1.79524
[181]	train-mlogloss:1.77416	validation-mlogloss:1.79310
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[183]	train-mlogloss:1.77002	validation-mlogloss:1.78927
[184]	train-mlogloss:1.76789	validation-mlogloss:1.78731
[185]	train-mlogloss:1.76580	validation-mlogloss:1.78542
[186]	train-mlogloss:1.76359	validation-mlogloss:1.78348
[187]	train-mlogloss:1.76146	validation-mlogloss:1.78140
[188]	train-mlogloss:1.75947	validation-mlogloss:1.77949
[189]	train-mlogloss:1.75755	validation-mlogloss:1.77761
[190]	train-mlogloss:1.75558	validation-mlogloss:1.77570
[191]	train-mlogloss:1.75358	validation-mlogloss:1.77379
[192]	train-mlogloss:1.75145	validation-mlogloss:1.77178
[193]	train-mlogloss:1.74949	validation-mlogloss:1.76998
[194]	train-mlogloss:1.74759	validation-mlogloss:1.76834
[195]	train-mlogloss:1.74561	validation-mlogloss:1.76644
[196]	train-mlogloss:1.74365	validation-mlogloss:1.76463
[197]	train-mlogloss:1.74170	validation-mlogloss:1.76264
[198]	train-mlogloss:1.73977	validation-mlogloss:1.76075
[199]	train-mlogloss:1.73781	validation-mlogloss:1.75899
[200]	train-mlogloss:1.73590	validation-mlogloss:1.75721
[201]	train-mlogloss:1.73394	validation-mlogloss:1.75546
[202]	train-mlogloss:1.73215	validation-mlogloss:1.75379
[203]	train-mlogloss:1.73037	validation-mlogloss:1.75210
[204]	train-mlogloss:1.72864	validation-mlogloss:1.75053
[205]	train-mlogloss:1.72682	validation-mlogloss:1.74915
[206]	train-mlogloss:1.72493	validation-mlogloss:1.74746
[207]	train-mlogloss:1.72306	validation-mlogloss:1.74581
[208]	train-mlogloss:1.72107	validation-mlogloss:1.74395
[209]	train-mlogloss:1.71922	validation-mlogloss:1.74219
[210]	train-mlogloss:1.71741	validation-mlogloss:1.74052
[211]	train-mlogloss:1.71563	validation-mlogloss:1.73886
[212]	train-mlogloss:1.71394	validation-mlogloss:1.73730
[213]	train-mlogloss:1.71222	validation-mlogloss:1.73564
[214]	train-mlogloss:1.71053	validation-mlogloss:1.73409
[215]	train-mlogloss:1.70872	validation-mlogloss:1.73264
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[217]	train-mlogloss:1.70508	validation-mlogloss:1.72952
[218]	train-mlogloss:1.70343	validation-mlogloss:1.72810
[219]	train-mlogloss:1.70158	validation-mlogloss:1.72633
[220]	train-mlogloss:1.69995	validation-mlogloss:1.72482
[221]	train-mlogloss:1.69834	validation-mlogloss:1.72345
[222]	train-mlogloss:1.69673	validation-mlogloss:1.72201
[223]	train-mlogloss:1.69513	validation-mlogloss:1.72043
[224]	train-mlogloss:1.69351	validation-mlogloss:1.71910
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[228]	train-mlogloss:1.68712	validation-mlogloss:1.71306
[229]	train-mlogloss:1.68555	validation-mlogloss:1.71167
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[238]	train-mlogloss:1.67168	validation-mlogloss:1.69886
[239]	train-mlogloss:1.67013	validation-mlogloss:1.69748
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[248]	train-mlogloss:1.65718	validation-mlogloss:1.68546
[249]	train-mlogloss:1.65581	validation-mlogloss:1.68401
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[552]	train-mlogloss:1.43765	validation-mlogloss:1.50443
[553]	train-mlogloss:1.43723	validation-mlogloss:1.50421
[554]	train-mlogloss:1.43688	validation-mlogloss:1.50398
[555]	train-mlogloss:1.43649	validation-mlogloss:1.50364
[556]	train-mlogloss:1.43610	validation-mlogloss:1.50347
[557]	train-mlogloss:1.43574	validation-mlogloss:1.50323
[558]	train-mlogloss:1.43538	validation-mlogloss:1.50288
[559]	train-mlogloss:1.43500	validation-mlogloss:1.50258
[560]	train-mlogloss:1.43467	validation-mlogloss:1.50224
[561]	train-mlogloss:1.43435	validation-mlogloss:1.50198
[562]	train-mlogloss:1.43398	validation-mlogloss:1.50176
[563]	train-mlogloss:1.43367	validation-mlogloss:1.50155
[564]	train-mlogloss:1.43334	validation-mlogloss:1.50127
[565]	train-mlogloss:1.43300	validation-mlogloss:1.50109
[566]	train-mlogloss:1.43263	validation-mlogloss:1.50082
[567]	train-mlogloss:1.43227	validation-mlogloss:1.50060
[568]	train-mlogloss:1.43189	validation-mlogloss:1.50035
[569]	train-mlogloss:1.43154	validation-mlogloss:1.50008
[570]	train-mlogloss:1.43119	validation-mlogloss:1.49979
[571]	train-mlogloss:1.43083	validation-mlogloss:1.49957
[572]	train-mlogloss:1.43047	validation-mlogloss:1.49927
[573]	train-mlogloss:1.43013	validation-mlogloss:1.49916
[574]	train-mlogloss:1.42973	validation-mlogloss:1.49898
[575]	train-mlogloss:1.42935	validation-mlogloss:1.49875
[576]	train-mlogloss:1.42901	validation-mlogloss:1.49857
[577]	train-mlogloss:1.42865	validation-mlogloss:1.49837
[578]	train-mlogloss:1.42828	validation-mlogloss:1.49814
[579]	train-mlogloss:1.42790	validation-mlogloss:1.49795
[580]	train-mlogloss:1.42753	validation-mlogloss:1.49765
[581]	train-mlogloss:1.42716	validation-mlogloss:1.49741
[582]	train-mlogloss:1.42682	validation-mlogloss:1.49727
[583]	train-mlogloss:1.42655	validation-mlogloss:1.49709
[584]	train-mlogloss:1.42617	validation-mlogloss:1.49694
[585]	train-mlogloss:1.42583	validation-mlogloss:1.49670
[586]	train-mlogloss:1.42545	validation-mlogloss:1.49647
[587]	train-mlogloss:1.42511	validation-mlogloss:1.49624
[588]	train-mlogloss:1.42473	validation-mlogloss:1.49602
[589]	train-mlogloss:1.42437	validation-mlogloss:1.49577
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[591]	train-mlogloss:1.42369	validation-mlogloss:1.49536
[592]	train-mlogloss:1.42334	validation-mlogloss:1.49516
[593]	train-mlogloss:1.42307	validation-mlogloss:1.49497
[594]	train-mlogloss:1.42272	validation-mlogloss:1.49476
[595]	train-mlogloss:1.42238	validation-mlogloss:1.49455
[596]	train-mlogloss:1.42208	validation-mlogloss:1.49444
[597]	train-mlogloss:1.42174	validation-mlogloss:1.49420
[598]	train-mlogloss:1.42139	validation-mlogloss:1.49404
[599]	train-mlogloss:1.42109	validation-mlogloss:1.49385
[600]	train-mlogloss:1.42081	validation-mlogloss:1.49364
[601]	train-mlogloss:1.42048	validation-mlogloss:1.49346
[602]	train-mlogloss:1.42012	validation-mlogloss:1.49326
[603]	train-mlogloss:1.41975	validation-mlogloss:1.49306
[604]	train-mlogloss:1.41946	validation-mlogloss:1.49281
[605]	train-mlogloss:1.41918	validation-mlogloss:1.49263
[606]	train-mlogloss:1.41887	validation-mlogloss:1.49242
[607]	train-mlogloss:1.41862	validation-mlogloss:1.49236
[608]	train-mlogloss:1.41824	validation-mlogloss:1.49216
[609]	train-mlogloss:1.41789	validation-mlogloss:1.49191
[610]	train-mlogloss:1.41758	validation-mlogloss:1.49178
[611]	train-mlogloss:1.41726	validation-mlogloss:1.49166
[612]	train-mlogloss:1.41690	validation-mlogloss:1.49150
[613]	train-mlogloss:1.41660	validation-mlogloss:1.49137
[614]	train-mlogloss:1.41627	validation-mlogloss:1.49119
[615]	train-mlogloss:1.41600	validation-mlogloss:1.49097
[616]	train-mlogloss:1.41565	validation-mlogloss:1.49074
[617]	train-mlogloss:1.41529	validation-mlogloss:1.49058
[618]	train-mlogloss:1.41502	validation-mlogloss:1.49040
[619]	train-mlogloss:1.41473	validation-mlogloss:1.49025
[620]	train-mlogloss:1.41445	validation-mlogloss:1.49008
[621]	train-mlogloss:1.41413	validation-mlogloss:1.48988
[622]	train-mlogloss:1.41380	validation-mlogloss:1.48965
[623]	train-mlogloss:1.41355	validation-mlogloss:1.48942
[624]	train-mlogloss:1.41324	validation-mlogloss:1.48931
[625]	train-mlogloss:1.41291	validation-mlogloss:1.48906
[626]	train-mlogloss:1.41262	validation-mlogloss:1.48883
[627]	train-mlogloss:1.41227	validation-mlogloss:1.48854
[628]	train-mlogloss:1.41200	validation-mlogloss:1.48834
Saved model to disk.
2025-11-07 10:25:31.365335: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:25:31.377072: 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 00:00:1762507531.390814 2385223 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507531.395170 2385223 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507531.405230 2385223 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507531.405251 2385223 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507531.405254 2385223 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507531.405256 2385223 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:25:31.408471: 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:2.69758	validation-mlogloss:2.69742
[1]	train-mlogloss:2.68750	validation-mlogloss:2.68755
[2]	train-mlogloss:2.67716	validation-mlogloss:2.67706
[3]	train-mlogloss:2.66678	validation-mlogloss:2.66646
[4]	train-mlogloss:2.65650	validation-mlogloss:2.65617
[5]	train-mlogloss:2.64627	validation-mlogloss:2.64581
[6]	train-mlogloss:2.63609	validation-mlogloss:2.63569
[7]	train-mlogloss:2.62587	validation-mlogloss:2.62543
[8]	train-mlogloss:2.61578	validation-mlogloss:2.61539
[9]	train-mlogloss:2.60574	validation-mlogloss:2.60548
[10]	train-mlogloss:2.59560	validation-mlogloss:2.59525
[11]	train-mlogloss:2.58579	validation-mlogloss:2.58565
[12]	train-mlogloss:2.57637	validation-mlogloss:2.57615
[13]	train-mlogloss:2.56629	validation-mlogloss:2.56633
[14]	train-mlogloss:2.55691	validation-mlogloss:2.55673
[15]	train-mlogloss:2.54725	validation-mlogloss:2.54707
[16]	train-mlogloss:2.53792	validation-mlogloss:2.53788
[17]	train-mlogloss:2.52862	validation-mlogloss:2.52868
[18]	train-mlogloss:2.51964	validation-mlogloss:2.51975
[19]	train-mlogloss:2.51052	validation-mlogloss:2.51040
[20]	train-mlogloss:2.50178	validation-mlogloss:2.50185
[21]	train-mlogloss:2.49297	validation-mlogloss:2.49306
[22]	train-mlogloss:2.48397	validation-mlogloss:2.48412
[23]	train-mlogloss:2.47548	validation-mlogloss:2.47544
[24]	train-mlogloss:2.46655	validation-mlogloss:2.46637
[25]	train-mlogloss:2.45789	validation-mlogloss:2.45777
[26]	train-mlogloss:2.44941	validation-mlogloss:2.44924
[27]	train-mlogloss:2.44090	validation-mlogloss:2.44079
[28]	train-mlogloss:2.43279	validation-mlogloss:2.43270
[29]	train-mlogloss:2.42462	validation-mlogloss:2.42462
[30]	train-mlogloss:2.41648	validation-mlogloss:2.41653
[31]	train-mlogloss:2.40882	validation-mlogloss:2.40893
[32]	train-mlogloss:2.40094	validation-mlogloss:2.40088
[33]	train-mlogloss:2.39329	validation-mlogloss:2.39289
[34]	train-mlogloss:2.38552	validation-mlogloss:2.38523
[35]	train-mlogloss:2.37813	validation-mlogloss:2.37782
[36]	train-mlogloss:2.37081	validation-mlogloss:2.37048
[37]	train-mlogloss:2.36363	validation-mlogloss:2.36324
[38]	train-mlogloss:2.35618	validation-mlogloss:2.35577
[39]	train-mlogloss:2.34885	validation-mlogloss:2.34862
[40]	train-mlogloss:2.34198	validation-mlogloss:2.34152
[41]	train-mlogloss:2.33496	validation-mlogloss:2.33449
[42]	train-mlogloss:2.32775	validation-mlogloss:2.32725
[43]	train-mlogloss:2.32063	validation-mlogloss:2.32040
[44]	train-mlogloss:2.31378	validation-mlogloss:2.31339
[45]	train-mlogloss:2.30671	validation-mlogloss:2.30612
[46]	train-mlogloss:2.30002	validation-mlogloss:2.29955
[47]	train-mlogloss:2.29353	validation-mlogloss:2.29295
[48]	train-mlogloss:2.28670	validation-mlogloss:2.28650
[49]	train-mlogloss:2.28006	validation-mlogloss:2.27984
[50]	train-mlogloss:2.27348	validation-mlogloss:2.27338
[51]	train-mlogloss:2.26695	validation-mlogloss:2.26676
[52]	train-mlogloss:2.26039	validation-mlogloss:2.26055
[53]	train-mlogloss:2.25406	validation-mlogloss:2.25450
[54]	train-mlogloss:2.24783	validation-mlogloss:2.24836
[55]	train-mlogloss:2.24158	validation-mlogloss:2.24224
[56]	train-mlogloss:2.23572	validation-mlogloss:2.23646
[57]	train-mlogloss:2.22961	validation-mlogloss:2.23047
[58]	train-mlogloss:2.22352	validation-mlogloss:2.22437
[59]	train-mlogloss:2.21758	validation-mlogloss:2.21861
[60]	train-mlogloss:2.21190	validation-mlogloss:2.21291
[61]	train-mlogloss:2.20601	validation-mlogloss:2.20730
[62]	train-mlogloss:2.20016	validation-mlogloss:2.20156
[63]	train-mlogloss:2.19444	validation-mlogloss:2.19607
[64]	train-mlogloss:2.18887	validation-mlogloss:2.19067
[65]	train-mlogloss:2.18329	validation-mlogloss:2.18521
[66]	train-mlogloss:2.17772	validation-mlogloss:2.17986
[67]	train-mlogloss:2.17194	validation-mlogloss:2.17417
[68]	train-mlogloss:2.16650	validation-mlogloss:2.16875
[69]	train-mlogloss:2.16094	validation-mlogloss:2.16333
[70]	train-mlogloss:2.15544	validation-mlogloss:2.15788
[71]	train-mlogloss:2.15021	validation-mlogloss:2.15287
[72]	train-mlogloss:2.14480	validation-mlogloss:2.14773
[73]	train-mlogloss:2.13943	validation-mlogloss:2.14253
[74]	train-mlogloss:2.13423	validation-mlogloss:2.13765
[75]	train-mlogloss:2.12889	validation-mlogloss:2.13234
[76]	train-mlogloss:2.12366	validation-mlogloss:2.12713
[77]	train-mlogloss:2.11883	validation-mlogloss:2.12231
[78]	train-mlogloss:2.11404	validation-mlogloss:2.11760
[79]	train-mlogloss:2.10915	validation-mlogloss:2.11282
[80]	train-mlogloss:2.10429	validation-mlogloss:2.10804
[81]	train-mlogloss:2.09947	validation-mlogloss:2.10328
[82]	train-mlogloss:2.09455	validation-mlogloss:2.09857
[83]	train-mlogloss:2.08967	validation-mlogloss:2.09388
[84]	train-mlogloss:2.08507	validation-mlogloss:2.08946
[85]	train-mlogloss:2.08041	validation-mlogloss:2.08495
[86]	train-mlogloss:2.07568	validation-mlogloss:2.08025
[87]	train-mlogloss:2.07126	validation-mlogloss:2.07600
[88]	train-mlogloss:2.06692	validation-mlogloss:2.07183
[89]	train-mlogloss:2.06236	validation-mlogloss:2.06743
[90]	train-mlogloss:2.05785	validation-mlogloss:2.06285
[91]	train-mlogloss:2.05322	validation-mlogloss:2.05833
[92]	train-mlogloss:2.04879	validation-mlogloss:2.05400
[93]	train-mlogloss:2.04444	validation-mlogloss:2.04989
[94]	train-mlogloss:2.04032	validation-mlogloss:2.04592
[95]	train-mlogloss:2.03602	validation-mlogloss:2.04177
[96]	train-mlogloss:2.03187	validation-mlogloss:2.03787
[97]	train-mlogloss:2.02787	validation-mlogloss:2.03404
[98]	train-mlogloss:2.02383	validation-mlogloss:2.03000
[99]	train-mlogloss:2.01965	validation-mlogloss:2.02596
[100]	train-mlogloss:2.01568	validation-mlogloss:2.02207
[101]	train-mlogloss:2.01165	validation-mlogloss:2.01850
[102]	train-mlogloss:2.00795	validation-mlogloss:2.01482
[103]	train-mlogloss:2.00396	validation-mlogloss:2.01103
[104]	train-mlogloss:2.00005	validation-mlogloss:2.00714
[105]	train-mlogloss:1.99608	validation-mlogloss:2.00336
[106]	train-mlogloss:1.99229	validation-mlogloss:1.99982
[107]	train-mlogloss:1.98843	validation-mlogloss:1.99632
[108]	train-mlogloss:1.98464	validation-mlogloss:1.99269
[109]	train-mlogloss:1.98090	validation-mlogloss:1.98910
[110]	train-mlogloss:1.97724	validation-mlogloss:1.98555
[111]	train-mlogloss:1.97359	validation-mlogloss:1.98211
[112]	train-mlogloss:1.97009	validation-mlogloss:1.97878
[113]	train-mlogloss:1.96625	validation-mlogloss:1.97513
[114]	train-mlogloss:1.96258	validation-mlogloss:1.97170
[115]	train-mlogloss:1.95889	validation-mlogloss:1.96819
[116]	train-mlogloss:1.95547	validation-mlogloss:1.96489
[117]	train-mlogloss:1.95189	validation-mlogloss:1.96135
[118]	train-mlogloss:1.94837	validation-mlogloss:1.95791
[119]	train-mlogloss:1.94492	validation-mlogloss:1.95465
[120]	train-mlogloss:1.94140	validation-mlogloss:1.95137
[121]	train-mlogloss:1.93817	validation-mlogloss:1.94831
[122]	train-mlogloss:1.93486	validation-mlogloss:1.94532
[123]	train-mlogloss:1.93133	validation-mlogloss:1.94187
[124]	train-mlogloss:1.92811	validation-mlogloss:1.93877
[125]	train-mlogloss:1.92480	validation-mlogloss:1.93549
[126]	train-mlogloss:1.92155	validation-mlogloss:1.93236
[127]	train-mlogloss:1.91832	validation-mlogloss:1.92928
[128]	train-mlogloss:1.91525	validation-mlogloss:1.92631
[129]	train-mlogloss:1.91189	validation-mlogloss:1.92331
[130]	train-mlogloss:1.90877	validation-mlogloss:1.92030
[131]	train-mlogloss:1.90550	validation-mlogloss:1.91712
[132]	train-mlogloss:1.90251	validation-mlogloss:1.91396
[133]	train-mlogloss:1.89936	validation-mlogloss:1.91101
[134]	train-mlogloss:1.89621	validation-mlogloss:1.90797
[135]	train-mlogloss:1.89337	validation-mlogloss:1.90534
[136]	train-mlogloss:1.89041	validation-mlogloss:1.90271
[137]	train-mlogloss:1.88741	validation-mlogloss:1.90001
[138]	train-mlogloss:1.88442	validation-mlogloss:1.89720
[139]	train-mlogloss:1.88153	validation-mlogloss:1.89458
[140]	train-mlogloss:1.87865	validation-mlogloss:1.89198
[141]	train-mlogloss:1.87565	validation-mlogloss:1.88912
[142]	train-mlogloss:1.87283	validation-mlogloss:1.88650
[143]	train-mlogloss:1.87009	validation-mlogloss:1.88396
[144]	train-mlogloss:1.86716	validation-mlogloss:1.88117
[145]	train-mlogloss:1.86414	validation-mlogloss:1.87829
[146]	train-mlogloss:1.86133	validation-mlogloss:1.87553
[147]	train-mlogloss:1.85836	validation-mlogloss:1.87266
[148]	train-mlogloss:1.85552	validation-mlogloss:1.86998
[149]	train-mlogloss:1.85271	validation-mlogloss:1.86729
[150]	train-mlogloss:1.85009	validation-mlogloss:1.86491
[151]	train-mlogloss:1.84742	validation-mlogloss:1.86246
[152]	train-mlogloss:1.84480	validation-mlogloss:1.85997
[153]	train-mlogloss:1.84193	validation-mlogloss:1.85727
[154]	train-mlogloss:1.83920	validation-mlogloss:1.85479
[155]	train-mlogloss:1.83667	validation-mlogloss:1.85249
[156]	train-mlogloss:1.83412	validation-mlogloss:1.85005
[157]	train-mlogloss:1.83148	validation-mlogloss:1.84745
[158]	train-mlogloss:1.82894	validation-mlogloss:1.84504
[159]	train-mlogloss:1.82643	validation-mlogloss:1.84271
[160]	train-mlogloss:1.82376	validation-mlogloss:1.84029
[161]	train-mlogloss:1.82128	validation-mlogloss:1.83804
[162]	train-mlogloss:1.81858	validation-mlogloss:1.83548
[163]	train-mlogloss:1.81612	validation-mlogloss:1.83309
[164]	train-mlogloss:1.81367	validation-mlogloss:1.83069
[165]	train-mlogloss:1.81120	validation-mlogloss:1.82844
[166]	train-mlogloss:1.80867	validation-mlogloss:1.82605
[167]	train-mlogloss:1.80630	validation-mlogloss:1.82380
[168]	train-mlogloss:1.80388	validation-mlogloss:1.82157
[169]	train-mlogloss:1.80152	validation-mlogloss:1.81931
[170]	train-mlogloss:1.79923	validation-mlogloss:1.81703
[171]	train-mlogloss:1.79696	validation-mlogloss:1.81495
[172]	train-mlogloss:1.79474	validation-mlogloss:1.81281
[173]	train-mlogloss:1.79239	validation-mlogloss:1.81061
[174]	train-mlogloss:1.79007	validation-mlogloss:1.80851
[175]	train-mlogloss:1.78785	validation-mlogloss:1.80643
[176]	train-mlogloss:1.78550	validation-mlogloss:1.80431
[177]	train-mlogloss:1.78306	validation-mlogloss:1.80196
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[180]	train-mlogloss:1.77614	validation-mlogloss:1.79553
[181]	train-mlogloss:1.77403	validation-mlogloss:1.79352
[182]	train-mlogloss:1.77184	validation-mlogloss:1.79151
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[185]	train-mlogloss:1.76529	validation-mlogloss:1.78541
[186]	train-mlogloss:1.76313	validation-mlogloss:1.78324
[187]	train-mlogloss:1.76106	validation-mlogloss:1.78126
[188]	train-mlogloss:1.75903	validation-mlogloss:1.77931
[189]	train-mlogloss:1.75686	validation-mlogloss:1.77732
[190]	train-mlogloss:1.75477	validation-mlogloss:1.77533
[191]	train-mlogloss:1.75269	validation-mlogloss:1.77329
[192]	train-mlogloss:1.75073	validation-mlogloss:1.77137
[193]	train-mlogloss:1.74868	validation-mlogloss:1.76932
[194]	train-mlogloss:1.74681	validation-mlogloss:1.76753
[195]	train-mlogloss:1.74491	validation-mlogloss:1.76564
[196]	train-mlogloss:1.74299	validation-mlogloss:1.76391
[197]	train-mlogloss:1.74110	validation-mlogloss:1.76209
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[500]	train-mlogloss:1.46013	validation-mlogloss:1.51847
[501]	train-mlogloss:1.45970	validation-mlogloss:1.51821
[502]	train-mlogloss:1.45920	validation-mlogloss:1.51793
[503]	train-mlogloss:1.45873	validation-mlogloss:1.51755
[504]	train-mlogloss:1.45824	validation-mlogloss:1.51732
[505]	train-mlogloss:1.45778	validation-mlogloss:1.51704
[506]	train-mlogloss:1.45739	validation-mlogloss:1.51670
[507]	train-mlogloss:1.45695	validation-mlogloss:1.51642
[508]	train-mlogloss:1.45655	validation-mlogloss:1.51622
[509]	train-mlogloss:1.45614	validation-mlogloss:1.51600
[510]	train-mlogloss:1.45577	validation-mlogloss:1.51560
[511]	train-mlogloss:1.45533	validation-mlogloss:1.51530
[512]	train-mlogloss:1.45491	validation-mlogloss:1.51493
[513]	train-mlogloss:1.45450	validation-mlogloss:1.51472
[514]	train-mlogloss:1.45410	validation-mlogloss:1.51439
[515]	train-mlogloss:1.45366	validation-mlogloss:1.51402
[516]	train-mlogloss:1.45323	validation-mlogloss:1.51366
[517]	train-mlogloss:1.45282	validation-mlogloss:1.51346
[518]	train-mlogloss:1.45240	validation-mlogloss:1.51311
[519]	train-mlogloss:1.45197	validation-mlogloss:1.51288
[520]	train-mlogloss:1.45153	validation-mlogloss:1.51265
[521]	train-mlogloss:1.45113	validation-mlogloss:1.51240
[522]	train-mlogloss:1.45070	validation-mlogloss:1.51205
[523]	train-mlogloss:1.45026	validation-mlogloss:1.51179
[524]	train-mlogloss:1.44983	validation-mlogloss:1.51145
[525]	train-mlogloss:1.44937	validation-mlogloss:1.51106
[526]	train-mlogloss:1.44897	validation-mlogloss:1.51081
[527]	train-mlogloss:1.44856	validation-mlogloss:1.51055
[528]	train-mlogloss:1.44817	validation-mlogloss:1.51038
[529]	train-mlogloss:1.44778	validation-mlogloss:1.51010
[530]	train-mlogloss:1.44728	validation-mlogloss:1.50974
[531]	train-mlogloss:1.44680	validation-mlogloss:1.50932
[532]	train-mlogloss:1.44639	validation-mlogloss:1.50910
[533]	train-mlogloss:1.44593	validation-mlogloss:1.50887
[534]	train-mlogloss:1.44558	validation-mlogloss:1.50863
[535]	train-mlogloss:1.44518	validation-mlogloss:1.50829
[536]	train-mlogloss:1.44479	validation-mlogloss:1.50799
[537]	train-mlogloss:1.44447	validation-mlogloss:1.50769
[538]	train-mlogloss:1.44414	validation-mlogloss:1.50747
[539]	train-mlogloss:1.44371	validation-mlogloss:1.50722
[540]	train-mlogloss:1.44330	validation-mlogloss:1.50695
[541]	train-mlogloss:1.44297	validation-mlogloss:1.50665
[542]	train-mlogloss:1.44255	validation-mlogloss:1.50639
[543]	train-mlogloss:1.44220	validation-mlogloss:1.50610
[544]	train-mlogloss:1.44188	validation-mlogloss:1.50585
[545]	train-mlogloss:1.44151	validation-mlogloss:1.50562
[546]	train-mlogloss:1.44114	validation-mlogloss:1.50530
[547]	train-mlogloss:1.44074	validation-mlogloss:1.50514
[548]	train-mlogloss:1.44035	validation-mlogloss:1.50490
[549]	train-mlogloss:1.43996	validation-mlogloss:1.50467
[550]	train-mlogloss:1.43959	validation-mlogloss:1.50452
[551]	train-mlogloss:1.43922	validation-mlogloss:1.50431
[552]	train-mlogloss:1.43881	validation-mlogloss:1.50405
[553]	train-mlogloss:1.43840	validation-mlogloss:1.50378
[554]	train-mlogloss:1.43807	validation-mlogloss:1.50352
[555]	train-mlogloss:1.43770	validation-mlogloss:1.50327
[556]	train-mlogloss:1.43737	validation-mlogloss:1.50297
[557]	train-mlogloss:1.43695	validation-mlogloss:1.50278
[558]	train-mlogloss:1.43658	validation-mlogloss:1.50255
[559]	train-mlogloss:1.43615	validation-mlogloss:1.50239
[560]	train-mlogloss:1.43576	validation-mlogloss:1.50210
[561]	train-mlogloss:1.43540	validation-mlogloss:1.50188
[562]	train-mlogloss:1.43501	validation-mlogloss:1.50159
[563]	train-mlogloss:1.43469	validation-mlogloss:1.50139
[564]	train-mlogloss:1.43428	validation-mlogloss:1.50114
[565]	train-mlogloss:1.43392	validation-mlogloss:1.50076
[566]	train-mlogloss:1.43356	validation-mlogloss:1.50047
[567]	train-mlogloss:1.43317	validation-mlogloss:1.50018
[568]	train-mlogloss:1.43281	validation-mlogloss:1.50005
[569]	train-mlogloss:1.43244	validation-mlogloss:1.49983
[570]	train-mlogloss:1.43209	validation-mlogloss:1.49954
[571]	train-mlogloss:1.43174	validation-mlogloss:1.49930
[572]	train-mlogloss:1.43137	validation-mlogloss:1.49900
[573]	train-mlogloss:1.43104	validation-mlogloss:1.49871
[574]	train-mlogloss:1.43070	validation-mlogloss:1.49840
[575]	train-mlogloss:1.43032	validation-mlogloss:1.49822
[576]	train-mlogloss:1.42993	validation-mlogloss:1.49810
[577]	train-mlogloss:1.42964	validation-mlogloss:1.49793
[578]	train-mlogloss:1.42932	validation-mlogloss:1.49776
[579]	train-mlogloss:1.42892	validation-mlogloss:1.49757
[580]	train-mlogloss:1.42862	validation-mlogloss:1.49730
[581]	train-mlogloss:1.42830	validation-mlogloss:1.49714
[582]	train-mlogloss:1.42793	validation-mlogloss:1.49695
[583]	train-mlogloss:1.42751	validation-mlogloss:1.49670
[584]	train-mlogloss:1.42719	validation-mlogloss:1.49646
[585]	train-mlogloss:1.42683	validation-mlogloss:1.49629
[586]	train-mlogloss:1.42645	validation-mlogloss:1.49607
[587]	train-mlogloss:1.42611	validation-mlogloss:1.49589
[588]	train-mlogloss:1.42581	validation-mlogloss:1.49573
[589]	train-mlogloss:1.42548	validation-mlogloss:1.49548
[590]	train-mlogloss:1.42517	validation-mlogloss:1.49531
[591]	train-mlogloss:1.42483	validation-mlogloss:1.49508
[592]	train-mlogloss:1.42451	validation-mlogloss:1.49492
[593]	train-mlogloss:1.42421	validation-mlogloss:1.49471
[594]	train-mlogloss:1.42388	validation-mlogloss:1.49448
[595]	train-mlogloss:1.42353	validation-mlogloss:1.49430
[596]	train-mlogloss:1.42315	validation-mlogloss:1.49412
[597]	train-mlogloss:1.42282	validation-mlogloss:1.49392
[598]	train-mlogloss:1.42249	validation-mlogloss:1.49374
[599]	train-mlogloss:1.42217	validation-mlogloss:1.49358
[600]	train-mlogloss:1.42184	validation-mlogloss:1.49333
[601]	train-mlogloss:1.42152	validation-mlogloss:1.49311
[602]	train-mlogloss:1.42115	validation-mlogloss:1.49299
[603]	train-mlogloss:1.42087	validation-mlogloss:1.49276
[604]	train-mlogloss:1.42054	validation-mlogloss:1.49265
[605]	train-mlogloss:1.42024	validation-mlogloss:1.49239
[606]	train-mlogloss:1.41991	validation-mlogloss:1.49227
[607]	train-mlogloss:1.41961	validation-mlogloss:1.49206
[608]	train-mlogloss:1.41933	validation-mlogloss:1.49195
[609]	train-mlogloss:1.41905	validation-mlogloss:1.49176
[610]	train-mlogloss:1.41876	validation-mlogloss:1.49154
[611]	train-mlogloss:1.41844	validation-mlogloss:1.49139
[612]	train-mlogloss:1.41817	validation-mlogloss:1.49118
[613]	train-mlogloss:1.41783	validation-mlogloss:1.49101
[614]	train-mlogloss:1.41760	validation-mlogloss:1.49078
[615]	train-mlogloss:1.41722	validation-mlogloss:1.49056
[616]	train-mlogloss:1.41697	validation-mlogloss:1.49045
[617]	train-mlogloss:1.41671	validation-mlogloss:1.49026
[618]	train-mlogloss:1.41644	validation-mlogloss:1.49005
[619]	train-mlogloss:1.41616	validation-mlogloss:1.48976
[620]	train-mlogloss:1.41584	validation-mlogloss:1.48959
[621]	train-mlogloss:1.41552	validation-mlogloss:1.48942
[622]	train-mlogloss:1.41520	validation-mlogloss:1.48920
[623]	train-mlogloss:1.41491	validation-mlogloss:1.48902
[624]	train-mlogloss:1.41462	validation-mlogloss:1.48886
[625]	train-mlogloss:1.41427	validation-mlogloss:1.48858
[626]	train-mlogloss:1.41392	validation-mlogloss:1.48834
[627]	train-mlogloss:1.41363	validation-mlogloss:1.48818
[628]	train-mlogloss:1.41335	validation-mlogloss:1.48799
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.0 [%]
Global accuracy score (test) = 43.84 [%]
Global F1 score (train) = 55.81 [%]
Global F1 score (test) = 42.87 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.22      0.22       184
 CAMINAR CON MÓVIL O LIBRO       0.32      0.41      0.36       184
       CAMINAR USUAL SPEED       0.26      0.14      0.18       184
            CAMINAR ZIGZAG       0.30      0.28      0.29       184
          DE PIE BARRIENDO       0.44      0.46      0.45       184
   DE PIE DOBLANDO TOALLAS       0.46      0.42      0.44       184
    DE PIE MOVIENDO LIBROS       0.39      0.40      0.39       184
          DE PIE USANDO PC       0.52      0.65      0.58       184
        FASE REPOSO CON K5       0.41      0.81      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.29      0.25      0.27       184
         SENTADO USANDO PC       0.16      0.06      0.09       184
      SENTADO VIENDO LA TV       0.40      0.38      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 13: 43.84 [%]
F1-score capturado en la ejecución 13: 42.87 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.15 [%]
Global accuracy score (test) = 43.77 [%]
Global F1 score (train) = 56.0 [%]
Global F1 score (test) = 42.82 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.19      0.19       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.40      0.34       184
       CAMINAR USUAL SPEED       0.27      0.14      0.19       184
            CAMINAR ZIGZAG       0.30      0.31      0.31       184
          DE PIE BARRIENDO       0.48      0.51      0.49       184
   DE PIE DOBLANDO TOALLAS       0.44      0.42      0.43       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.52      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.26      0.21      0.23       184
         SENTADO USANDO PC       0.19      0.08      0.11       184
      SENTADO VIENDO LA TV       0.38      0.35      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.59      0.74      0.66       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 14: 43.77 [%]
F1-score capturado en la ejecución 14: 42.82 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.96 [%]
Global accuracy score (test) = 43.33 [%]
Global F1 score (train) = 55.77 [%]
Global F1 score (test) = 42.31 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.22      0.23       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.37      0.33       184
       CAMINAR USUAL SPEED       0.23      0.11      0.15       184
            CAMINAR ZIGZAG       0.27      0.29      0.28       184
          DE PIE BARRIENDO       0.46      0.49      0.47       184
   DE PIE DOBLANDO TOALLAS       0.44      0.42      0.43       184
    DE PIE MOVIENDO LIBROS       0.38      0.38      0.38       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.28      0.24      0.26       184
         SENTADO USANDO PC       0.15      0.06      0.09       184
      SENTADO VIENDO LA TV       0.38      0.34      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.43      0.42      2737


Accuracy capturado en la ejecución 15: 43.33 [%]
F1-score capturado en la ejecución 15: 42.31 [%]

=== EJECUCIÓN 16 ===

--- TRAIN (ejecución 16) ---

--- TEST (ejecución 16) ---
2025-11-07 10:25:58.158509: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:25:58.169695: 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 00:00:1762507558.182893 2385670 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507558.186845 2385670 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507558.196865 2385670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507558.196885 2385670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507558.196887 2385670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507558.196889 2385670 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:25:58.199857: 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:2.69753	validation-mlogloss:2.69751
[1]	train-mlogloss:2.68734	validation-mlogloss:2.68744
[2]	train-mlogloss:2.67726	validation-mlogloss:2.67737
[3]	train-mlogloss:2.66657	validation-mlogloss:2.66661
[4]	train-mlogloss:2.65578	validation-mlogloss:2.65578
[5]	train-mlogloss:2.64565	validation-mlogloss:2.64563
[6]	train-mlogloss:2.63495	validation-mlogloss:2.63500
[7]	train-mlogloss:2.62511	validation-mlogloss:2.62524
[8]	train-mlogloss:2.61506	validation-mlogloss:2.61530
[9]	train-mlogloss:2.60551	validation-mlogloss:2.60568
[10]	train-mlogloss:2.59570	validation-mlogloss:2.59565
[11]	train-mlogloss:2.58566	validation-mlogloss:2.58559
[12]	train-mlogloss:2.57619	validation-mlogloss:2.57604
[13]	train-mlogloss:2.56668	validation-mlogloss:2.56637
[14]	train-mlogloss:2.55690	validation-mlogloss:2.55653
[15]	train-mlogloss:2.54728	validation-mlogloss:2.54685
[16]	train-mlogloss:2.53791	validation-mlogloss:2.53733
[17]	train-mlogloss:2.52884	validation-mlogloss:2.52848
[18]	train-mlogloss:2.51950	validation-mlogloss:2.51920
[19]	train-mlogloss:2.51079	validation-mlogloss:2.51071
[20]	train-mlogloss:2.50167	validation-mlogloss:2.50153
[21]	train-mlogloss:2.49314	validation-mlogloss:2.49275
[22]	train-mlogloss:2.48463	validation-mlogloss:2.48433
[23]	train-mlogloss:2.47672	validation-mlogloss:2.47647
[24]	train-mlogloss:2.46785	validation-mlogloss:2.46761
[25]	train-mlogloss:2.45908	validation-mlogloss:2.45862
[26]	train-mlogloss:2.45027	validation-mlogloss:2.44982
[27]	train-mlogloss:2.44203	validation-mlogloss:2.44149
[28]	train-mlogloss:2.43386	validation-mlogloss:2.43332
[29]	train-mlogloss:2.42554	validation-mlogloss:2.42486
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[628]	train-mlogloss:1.41231	validation-mlogloss:1.48815
Saved model to disk.
2025-11-07 10:26:24.458425: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:26:24.469657: 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 00:00:1762507584.482637 2386086 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507584.486749 2386086 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507584.496773 2386086 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507584.496792 2386086 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507584.496802 2386086 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507584.496804 2386086 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:26:24.499904: 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:2.69717	validation-mlogloss:2.69701
[1]	train-mlogloss:2.68628	validation-mlogloss:2.68627
[2]	train-mlogloss:2.67570	validation-mlogloss:2.67555
[3]	train-mlogloss:2.66511	validation-mlogloss:2.66498
[4]	train-mlogloss:2.65501	validation-mlogloss:2.65473
[5]	train-mlogloss:2.64507	validation-mlogloss:2.64485
[6]	train-mlogloss:2.63486	validation-mlogloss:2.63457
[7]	train-mlogloss:2.62463	validation-mlogloss:2.62423
[8]	train-mlogloss:2.61453	validation-mlogloss:2.61395
[9]	train-mlogloss:2.60422	validation-mlogloss:2.60357
[10]	train-mlogloss:2.59478	validation-mlogloss:2.59407
[11]	train-mlogloss:2.58520	validation-mlogloss:2.58446
[12]	train-mlogloss:2.57560	validation-mlogloss:2.57470
[13]	train-mlogloss:2.56620	validation-mlogloss:2.56512
[14]	train-mlogloss:2.55715	validation-mlogloss:2.55592
[15]	train-mlogloss:2.54808	validation-mlogloss:2.54687
[16]	train-mlogloss:2.53837	validation-mlogloss:2.53713
[17]	train-mlogloss:2.52933	validation-mlogloss:2.52807
[18]	train-mlogloss:2.52059	validation-mlogloss:2.51917
[19]	train-mlogloss:2.51209	validation-mlogloss:2.51057
[20]	train-mlogloss:2.50321	validation-mlogloss:2.50179
[21]	train-mlogloss:2.49443	validation-mlogloss:2.49306
[22]	train-mlogloss:2.48538	validation-mlogloss:2.48404
[23]	train-mlogloss:2.47665	validation-mlogloss:2.47535
[24]	train-mlogloss:2.46817	validation-mlogloss:2.46672
[25]	train-mlogloss:2.45984	validation-mlogloss:2.45833
[26]	train-mlogloss:2.45112	validation-mlogloss:2.44940
[27]	train-mlogloss:2.44234	validation-mlogloss:2.44064
[28]	train-mlogloss:2.43403	validation-mlogloss:2.43225
[29]	train-mlogloss:2.42588	validation-mlogloss:2.42402
[30]	train-mlogloss:2.41773	validation-mlogloss:2.41604
[31]	train-mlogloss:2.40934	validation-mlogloss:2.40777
[32]	train-mlogloss:2.40169	validation-mlogloss:2.40009
[33]	train-mlogloss:2.39390	validation-mlogloss:2.39226
[34]	train-mlogloss:2.38633	validation-mlogloss:2.38459
[35]	train-mlogloss:2.37871	validation-mlogloss:2.37713
[36]	train-mlogloss:2.37162	validation-mlogloss:2.36992
[37]	train-mlogloss:2.36445	validation-mlogloss:2.36272
[38]	train-mlogloss:2.35727	validation-mlogloss:2.35528
[39]	train-mlogloss:2.34980	validation-mlogloss:2.34773
[40]	train-mlogloss:2.34271	validation-mlogloss:2.34065
[41]	train-mlogloss:2.33521	validation-mlogloss:2.33331
[42]	train-mlogloss:2.32826	validation-mlogloss:2.32630
[43]	train-mlogloss:2.32149	validation-mlogloss:2.31974
[44]	train-mlogloss:2.31435	validation-mlogloss:2.31273
[45]	train-mlogloss:2.30734	validation-mlogloss:2.30570
[46]	train-mlogloss:2.30055	validation-mlogloss:2.29896
[47]	train-mlogloss:2.29359	validation-mlogloss:2.29216
[48]	train-mlogloss:2.28684	validation-mlogloss:2.28572
[49]	train-mlogloss:2.28015	validation-mlogloss:2.27915
[50]	train-mlogloss:2.27365	validation-mlogloss:2.27322
[51]	train-mlogloss:2.26717	validation-mlogloss:2.26685
[52]	train-mlogloss:2.26071	validation-mlogloss:2.26035
[53]	train-mlogloss:2.25425	validation-mlogloss:2.25416
[54]	train-mlogloss:2.24811	validation-mlogloss:2.24826
[55]	train-mlogloss:2.24187	validation-mlogloss:2.24207
[56]	train-mlogloss:2.23561	validation-mlogloss:2.23565
[57]	train-mlogloss:2.22965	validation-mlogloss:2.22984
[58]	train-mlogloss:2.22332	validation-mlogloss:2.22379
[59]	train-mlogloss:2.21728	validation-mlogloss:2.21780
[60]	train-mlogloss:2.21155	validation-mlogloss:2.21228
[61]	train-mlogloss:2.20519	validation-mlogloss:2.20601
[62]	train-mlogloss:2.19930	validation-mlogloss:2.20004
[63]	train-mlogloss:2.19369	validation-mlogloss:2.19457
[64]	train-mlogloss:2.18775	validation-mlogloss:2.18884
[65]	train-mlogloss:2.18216	validation-mlogloss:2.18348
[66]	train-mlogloss:2.17652	validation-mlogloss:2.17777
[67]	train-mlogloss:2.17093	validation-mlogloss:2.17224
[68]	train-mlogloss:2.16541	validation-mlogloss:2.16692
[69]	train-mlogloss:2.16016	validation-mlogloss:2.16197
[70]	train-mlogloss:2.15505	validation-mlogloss:2.15721
[71]	train-mlogloss:2.14975	validation-mlogloss:2.15216
[72]	train-mlogloss:2.14453	validation-mlogloss:2.14710
[73]	train-mlogloss:2.13935	validation-mlogloss:2.14188
[74]	train-mlogloss:2.13425	validation-mlogloss:2.13689
[75]	train-mlogloss:2.12924	validation-mlogloss:2.13206
[76]	train-mlogloss:2.12430	validation-mlogloss:2.12704
[77]	train-mlogloss:2.11923	validation-mlogloss:2.12213
[78]	train-mlogloss:2.11428	validation-mlogloss:2.11732
[79]	train-mlogloss:2.10931	validation-mlogloss:2.11254
[80]	train-mlogloss:2.10449	validation-mlogloss:2.10790
[81]	train-mlogloss:2.09980	validation-mlogloss:2.10325
[82]	train-mlogloss:2.09485	validation-mlogloss:2.09858
[83]	train-mlogloss:2.09011	validation-mlogloss:2.09399
[84]	train-mlogloss:2.08527	validation-mlogloss:2.08926
[85]	train-mlogloss:2.08057	validation-mlogloss:2.08476
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[88]	train-mlogloss:2.06674	validation-mlogloss:2.07160
[89]	train-mlogloss:2.06243	validation-mlogloss:2.06739
[90]	train-mlogloss:2.05822	validation-mlogloss:2.06328
[91]	train-mlogloss:2.05380	validation-mlogloss:2.05899
[92]	train-mlogloss:2.04951	validation-mlogloss:2.05488
[93]	train-mlogloss:2.04520	validation-mlogloss:2.05078
[94]	train-mlogloss:2.04098	validation-mlogloss:2.04666
[95]	train-mlogloss:2.03685	validation-mlogloss:2.04252
[96]	train-mlogloss:2.03273	validation-mlogloss:2.03877
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[98]	train-mlogloss:2.02418	validation-mlogloss:2.03078
[99]	train-mlogloss:2.02006	validation-mlogloss:2.02665
[100]	train-mlogloss:2.01614	validation-mlogloss:2.02292
[101]	train-mlogloss:2.01204	validation-mlogloss:2.01907
[102]	train-mlogloss:2.00819	validation-mlogloss:2.01545
[103]	train-mlogloss:2.00427	validation-mlogloss:2.01169
[104]	train-mlogloss:2.00041	validation-mlogloss:2.00817
[105]	train-mlogloss:1.99642	validation-mlogloss:2.00421
[106]	train-mlogloss:1.99255	validation-mlogloss:2.00028
[107]	train-mlogloss:1.98869	validation-mlogloss:1.99661
[108]	train-mlogloss:1.98515	validation-mlogloss:1.99336
[109]	train-mlogloss:1.98149	validation-mlogloss:1.98988
[110]	train-mlogloss:1.97775	validation-mlogloss:1.98615
[111]	train-mlogloss:1.97425	validation-mlogloss:1.98270
[112]	train-mlogloss:1.97062	validation-mlogloss:1.97907
[113]	train-mlogloss:1.96685	validation-mlogloss:1.97549
[114]	train-mlogloss:1.96329	validation-mlogloss:1.97204
[115]	train-mlogloss:1.95969	validation-mlogloss:1.96862
[116]	train-mlogloss:1.95598	validation-mlogloss:1.96520
[117]	train-mlogloss:1.95245	validation-mlogloss:1.96171
[118]	train-mlogloss:1.94896	validation-mlogloss:1.95845
[119]	train-mlogloss:1.94548	validation-mlogloss:1.95510
[120]	train-mlogloss:1.94207	validation-mlogloss:1.95189
[121]	train-mlogloss:1.93860	validation-mlogloss:1.94857
[122]	train-mlogloss:1.93534	validation-mlogloss:1.94549
[123]	train-mlogloss:1.93205	validation-mlogloss:1.94228
[124]	train-mlogloss:1.92851	validation-mlogloss:1.93884
[125]	train-mlogloss:1.92503	validation-mlogloss:1.93545
[126]	train-mlogloss:1.92181	validation-mlogloss:1.93265
[127]	train-mlogloss:1.91859	validation-mlogloss:1.92957
[128]	train-mlogloss:1.91532	validation-mlogloss:1.92657
[129]	train-mlogloss:1.91216	validation-mlogloss:1.92357
[130]	train-mlogloss:1.90896	validation-mlogloss:1.92061
[131]	train-mlogloss:1.90583	validation-mlogloss:1.91756
[132]	train-mlogloss:1.90271	validation-mlogloss:1.91465
[133]	train-mlogloss:1.89950	validation-mlogloss:1.91154
[134]	train-mlogloss:1.89647	validation-mlogloss:1.90863
[135]	train-mlogloss:1.89331	validation-mlogloss:1.90574
[136]	train-mlogloss:1.89039	validation-mlogloss:1.90316
[137]	train-mlogloss:1.88725	validation-mlogloss:1.90016
[138]	train-mlogloss:1.88435	validation-mlogloss:1.89727
[139]	train-mlogloss:1.88141	validation-mlogloss:1.89443
[140]	train-mlogloss:1.87850	validation-mlogloss:1.89164
[141]	train-mlogloss:1.87576	validation-mlogloss:1.88899
[142]	train-mlogloss:1.87267	validation-mlogloss:1.88635
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[144]	train-mlogloss:1.86689	validation-mlogloss:1.88106
[145]	train-mlogloss:1.86391	validation-mlogloss:1.87834
[146]	train-mlogloss:1.86098	validation-mlogloss:1.87542
[147]	train-mlogloss:1.85822	validation-mlogloss:1.87301
[148]	train-mlogloss:1.85547	validation-mlogloss:1.87048
[149]	train-mlogloss:1.85279	validation-mlogloss:1.86793
[150]	train-mlogloss:1.84998	validation-mlogloss:1.86525
[151]	train-mlogloss:1.84729	validation-mlogloss:1.86277
[152]	train-mlogloss:1.84442	validation-mlogloss:1.86006
[153]	train-mlogloss:1.84168	validation-mlogloss:1.85766
[154]	train-mlogloss:1.83889	validation-mlogloss:1.85494
[155]	train-mlogloss:1.83631	validation-mlogloss:1.85247
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[157]	train-mlogloss:1.83112	validation-mlogloss:1.84748
[158]	train-mlogloss:1.82864	validation-mlogloss:1.84502
[159]	train-mlogloss:1.82613	validation-mlogloss:1.84256
[160]	train-mlogloss:1.82363	validation-mlogloss:1.84018
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[163]	train-mlogloss:1.81615	validation-mlogloss:1.83304
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[191]	train-mlogloss:1.75293	validation-mlogloss:1.77338
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[200]	train-mlogloss:1.73525	validation-mlogloss:1.75756
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[577]	train-mlogloss:1.42830	validation-mlogloss:1.49770
[578]	train-mlogloss:1.42791	validation-mlogloss:1.49748
[579]	train-mlogloss:1.42758	validation-mlogloss:1.49715
[580]	train-mlogloss:1.42725	validation-mlogloss:1.49688
[581]	train-mlogloss:1.42691	validation-mlogloss:1.49661
[582]	train-mlogloss:1.42655	validation-mlogloss:1.49636
[583]	train-mlogloss:1.42621	validation-mlogloss:1.49615
[584]	train-mlogloss:1.42587	validation-mlogloss:1.49592
[585]	train-mlogloss:1.42557	validation-mlogloss:1.49572
[586]	train-mlogloss:1.42526	validation-mlogloss:1.49551
[587]	train-mlogloss:1.42492	validation-mlogloss:1.49529
[588]	train-mlogloss:1.42455	validation-mlogloss:1.49506
[589]	train-mlogloss:1.42421	validation-mlogloss:1.49492
[590]	train-mlogloss:1.42382	validation-mlogloss:1.49476
[591]	train-mlogloss:1.42347	validation-mlogloss:1.49449
[592]	train-mlogloss:1.42311	validation-mlogloss:1.49432
[593]	train-mlogloss:1.42274	validation-mlogloss:1.49413
[594]	train-mlogloss:1.42242	validation-mlogloss:1.49396
[595]	train-mlogloss:1.42209	validation-mlogloss:1.49369
[596]	train-mlogloss:1.42171	validation-mlogloss:1.49352
[597]	train-mlogloss:1.42139	validation-mlogloss:1.49332
[598]	train-mlogloss:1.42112	validation-mlogloss:1.49314
[599]	train-mlogloss:1.42078	validation-mlogloss:1.49287
[600]	train-mlogloss:1.42047	validation-mlogloss:1.49275
[601]	train-mlogloss:1.42016	validation-mlogloss:1.49255
[602]	train-mlogloss:1.41989	validation-mlogloss:1.49231
[603]	train-mlogloss:1.41956	validation-mlogloss:1.49211
[604]	train-mlogloss:1.41924	validation-mlogloss:1.49199
[605]	train-mlogloss:1.41897	validation-mlogloss:1.49187
[606]	train-mlogloss:1.41863	validation-mlogloss:1.49170
[607]	train-mlogloss:1.41829	validation-mlogloss:1.49143
[608]	train-mlogloss:1.41803	validation-mlogloss:1.49122
[609]	train-mlogloss:1.41781	validation-mlogloss:1.49107
[610]	train-mlogloss:1.41750	validation-mlogloss:1.49085
[611]	train-mlogloss:1.41721	validation-mlogloss:1.49072
[612]	train-mlogloss:1.41690	validation-mlogloss:1.49053
[613]	train-mlogloss:1.41663	validation-mlogloss:1.49038
[614]	train-mlogloss:1.41627	validation-mlogloss:1.49021
[615]	train-mlogloss:1.41595	validation-mlogloss:1.49007
[616]	train-mlogloss:1.41562	validation-mlogloss:1.48981
[617]	train-mlogloss:1.41536	validation-mlogloss:1.48960
[618]	train-mlogloss:1.41502	validation-mlogloss:1.48937
[619]	train-mlogloss:1.41468	validation-mlogloss:1.48915
[620]	train-mlogloss:1.41440	validation-mlogloss:1.48898
[621]	train-mlogloss:1.41404	validation-mlogloss:1.48880
[622]	train-mlogloss:1.41377	validation-mlogloss:1.48866
[623]	train-mlogloss:1.41342	validation-mlogloss:1.48857
[624]	train-mlogloss:1.41314	validation-mlogloss:1.48840
[625]	train-mlogloss:1.41281	validation-mlogloss:1.48824
[626]	train-mlogloss:1.41258	validation-mlogloss:1.48807
[627]	train-mlogloss:1.41228	validation-mlogloss:1.48789
[628]	train-mlogloss:1.41200	validation-mlogloss:1.48779
Saved model to disk.
2025-11-07 10:26:51.169948: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:26:51.181254: 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 00:00:1762507611.194401 2386512 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507611.198579 2386512 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507611.209231 2386512 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507611.209251 2386512 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507611.209253 2386512 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507611.209255 2386512 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:26:51.212394: 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:2.69683	validation-mlogloss:2.69671
[1]	train-mlogloss:2.68613	validation-mlogloss:2.68605
[2]	train-mlogloss:2.67595	validation-mlogloss:2.67572
[3]	train-mlogloss:2.66599	validation-mlogloss:2.66567
[4]	train-mlogloss:2.65560	validation-mlogloss:2.65537
[5]	train-mlogloss:2.64490	validation-mlogloss:2.64478
[6]	train-mlogloss:2.63479	validation-mlogloss:2.63472
[7]	train-mlogloss:2.62479	validation-mlogloss:2.62473
[8]	train-mlogloss:2.61504	validation-mlogloss:2.61458
[9]	train-mlogloss:2.60508	validation-mlogloss:2.60469
[10]	train-mlogloss:2.59541	validation-mlogloss:2.59488
[11]	train-mlogloss:2.58596	validation-mlogloss:2.58532
[12]	train-mlogloss:2.57664	validation-mlogloss:2.57584
[13]	train-mlogloss:2.56753	validation-mlogloss:2.56673
[14]	train-mlogloss:2.55757	validation-mlogloss:2.55702
[15]	train-mlogloss:2.54835	validation-mlogloss:2.54778
[16]	train-mlogloss:2.53925	validation-mlogloss:2.53850
[17]	train-mlogloss:2.53012	validation-mlogloss:2.52918
[18]	train-mlogloss:2.52150	validation-mlogloss:2.52036
[19]	train-mlogloss:2.51202	validation-mlogloss:2.51086
[20]	train-mlogloss:2.50275	validation-mlogloss:2.50146
[21]	train-mlogloss:2.49375	validation-mlogloss:2.49235
[22]	train-mlogloss:2.48508	validation-mlogloss:2.48370
[23]	train-mlogloss:2.47641	validation-mlogloss:2.47492
[24]	train-mlogloss:2.46758	validation-mlogloss:2.46610
[25]	train-mlogloss:2.45878	validation-mlogloss:2.45717
[26]	train-mlogloss:2.45040	validation-mlogloss:2.44887
[27]	train-mlogloss:2.44205	validation-mlogloss:2.44043
[28]	train-mlogloss:2.43393	validation-mlogloss:2.43207
[29]	train-mlogloss:2.42567	validation-mlogloss:2.42377
[30]	train-mlogloss:2.41757	validation-mlogloss:2.41567
[31]	train-mlogloss:2.40973	validation-mlogloss:2.40785
[32]	train-mlogloss:2.40207	validation-mlogloss:2.40018
[33]	train-mlogloss:2.39392	validation-mlogloss:2.39201
[34]	train-mlogloss:2.38594	validation-mlogloss:2.38405
[35]	train-mlogloss:2.37815	validation-mlogloss:2.37639
[36]	train-mlogloss:2.37085	validation-mlogloss:2.36883
[37]	train-mlogloss:2.36350	validation-mlogloss:2.36155
[38]	train-mlogloss:2.35631	validation-mlogloss:2.35413
[39]	train-mlogloss:2.34901	validation-mlogloss:2.34698
[40]	train-mlogloss:2.34200	validation-mlogloss:2.34002
[41]	train-mlogloss:2.33482	validation-mlogloss:2.33307
[42]	train-mlogloss:2.32773	validation-mlogloss:2.32596
[43]	train-mlogloss:2.32064	validation-mlogloss:2.31892
[44]	train-mlogloss:2.31382	validation-mlogloss:2.31211
[45]	train-mlogloss:2.30688	validation-mlogloss:2.30525
[46]	train-mlogloss:2.30024	validation-mlogloss:2.29864
[47]	train-mlogloss:2.29359	validation-mlogloss:2.29214
[48]	train-mlogloss:2.28677	validation-mlogloss:2.28530
[49]	train-mlogloss:2.28025	validation-mlogloss:2.27872
[50]	train-mlogloss:2.27365	validation-mlogloss:2.27234
[51]	train-mlogloss:2.26697	validation-mlogloss:2.26594
[52]	train-mlogloss:2.26036	validation-mlogloss:2.25961
[53]	train-mlogloss:2.25382	validation-mlogloss:2.25316
[54]	train-mlogloss:2.24758	validation-mlogloss:2.24708
[55]	train-mlogloss:2.24128	validation-mlogloss:2.24083
[56]	train-mlogloss:2.23519	validation-mlogloss:2.23505
[57]	train-mlogloss:2.22924	validation-mlogloss:2.22933
[58]	train-mlogloss:2.22338	validation-mlogloss:2.22335
[59]	train-mlogloss:2.21738	validation-mlogloss:2.21735
[60]	train-mlogloss:2.21130	validation-mlogloss:2.21159
[61]	train-mlogloss:2.20567	validation-mlogloss:2.20587
[62]	train-mlogloss:2.19969	validation-mlogloss:2.20021
[63]	train-mlogloss:2.19365	validation-mlogloss:2.19443
[64]	train-mlogloss:2.18788	validation-mlogloss:2.18880
[65]	train-mlogloss:2.18208	validation-mlogloss:2.18320
[66]	train-mlogloss:2.17658	validation-mlogloss:2.17792
[67]	train-mlogloss:2.17082	validation-mlogloss:2.17207
[68]	train-mlogloss:2.16529	validation-mlogloss:2.16633
[69]	train-mlogloss:2.16007	validation-mlogloss:2.16111
[70]	train-mlogloss:2.15469	validation-mlogloss:2.15592
[71]	train-mlogloss:2.14936	validation-mlogloss:2.15091
[72]	train-mlogloss:2.14402	validation-mlogloss:2.14577
[73]	train-mlogloss:2.13885	validation-mlogloss:2.14092
[74]	train-mlogloss:2.13359	validation-mlogloss:2.13574
[75]	train-mlogloss:2.12832	validation-mlogloss:2.13057
[76]	train-mlogloss:2.12333	validation-mlogloss:2.12562
[77]	train-mlogloss:2.11842	validation-mlogloss:2.12083
[78]	train-mlogloss:2.11326	validation-mlogloss:2.11584
[79]	train-mlogloss:2.10804	validation-mlogloss:2.11095
[80]	train-mlogloss:2.10326	validation-mlogloss:2.10621
[81]	train-mlogloss:2.09860	validation-mlogloss:2.10142
[82]	train-mlogloss:2.09382	validation-mlogloss:2.09690
[83]	train-mlogloss:2.08918	validation-mlogloss:2.09219
[84]	train-mlogloss:2.08427	validation-mlogloss:2.08726
[85]	train-mlogloss:2.07987	validation-mlogloss:2.08294
[86]	train-mlogloss:2.07534	validation-mlogloss:2.07864
[87]	train-mlogloss:2.07073	validation-mlogloss:2.07422
[88]	train-mlogloss:2.06611	validation-mlogloss:2.06970
[89]	train-mlogloss:2.06162	validation-mlogloss:2.06523
[90]	train-mlogloss:2.05730	validation-mlogloss:2.06111
[91]	train-mlogloss:2.05267	validation-mlogloss:2.05655
[92]	train-mlogloss:2.04828	validation-mlogloss:2.05256
[93]	train-mlogloss:2.04390	validation-mlogloss:2.04827
[94]	train-mlogloss:2.03967	validation-mlogloss:2.04410
[95]	train-mlogloss:2.03527	validation-mlogloss:2.03994
[96]	train-mlogloss:2.03111	validation-mlogloss:2.03590
[97]	train-mlogloss:2.02697	validation-mlogloss:2.03198
[98]	train-mlogloss:2.02294	validation-mlogloss:2.02822
[99]	train-mlogloss:2.01875	validation-mlogloss:2.02413
[100]	train-mlogloss:2.01460	validation-mlogloss:2.02013
[101]	train-mlogloss:2.01049	validation-mlogloss:2.01623
[102]	train-mlogloss:2.00660	validation-mlogloss:2.01263
[103]	train-mlogloss:2.00268	validation-mlogloss:2.00876
[104]	train-mlogloss:1.99878	validation-mlogloss:2.00520
[105]	train-mlogloss:1.99485	validation-mlogloss:2.00137
[106]	train-mlogloss:1.99105	validation-mlogloss:1.99765
[107]	train-mlogloss:1.98741	validation-mlogloss:1.99429
[108]	train-mlogloss:1.98374	validation-mlogloss:1.99083
[109]	train-mlogloss:1.98004	validation-mlogloss:1.98741
[110]	train-mlogloss:1.97643	validation-mlogloss:1.98406
[111]	train-mlogloss:1.97279	validation-mlogloss:1.98052
[112]	train-mlogloss:1.96909	validation-mlogloss:1.97704
[113]	train-mlogloss:1.96564	validation-mlogloss:1.97364
[114]	train-mlogloss:1.96208	validation-mlogloss:1.97015
[115]	train-mlogloss:1.95842	validation-mlogloss:1.96675
[116]	train-mlogloss:1.95492	validation-mlogloss:1.96350
[117]	train-mlogloss:1.95143	validation-mlogloss:1.96012
[118]	train-mlogloss:1.94813	validation-mlogloss:1.95718
[119]	train-mlogloss:1.94463	validation-mlogloss:1.95384
[120]	train-mlogloss:1.94117	validation-mlogloss:1.95053
[121]	train-mlogloss:1.93778	validation-mlogloss:1.94745
[122]	train-mlogloss:1.93437	validation-mlogloss:1.94431
[123]	train-mlogloss:1.93104	validation-mlogloss:1.94125
[124]	train-mlogloss:1.92767	validation-mlogloss:1.93808
[125]	train-mlogloss:1.92454	validation-mlogloss:1.93514
[126]	train-mlogloss:1.92132	validation-mlogloss:1.93229
[127]	train-mlogloss:1.91823	validation-mlogloss:1.92947
[128]	train-mlogloss:1.91485	validation-mlogloss:1.92617
[129]	train-mlogloss:1.91162	validation-mlogloss:1.92297
[130]	train-mlogloss:1.90839	validation-mlogloss:1.91993
[131]	train-mlogloss:1.90522	validation-mlogloss:1.91691
[132]	train-mlogloss:1.90224	validation-mlogloss:1.91387
[133]	train-mlogloss:1.89905	validation-mlogloss:1.91080
[134]	train-mlogloss:1.89601	validation-mlogloss:1.90798
[135]	train-mlogloss:1.89300	validation-mlogloss:1.90516
[136]	train-mlogloss:1.89009	validation-mlogloss:1.90235
[137]	train-mlogloss:1.88684	validation-mlogloss:1.89927
[138]	train-mlogloss:1.88381	validation-mlogloss:1.89651
[139]	train-mlogloss:1.88087	validation-mlogloss:1.89372
[140]	train-mlogloss:1.87793	validation-mlogloss:1.89077
[141]	train-mlogloss:1.87500	validation-mlogloss:1.88802
[142]	train-mlogloss:1.87229	validation-mlogloss:1.88549
[143]	train-mlogloss:1.86950	validation-mlogloss:1.88282
[144]	train-mlogloss:1.86651	validation-mlogloss:1.88006
[145]	train-mlogloss:1.86358	validation-mlogloss:1.87717
[146]	train-mlogloss:1.86073	validation-mlogloss:1.87447
[147]	train-mlogloss:1.85818	validation-mlogloss:1.87215
[148]	train-mlogloss:1.85545	validation-mlogloss:1.86963
[149]	train-mlogloss:1.85281	validation-mlogloss:1.86701
[150]	train-mlogloss:1.84996	validation-mlogloss:1.86433
[151]	train-mlogloss:1.84721	validation-mlogloss:1.86174
[152]	train-mlogloss:1.84463	validation-mlogloss:1.85918
[153]	train-mlogloss:1.84210	validation-mlogloss:1.85671
[154]	train-mlogloss:1.83936	validation-mlogloss:1.85414
[155]	train-mlogloss:1.83672	validation-mlogloss:1.85164
[156]	train-mlogloss:1.83416	validation-mlogloss:1.84924
[157]	train-mlogloss:1.83164	validation-mlogloss:1.84674
[158]	train-mlogloss:1.82910	validation-mlogloss:1.84423
[159]	train-mlogloss:1.82664	validation-mlogloss:1.84198
[160]	train-mlogloss:1.82412	validation-mlogloss:1.83954
[161]	train-mlogloss:1.82167	validation-mlogloss:1.83736
[162]	train-mlogloss:1.81929	validation-mlogloss:1.83503
[163]	train-mlogloss:1.81697	validation-mlogloss:1.83279
[164]	train-mlogloss:1.81437	validation-mlogloss:1.83041
[165]	train-mlogloss:1.81174	validation-mlogloss:1.82794
[166]	train-mlogloss:1.80936	validation-mlogloss:1.82571
[167]	train-mlogloss:1.80699	validation-mlogloss:1.82353
[168]	train-mlogloss:1.80441	validation-mlogloss:1.82106
[169]	train-mlogloss:1.80209	validation-mlogloss:1.81880
[170]	train-mlogloss:1.79988	validation-mlogloss:1.81663
[171]	train-mlogloss:1.79759	validation-mlogloss:1.81430
[172]	train-mlogloss:1.79516	validation-mlogloss:1.81204
[173]	train-mlogloss:1.79278	validation-mlogloss:1.80957
[174]	train-mlogloss:1.79051	validation-mlogloss:1.80751
[175]	train-mlogloss:1.78823	validation-mlogloss:1.80538
[176]	train-mlogloss:1.78586	validation-mlogloss:1.80335
[177]	train-mlogloss:1.78364	validation-mlogloss:1.80112
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[179]	train-mlogloss:1.77914	validation-mlogloss:1.79698
[180]	train-mlogloss:1.77688	validation-mlogloss:1.79490
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[185]	train-mlogloss:1.76645	validation-mlogloss:1.78517
[186]	train-mlogloss:1.76417	validation-mlogloss:1.78323
[187]	train-mlogloss:1.76205	validation-mlogloss:1.78118
[188]	train-mlogloss:1.75992	validation-mlogloss:1.77907
[189]	train-mlogloss:1.75775	validation-mlogloss:1.77700
[190]	train-mlogloss:1.75557	validation-mlogloss:1.77503
[191]	train-mlogloss:1.75353	validation-mlogloss:1.77311
[192]	train-mlogloss:1.75159	validation-mlogloss:1.77117
[193]	train-mlogloss:1.74966	validation-mlogloss:1.76937
[194]	train-mlogloss:1.74749	validation-mlogloss:1.76736
[195]	train-mlogloss:1.74541	validation-mlogloss:1.76536
[196]	train-mlogloss:1.74341	validation-mlogloss:1.76353
[197]	train-mlogloss:1.74136	validation-mlogloss:1.76145
[198]	train-mlogloss:1.73944	validation-mlogloss:1.75972
[199]	train-mlogloss:1.73751	validation-mlogloss:1.75793
[200]	train-mlogloss:1.73571	validation-mlogloss:1.75634
[201]	train-mlogloss:1.73375	validation-mlogloss:1.75470
[202]	train-mlogloss:1.73189	validation-mlogloss:1.75297
[203]	train-mlogloss:1.73003	validation-mlogloss:1.75125
[204]	train-mlogloss:1.72812	validation-mlogloss:1.74949
[205]	train-mlogloss:1.72629	validation-mlogloss:1.74771
[206]	train-mlogloss:1.72450	validation-mlogloss:1.74594
[207]	train-mlogloss:1.72272	validation-mlogloss:1.74430
[208]	train-mlogloss:1.72095	validation-mlogloss:1.74255
[209]	train-mlogloss:1.71915	validation-mlogloss:1.74094
[210]	train-mlogloss:1.71736	validation-mlogloss:1.73930
[211]	train-mlogloss:1.71554	validation-mlogloss:1.73776
[212]	train-mlogloss:1.71375	validation-mlogloss:1.73610
[213]	train-mlogloss:1.71183	validation-mlogloss:1.73430
[214]	train-mlogloss:1.71002	validation-mlogloss:1.73251
[215]	train-mlogloss:1.70832	validation-mlogloss:1.73098
[216]	train-mlogloss:1.70648	validation-mlogloss:1.72921
[217]	train-mlogloss:1.70469	validation-mlogloss:1.72771
[218]	train-mlogloss:1.70294	validation-mlogloss:1.72609
[219]	train-mlogloss:1.70120	validation-mlogloss:1.72462
[220]	train-mlogloss:1.69944	validation-mlogloss:1.72297
[221]	train-mlogloss:1.69770	validation-mlogloss:1.72127
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[524]	train-mlogloss:1.44944	validation-mlogloss:1.51109
[525]	train-mlogloss:1.44908	validation-mlogloss:1.51089
[526]	train-mlogloss:1.44870	validation-mlogloss:1.51056
[527]	train-mlogloss:1.44824	validation-mlogloss:1.51036
[528]	train-mlogloss:1.44791	validation-mlogloss:1.51012
[529]	train-mlogloss:1.44747	validation-mlogloss:1.50989
[530]	train-mlogloss:1.44703	validation-mlogloss:1.50959
[531]	train-mlogloss:1.44664	validation-mlogloss:1.50928
[532]	train-mlogloss:1.44621	validation-mlogloss:1.50901
[533]	train-mlogloss:1.44581	validation-mlogloss:1.50874
[534]	train-mlogloss:1.44541	validation-mlogloss:1.50855
[535]	train-mlogloss:1.44495	validation-mlogloss:1.50831
[536]	train-mlogloss:1.44451	validation-mlogloss:1.50800
[537]	train-mlogloss:1.44408	validation-mlogloss:1.50770
[538]	train-mlogloss:1.44369	validation-mlogloss:1.50742
[539]	train-mlogloss:1.44327	validation-mlogloss:1.50721
[540]	train-mlogloss:1.44281	validation-mlogloss:1.50692
[541]	train-mlogloss:1.44234	validation-mlogloss:1.50658
[542]	train-mlogloss:1.44190	validation-mlogloss:1.50631
[543]	train-mlogloss:1.44153	validation-mlogloss:1.50601
[544]	train-mlogloss:1.44112	validation-mlogloss:1.50582
[545]	train-mlogloss:1.44075	validation-mlogloss:1.50556
[546]	train-mlogloss:1.44036	validation-mlogloss:1.50529
[547]	train-mlogloss:1.43995	validation-mlogloss:1.50515
[548]	train-mlogloss:1.43956	validation-mlogloss:1.50479
[549]	train-mlogloss:1.43917	validation-mlogloss:1.50461
[550]	train-mlogloss:1.43875	validation-mlogloss:1.50437
[551]	train-mlogloss:1.43834	validation-mlogloss:1.50421
[552]	train-mlogloss:1.43795	validation-mlogloss:1.50391
[553]	train-mlogloss:1.43758	validation-mlogloss:1.50362
[554]	train-mlogloss:1.43721	validation-mlogloss:1.50335
[555]	train-mlogloss:1.43689	validation-mlogloss:1.50320
[556]	train-mlogloss:1.43645	validation-mlogloss:1.50283
[557]	train-mlogloss:1.43607	validation-mlogloss:1.50259
[558]	train-mlogloss:1.43567	validation-mlogloss:1.50233
[559]	train-mlogloss:1.43530	validation-mlogloss:1.50200
[560]	train-mlogloss:1.43494	validation-mlogloss:1.50183
[561]	train-mlogloss:1.43452	validation-mlogloss:1.50152
[562]	train-mlogloss:1.43411	validation-mlogloss:1.50127
[563]	train-mlogloss:1.43376	validation-mlogloss:1.50105
[564]	train-mlogloss:1.43334	validation-mlogloss:1.50077
[565]	train-mlogloss:1.43295	validation-mlogloss:1.50061
[566]	train-mlogloss:1.43262	validation-mlogloss:1.50037
[567]	train-mlogloss:1.43227	validation-mlogloss:1.50004
[568]	train-mlogloss:1.43189	validation-mlogloss:1.49979
[569]	train-mlogloss:1.43155	validation-mlogloss:1.49955
[570]	train-mlogloss:1.43120	validation-mlogloss:1.49932
[571]	train-mlogloss:1.43088	validation-mlogloss:1.49915
[572]	train-mlogloss:1.43051	validation-mlogloss:1.49889
[573]	train-mlogloss:1.43012	validation-mlogloss:1.49862
[574]	train-mlogloss:1.42981	validation-mlogloss:1.49845
[575]	train-mlogloss:1.42949	validation-mlogloss:1.49823
[576]	train-mlogloss:1.42911	validation-mlogloss:1.49803
[577]	train-mlogloss:1.42876	validation-mlogloss:1.49768
[578]	train-mlogloss:1.42837	validation-mlogloss:1.49746
[579]	train-mlogloss:1.42800	validation-mlogloss:1.49735
[580]	train-mlogloss:1.42766	validation-mlogloss:1.49722
[581]	train-mlogloss:1.42733	validation-mlogloss:1.49696
[582]	train-mlogloss:1.42696	validation-mlogloss:1.49683
[583]	train-mlogloss:1.42653	validation-mlogloss:1.49662
[584]	train-mlogloss:1.42615	validation-mlogloss:1.49643
[585]	train-mlogloss:1.42578	validation-mlogloss:1.49610
[586]	train-mlogloss:1.42537	validation-mlogloss:1.49577
[587]	train-mlogloss:1.42503	validation-mlogloss:1.49550
[588]	train-mlogloss:1.42471	validation-mlogloss:1.49539
[589]	train-mlogloss:1.42444	validation-mlogloss:1.49519
[590]	train-mlogloss:1.42405	validation-mlogloss:1.49501
[591]	train-mlogloss:1.42365	validation-mlogloss:1.49476
[592]	train-mlogloss:1.42328	validation-mlogloss:1.49459
[593]	train-mlogloss:1.42299	validation-mlogloss:1.49435
[594]	train-mlogloss:1.42268	validation-mlogloss:1.49415
[595]	train-mlogloss:1.42238	validation-mlogloss:1.49398
[596]	train-mlogloss:1.42207	validation-mlogloss:1.49379
[597]	train-mlogloss:1.42167	validation-mlogloss:1.49361
[598]	train-mlogloss:1.42134	validation-mlogloss:1.49342
[599]	train-mlogloss:1.42102	validation-mlogloss:1.49315
[600]	train-mlogloss:1.42071	validation-mlogloss:1.49297
[601]	train-mlogloss:1.42036	validation-mlogloss:1.49285
[602]	train-mlogloss:1.42001	validation-mlogloss:1.49270
[603]	train-mlogloss:1.41968	validation-mlogloss:1.49237
[604]	train-mlogloss:1.41932	validation-mlogloss:1.49223
[605]	train-mlogloss:1.41898	validation-mlogloss:1.49204
[606]	train-mlogloss:1.41869	validation-mlogloss:1.49199
[607]	train-mlogloss:1.41838	validation-mlogloss:1.49188
[608]	train-mlogloss:1.41804	validation-mlogloss:1.49175
[609]	train-mlogloss:1.41772	validation-mlogloss:1.49155
[610]	train-mlogloss:1.41739	validation-mlogloss:1.49145
[611]	train-mlogloss:1.41705	validation-mlogloss:1.49123
[612]	train-mlogloss:1.41663	validation-mlogloss:1.49112
[613]	train-mlogloss:1.41634	validation-mlogloss:1.49084
[614]	train-mlogloss:1.41606	validation-mlogloss:1.49068
[615]	train-mlogloss:1.41571	validation-mlogloss:1.49046
[616]	train-mlogloss:1.41536	validation-mlogloss:1.49022
[617]	train-mlogloss:1.41502	validation-mlogloss:1.49004
[618]	train-mlogloss:1.41475	validation-mlogloss:1.48993
[619]	train-mlogloss:1.41440	validation-mlogloss:1.48985
[620]	train-mlogloss:1.41408	validation-mlogloss:1.48972
[621]	train-mlogloss:1.41376	validation-mlogloss:1.48956
[622]	train-mlogloss:1.41351	validation-mlogloss:1.48939
[623]	train-mlogloss:1.41328	validation-mlogloss:1.48927
[624]	train-mlogloss:1.41295	validation-mlogloss:1.48905
[625]	train-mlogloss:1.41268	validation-mlogloss:1.48892
[626]	train-mlogloss:1.41240	validation-mlogloss:1.48876
[627]	train-mlogloss:1.41210	validation-mlogloss:1.48858
[628]	train-mlogloss:1.41181	validation-mlogloss:1.48845
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.05 [%]
Global accuracy score (test) = 43.84 [%]
Global F1 score (train) = 55.9 [%]
Global F1 score (test) = 42.78 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.21      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.32      0.41      0.36       184
       CAMINAR USUAL SPEED       0.24      0.12      0.16       184
            CAMINAR ZIGZAG       0.28      0.28      0.28       184
          DE PIE BARRIENDO       0.48      0.49      0.48       184
   DE PIE DOBLANDO TOALLAS       0.43      0.42      0.43       184
    DE PIE MOVIENDO LIBROS       0.40      0.40      0.40       184
          DE PIE USANDO PC       0.54      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.29      0.24      0.26       184
         SENTADO USANDO PC       0.13      0.05      0.07       184
      SENTADO VIENDO LA TV       0.39      0.37      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 16: 43.84 [%]
F1-score capturado en la ejecución 16: 42.78 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.93 [%]
Global accuracy score (test) = 43.44 [%]
Global F1 score (train) = 55.74 [%]
Global F1 score (test) = 42.46 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.21      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.41      0.35       184
       CAMINAR USUAL SPEED       0.25      0.13      0.17       184
            CAMINAR ZIGZAG       0.28      0.28      0.28       184
          DE PIE BARRIENDO       0.45      0.48      0.47       184
   DE PIE DOBLANDO TOALLAS       0.44      0.41      0.43       184
    DE PIE MOVIENDO LIBROS       0.37      0.38      0.37       184
          DE PIE USANDO PC       0.54      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.30      0.27      0.28       184
         SENTADO USANDO PC       0.18      0.07      0.10       184
      SENTADO VIENDO LA TV       0.37      0.31      0.34       184
   SUBIR Y BAJAR ESCALERAS       0.55      0.73      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.43      0.42      2737


Accuracy capturado en la ejecución 17: 43.44 [%]
F1-score capturado en la ejecución 17: 42.46 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.05 [%]
Global accuracy score (test) = 43.62 [%]
Global F1 score (train) = 55.88 [%]
Global F1 score (test) = 42.62 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.21      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.39      0.34       184
       CAMINAR USUAL SPEED       0.26      0.12      0.17       184
            CAMINAR ZIGZAG       0.31      0.33      0.32       184
          DE PIE BARRIENDO       0.47      0.48      0.48       184
   DE PIE DOBLANDO TOALLAS       0.42      0.41      0.42       184
    DE PIE MOVIENDO LIBROS       0.39      0.38      0.38       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.41      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.26      0.22      0.24       184
         SENTADO USANDO PC       0.16      0.06      0.09       184
      SENTADO VIENDO LA TV       0.40      0.37      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.57      0.73      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 18: 43.62 [%]
F1-score capturado en la ejecución 18: 42.62 [%]

=== EJECUCIÓN 19 ===

--- TRAIN (ejecución 19) ---

--- TEST (ejecución 19) ---
2025-11-07 10:27:17.725184: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:27:17.736432: 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 00:00:1762507637.749381 2386932 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507637.753445 2386932 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507637.763494 2386932 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507637.763514 2386932 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507637.763517 2386932 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507637.763519 2386932 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:27:17.766642: 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:2.69750	validation-mlogloss:2.69738
[1]	train-mlogloss:2.68705	validation-mlogloss:2.68715
[2]	train-mlogloss:2.67667	validation-mlogloss:2.67676
[3]	train-mlogloss:2.66606	validation-mlogloss:2.66615
[4]	train-mlogloss:2.65590	validation-mlogloss:2.65600
[5]	train-mlogloss:2.64558	validation-mlogloss:2.64547
[6]	train-mlogloss:2.63543	validation-mlogloss:2.63525
[7]	train-mlogloss:2.62560	validation-mlogloss:2.62515
[8]	train-mlogloss:2.61596	validation-mlogloss:2.61532
[9]	train-mlogloss:2.60613	validation-mlogloss:2.60539
[10]	train-mlogloss:2.59645	validation-mlogloss:2.59572
[11]	train-mlogloss:2.58680	validation-mlogloss:2.58607
[12]	train-mlogloss:2.57689	validation-mlogloss:2.57615
[13]	train-mlogloss:2.56716	validation-mlogloss:2.56637
[14]	train-mlogloss:2.55806	validation-mlogloss:2.55715
[15]	train-mlogloss:2.54830	validation-mlogloss:2.54741
[16]	train-mlogloss:2.53918	validation-mlogloss:2.53837
[17]	train-mlogloss:2.53031	validation-mlogloss:2.52938
[18]	train-mlogloss:2.52123	validation-mlogloss:2.52020
[19]	train-mlogloss:2.51229	validation-mlogloss:2.51130
[20]	train-mlogloss:2.50367	validation-mlogloss:2.50256
[21]	train-mlogloss:2.49537	validation-mlogloss:2.49423
[22]	train-mlogloss:2.48649	validation-mlogloss:2.48550
[23]	train-mlogloss:2.47780	validation-mlogloss:2.47667
[24]	train-mlogloss:2.46917	validation-mlogloss:2.46822
[25]	train-mlogloss:2.46057	validation-mlogloss:2.45965
[26]	train-mlogloss:2.45187	validation-mlogloss:2.45060
[27]	train-mlogloss:2.44371	validation-mlogloss:2.44222
[28]	train-mlogloss:2.43546	validation-mlogloss:2.43386
[29]	train-mlogloss:2.42732	validation-mlogloss:2.42573
[30]	train-mlogloss:2.41926	validation-mlogloss:2.41771
[31]	train-mlogloss:2.41097	validation-mlogloss:2.40931
[32]	train-mlogloss:2.40302	validation-mlogloss:2.40149
[33]	train-mlogloss:2.39548	validation-mlogloss:2.39390
[34]	train-mlogloss:2.38770	validation-mlogloss:2.38590
[35]	train-mlogloss:2.38022	validation-mlogloss:2.37834
[36]	train-mlogloss:2.37256	validation-mlogloss:2.37083
[37]	train-mlogloss:2.36496	validation-mlogloss:2.36330
[38]	train-mlogloss:2.35765	validation-mlogloss:2.35581
[39]	train-mlogloss:2.35019	validation-mlogloss:2.34832
[40]	train-mlogloss:2.34264	validation-mlogloss:2.34082
[41]	train-mlogloss:2.33556	validation-mlogloss:2.33377
[42]	train-mlogloss:2.32860	validation-mlogloss:2.32667
[43]	train-mlogloss:2.32130	validation-mlogloss:2.31940
[44]	train-mlogloss:2.31441	validation-mlogloss:2.31256
[45]	train-mlogloss:2.30766	validation-mlogloss:2.30568
[46]	train-mlogloss:2.30069	validation-mlogloss:2.29884
[47]	train-mlogloss:2.29380	validation-mlogloss:2.29209
[48]	train-mlogloss:2.28714	validation-mlogloss:2.28532
[49]	train-mlogloss:2.28044	validation-mlogloss:2.27856
[50]	train-mlogloss:2.27396	validation-mlogloss:2.27201
[51]	train-mlogloss:2.26766	validation-mlogloss:2.26569
[52]	train-mlogloss:2.26136	validation-mlogloss:2.25953
[53]	train-mlogloss:2.25504	validation-mlogloss:2.25316
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[613]	train-mlogloss:1.41746	validation-mlogloss:1.49062
[614]	train-mlogloss:1.41713	validation-mlogloss:1.49036
[615]	train-mlogloss:1.41679	validation-mlogloss:1.49025
[616]	train-mlogloss:1.41652	validation-mlogloss:1.49006
[617]	train-mlogloss:1.41619	validation-mlogloss:1.48986
[618]	train-mlogloss:1.41588	validation-mlogloss:1.48968
[619]	train-mlogloss:1.41557	validation-mlogloss:1.48948
[620]	train-mlogloss:1.41530	validation-mlogloss:1.48927
[621]	train-mlogloss:1.41500	validation-mlogloss:1.48909
[622]	train-mlogloss:1.41464	validation-mlogloss:1.48901
[623]	train-mlogloss:1.41433	validation-mlogloss:1.48878
[624]	train-mlogloss:1.41402	validation-mlogloss:1.48861
[625]	train-mlogloss:1.41373	validation-mlogloss:1.48842
[626]	train-mlogloss:1.41338	validation-mlogloss:1.48817
[627]	train-mlogloss:1.41311	validation-mlogloss:1.48783
[628]	train-mlogloss:1.41280	validation-mlogloss:1.48769
Saved model to disk.
2025-11-07 10:27:43.617237: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:27:43.628746: 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 00:00:1762507663.642092 2387352 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507663.646249 2387352 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507663.656765 2387352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507663.656787 2387352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507663.656790 2387352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507663.656791 2387352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:27:43.659939: 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:2.69738	validation-mlogloss:2.69739
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[602]	train-mlogloss:1.42013	validation-mlogloss:1.49391
[603]	train-mlogloss:1.41979	validation-mlogloss:1.49374
[604]	train-mlogloss:1.41949	validation-mlogloss:1.49359
[605]	train-mlogloss:1.41919	validation-mlogloss:1.49332
[606]	train-mlogloss:1.41889	validation-mlogloss:1.49307
[607]	train-mlogloss:1.41852	validation-mlogloss:1.49285
[608]	train-mlogloss:1.41822	validation-mlogloss:1.49264
[609]	train-mlogloss:1.41789	validation-mlogloss:1.49252
[610]	train-mlogloss:1.41759	validation-mlogloss:1.49240
[611]	train-mlogloss:1.41727	validation-mlogloss:1.49222
[612]	train-mlogloss:1.41698	validation-mlogloss:1.49206
[613]	train-mlogloss:1.41667	validation-mlogloss:1.49180
[614]	train-mlogloss:1.41633	validation-mlogloss:1.49153
[615]	train-mlogloss:1.41597	validation-mlogloss:1.49136
[616]	train-mlogloss:1.41569	validation-mlogloss:1.49122
[617]	train-mlogloss:1.41535	validation-mlogloss:1.49093
[618]	train-mlogloss:1.41503	validation-mlogloss:1.49077
[619]	train-mlogloss:1.41471	validation-mlogloss:1.49058
[620]	train-mlogloss:1.41432	validation-mlogloss:1.49038
[621]	train-mlogloss:1.41396	validation-mlogloss:1.49019
[622]	train-mlogloss:1.41369	validation-mlogloss:1.49001
[623]	train-mlogloss:1.41336	validation-mlogloss:1.48980
[624]	train-mlogloss:1.41306	validation-mlogloss:1.48960
[625]	train-mlogloss:1.41275	validation-mlogloss:1.48941
[626]	train-mlogloss:1.41246	validation-mlogloss:1.48931
[627]	train-mlogloss:1.41213	validation-mlogloss:1.48909
[628]	train-mlogloss:1.41182	validation-mlogloss:1.48893
Saved model to disk.
2025-11-07 10:28:09.180886: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:28:09.192037: 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 00:00:1762507689.204997 2387763 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507689.209167 2387763 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507689.219114 2387763 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507689.219134 2387763 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507689.219136 2387763 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507689.219138 2387763 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:28:09.222297: 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:2.69749	validation-mlogloss:2.69739
[1]	train-mlogloss:2.68659	validation-mlogloss:2.68618
[2]	train-mlogloss:2.67627	validation-mlogloss:2.67586
[3]	train-mlogloss:2.66532	validation-mlogloss:2.66482
[4]	train-mlogloss:2.65489	validation-mlogloss:2.65417
[5]	train-mlogloss:2.64456	validation-mlogloss:2.64403
[6]	train-mlogloss:2.63456	validation-mlogloss:2.63378
[7]	train-mlogloss:2.62501	validation-mlogloss:2.62424
[8]	train-mlogloss:2.61512	validation-mlogloss:2.61440
[9]	train-mlogloss:2.60472	validation-mlogloss:2.60403
[10]	train-mlogloss:2.59506	validation-mlogloss:2.59415
[11]	train-mlogloss:2.58562	validation-mlogloss:2.58465
[12]	train-mlogloss:2.57551	validation-mlogloss:2.57448
[13]	train-mlogloss:2.56590	validation-mlogloss:2.56490
[14]	train-mlogloss:2.55660	validation-mlogloss:2.55554
[15]	train-mlogloss:2.54715	validation-mlogloss:2.54597
[16]	train-mlogloss:2.53757	validation-mlogloss:2.53629
[17]	train-mlogloss:2.52860	validation-mlogloss:2.52715
[18]	train-mlogloss:2.51983	validation-mlogloss:2.51824
[19]	train-mlogloss:2.51128	validation-mlogloss:2.50947
[20]	train-mlogloss:2.50257	validation-mlogloss:2.50059
[21]	train-mlogloss:2.49400	validation-mlogloss:2.49179
[22]	train-mlogloss:2.48493	validation-mlogloss:2.48255
[23]	train-mlogloss:2.47658	validation-mlogloss:2.47417
[24]	train-mlogloss:2.46773	validation-mlogloss:2.46534
[25]	train-mlogloss:2.45899	validation-mlogloss:2.45640
[26]	train-mlogloss:2.45102	validation-mlogloss:2.44859
[27]	train-mlogloss:2.44239	validation-mlogloss:2.44002
[28]	train-mlogloss:2.43398	validation-mlogloss:2.43146
[29]	train-mlogloss:2.42560	validation-mlogloss:2.42316
[30]	train-mlogloss:2.41755	validation-mlogloss:2.41517
[31]	train-mlogloss:2.40968	validation-mlogloss:2.40721
[32]	train-mlogloss:2.40159	validation-mlogloss:2.39929
[33]	train-mlogloss:2.39414	validation-mlogloss:2.39184
[34]	train-mlogloss:2.38634	validation-mlogloss:2.38412
[35]	train-mlogloss:2.37844	validation-mlogloss:2.37636
[36]	train-mlogloss:2.37124	validation-mlogloss:2.36917
[37]	train-mlogloss:2.36347	validation-mlogloss:2.36141
[38]	train-mlogloss:2.35625	validation-mlogloss:2.35431
[39]	train-mlogloss:2.34887	validation-mlogloss:2.34706
[40]	train-mlogloss:2.34165	validation-mlogloss:2.34014
[41]	train-mlogloss:2.33459	validation-mlogloss:2.33307
[42]	train-mlogloss:2.32755	validation-mlogloss:2.32601
[43]	train-mlogloss:2.32050	validation-mlogloss:2.31887
[44]	train-mlogloss:2.31364	validation-mlogloss:2.31228
[45]	train-mlogloss:2.30652	validation-mlogloss:2.30530
[46]	train-mlogloss:2.29972	validation-mlogloss:2.29840
[47]	train-mlogloss:2.29321	validation-mlogloss:2.29186
[48]	train-mlogloss:2.28654	validation-mlogloss:2.28517
[49]	train-mlogloss:2.28004	validation-mlogloss:2.27866
[50]	train-mlogloss:2.27350	validation-mlogloss:2.27197
[51]	train-mlogloss:2.26706	validation-mlogloss:2.26540
[52]	train-mlogloss:2.26068	validation-mlogloss:2.25903
[53]	train-mlogloss:2.25433	validation-mlogloss:2.25276
[54]	train-mlogloss:2.24792	validation-mlogloss:2.24652
[55]	train-mlogloss:2.24190	validation-mlogloss:2.24054
[56]	train-mlogloss:2.23569	validation-mlogloss:2.23425
[57]	train-mlogloss:2.22963	validation-mlogloss:2.22824
[58]	train-mlogloss:2.22357	validation-mlogloss:2.22224
[59]	train-mlogloss:2.21747	validation-mlogloss:2.21628
[60]	train-mlogloss:2.21137	validation-mlogloss:2.21046
[61]	train-mlogloss:2.20549	validation-mlogloss:2.20475
[62]	train-mlogloss:2.19977	validation-mlogloss:2.19903
[63]	train-mlogloss:2.19391	validation-mlogloss:2.19346
[64]	train-mlogloss:2.18810	validation-mlogloss:2.18795
[65]	train-mlogloss:2.18278	validation-mlogloss:2.18261
[66]	train-mlogloss:2.17707	validation-mlogloss:2.17702
[67]	train-mlogloss:2.17146	validation-mlogloss:2.17164
[68]	train-mlogloss:2.16601	validation-mlogloss:2.16627
[69]	train-mlogloss:2.16040	validation-mlogloss:2.16095
[70]	train-mlogloss:2.15513	validation-mlogloss:2.15588
[71]	train-mlogloss:2.14968	validation-mlogloss:2.15076
[72]	train-mlogloss:2.14437	validation-mlogloss:2.14569
[73]	train-mlogloss:2.13914	validation-mlogloss:2.14043
[74]	train-mlogloss:2.13413	validation-mlogloss:2.13545
[75]	train-mlogloss:2.12888	validation-mlogloss:2.13022
[76]	train-mlogloss:2.12400	validation-mlogloss:2.12541
[77]	train-mlogloss:2.11887	validation-mlogloss:2.12034
[78]	train-mlogloss:2.11386	validation-mlogloss:2.11567
[79]	train-mlogloss:2.10897	validation-mlogloss:2.11088
[80]	train-mlogloss:2.10385	validation-mlogloss:2.10602
[81]	train-mlogloss:2.09926	validation-mlogloss:2.10137
[82]	train-mlogloss:2.09427	validation-mlogloss:2.09655
[83]	train-mlogloss:2.08953	validation-mlogloss:2.09192
[84]	train-mlogloss:2.08477	validation-mlogloss:2.08748
[85]	train-mlogloss:2.08035	validation-mlogloss:2.08316
[86]	train-mlogloss:2.07589	validation-mlogloss:2.07895
[87]	train-mlogloss:2.07128	validation-mlogloss:2.07468
[88]	train-mlogloss:2.06676	validation-mlogloss:2.07027
[89]	train-mlogloss:2.06233	validation-mlogloss:2.06612
[90]	train-mlogloss:2.05801	validation-mlogloss:2.06189
[91]	train-mlogloss:2.05358	validation-mlogloss:2.05783
[92]	train-mlogloss:2.04935	validation-mlogloss:2.05365
[93]	train-mlogloss:2.04520	validation-mlogloss:2.04986
[94]	train-mlogloss:2.04111	validation-mlogloss:2.04593
[95]	train-mlogloss:2.03686	validation-mlogloss:2.04205
[96]	train-mlogloss:2.03265	validation-mlogloss:2.03820
[97]	train-mlogloss:2.02870	validation-mlogloss:2.03437
[98]	train-mlogloss:2.02453	validation-mlogloss:2.03039
[99]	train-mlogloss:2.02050	validation-mlogloss:2.02656
[100]	train-mlogloss:2.01660	validation-mlogloss:2.02252
[101]	train-mlogloss:2.01261	validation-mlogloss:2.01875
[102]	train-mlogloss:2.00871	validation-mlogloss:2.01487
[103]	train-mlogloss:2.00474	validation-mlogloss:2.01099
[104]	train-mlogloss:2.00087	validation-mlogloss:2.00716
[105]	train-mlogloss:1.99717	validation-mlogloss:2.00348
[106]	train-mlogloss:1.99337	validation-mlogloss:1.99987
[107]	train-mlogloss:1.98939	validation-mlogloss:1.99608
[108]	train-mlogloss:1.98573	validation-mlogloss:1.99264
[109]	train-mlogloss:1.98191	validation-mlogloss:1.98882
[110]	train-mlogloss:1.97825	validation-mlogloss:1.98519
[111]	train-mlogloss:1.97454	validation-mlogloss:1.98156
[112]	train-mlogloss:1.97065	validation-mlogloss:1.97797
[113]	train-mlogloss:1.96673	validation-mlogloss:1.97428
[114]	train-mlogloss:1.96310	validation-mlogloss:1.97080
[115]	train-mlogloss:1.95949	validation-mlogloss:1.96747
[116]	train-mlogloss:1.95588	validation-mlogloss:1.96410
[117]	train-mlogloss:1.95235	validation-mlogloss:1.96072
[118]	train-mlogloss:1.94877	validation-mlogloss:1.95739
[119]	train-mlogloss:1.94552	validation-mlogloss:1.95413
[120]	train-mlogloss:1.94214	validation-mlogloss:1.95082
[121]	train-mlogloss:1.93873	validation-mlogloss:1.94758
[122]	train-mlogloss:1.93542	validation-mlogloss:1.94439
[123]	train-mlogloss:1.93207	validation-mlogloss:1.94133
[124]	train-mlogloss:1.92878	validation-mlogloss:1.93819
[125]	train-mlogloss:1.92537	validation-mlogloss:1.93487
[126]	train-mlogloss:1.92211	validation-mlogloss:1.93169
[127]	train-mlogloss:1.91895	validation-mlogloss:1.92885
[128]	train-mlogloss:1.91562	validation-mlogloss:1.92565
[129]	train-mlogloss:1.91228	validation-mlogloss:1.92248
[130]	train-mlogloss:1.90918	validation-mlogloss:1.91964
[131]	train-mlogloss:1.90600	validation-mlogloss:1.91664
[132]	train-mlogloss:1.90289	validation-mlogloss:1.91373
[133]	train-mlogloss:1.89974	validation-mlogloss:1.91077
[134]	train-mlogloss:1.89644	validation-mlogloss:1.90759
[135]	train-mlogloss:1.89349	validation-mlogloss:1.90489
[136]	train-mlogloss:1.89046	validation-mlogloss:1.90236
[137]	train-mlogloss:1.88732	validation-mlogloss:1.89929
[138]	train-mlogloss:1.88410	validation-mlogloss:1.89637
[139]	train-mlogloss:1.88095	validation-mlogloss:1.89350
[140]	train-mlogloss:1.87791	validation-mlogloss:1.89049
[141]	train-mlogloss:1.87509	validation-mlogloss:1.88810
[142]	train-mlogloss:1.87203	validation-mlogloss:1.88517
[143]	train-mlogloss:1.86913	validation-mlogloss:1.88241
[144]	train-mlogloss:1.86628	validation-mlogloss:1.87981
[145]	train-mlogloss:1.86345	validation-mlogloss:1.87701
[146]	train-mlogloss:1.86070	validation-mlogloss:1.87437
[147]	train-mlogloss:1.85788	validation-mlogloss:1.87182
[148]	train-mlogloss:1.85507	validation-mlogloss:1.86910
[149]	train-mlogloss:1.85236	validation-mlogloss:1.86662
[150]	train-mlogloss:1.84971	validation-mlogloss:1.86402
[151]	train-mlogloss:1.84713	validation-mlogloss:1.86155
[152]	train-mlogloss:1.84454	validation-mlogloss:1.85913
[153]	train-mlogloss:1.84183	validation-mlogloss:1.85636
[154]	train-mlogloss:1.83917	validation-mlogloss:1.85375
[155]	train-mlogloss:1.83646	validation-mlogloss:1.85123
[156]	train-mlogloss:1.83397	validation-mlogloss:1.84885
[157]	train-mlogloss:1.83159	validation-mlogloss:1.84655
[158]	train-mlogloss:1.82893	validation-mlogloss:1.84397
[159]	train-mlogloss:1.82632	validation-mlogloss:1.84141
[160]	train-mlogloss:1.82394	validation-mlogloss:1.83931
[161]	train-mlogloss:1.82126	validation-mlogloss:1.83680
[162]	train-mlogloss:1.81870	validation-mlogloss:1.83454
[163]	train-mlogloss:1.81615	validation-mlogloss:1.83207
[164]	train-mlogloss:1.81351	validation-mlogloss:1.82980
[165]	train-mlogloss:1.81104	validation-mlogloss:1.82733
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[168]	train-mlogloss:1.80368	validation-mlogloss:1.82063
[169]	train-mlogloss:1.80128	validation-mlogloss:1.81830
[170]	train-mlogloss:1.79897	validation-mlogloss:1.81626
[171]	train-mlogloss:1.79641	validation-mlogloss:1.81386
[172]	train-mlogloss:1.79414	validation-mlogloss:1.81158
[173]	train-mlogloss:1.79185	validation-mlogloss:1.80947
[174]	train-mlogloss:1.78945	validation-mlogloss:1.80717
[175]	train-mlogloss:1.78717	validation-mlogloss:1.80494
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[177]	train-mlogloss:1.78273	validation-mlogloss:1.80091
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[179]	train-mlogloss:1.77833	validation-mlogloss:1.79677
[180]	train-mlogloss:1.77612	validation-mlogloss:1.79466
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[185]	train-mlogloss:1.76544	validation-mlogloss:1.78462
[186]	train-mlogloss:1.76327	validation-mlogloss:1.78247
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[190]	train-mlogloss:1.75484	validation-mlogloss:1.77462
[191]	train-mlogloss:1.75273	validation-mlogloss:1.77261
[192]	train-mlogloss:1.75071	validation-mlogloss:1.77066
[193]	train-mlogloss:1.74861	validation-mlogloss:1.76883
[194]	train-mlogloss:1.74657	validation-mlogloss:1.76692
[195]	train-mlogloss:1.74460	validation-mlogloss:1.76518
[196]	train-mlogloss:1.74275	validation-mlogloss:1.76345
[197]	train-mlogloss:1.74084	validation-mlogloss:1.76182
[198]	train-mlogloss:1.73899	validation-mlogloss:1.76007
[199]	train-mlogloss:1.73702	validation-mlogloss:1.75818
[200]	train-mlogloss:1.73509	validation-mlogloss:1.75638
[201]	train-mlogloss:1.73316	validation-mlogloss:1.75450
[202]	train-mlogloss:1.73133	validation-mlogloss:1.75278
[203]	train-mlogloss:1.72933	validation-mlogloss:1.75090
[204]	train-mlogloss:1.72743	validation-mlogloss:1.74909
[205]	train-mlogloss:1.72571	validation-mlogloss:1.74758
[206]	train-mlogloss:1.72381	validation-mlogloss:1.74570
[207]	train-mlogloss:1.72195	validation-mlogloss:1.74390
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[209]	train-mlogloss:1.71830	validation-mlogloss:1.74056
[210]	train-mlogloss:1.71654	validation-mlogloss:1.73905
[211]	train-mlogloss:1.71476	validation-mlogloss:1.73738
[212]	train-mlogloss:1.71291	validation-mlogloss:1.73584
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[214]	train-mlogloss:1.70929	validation-mlogloss:1.73251
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[218]	train-mlogloss:1.70245	validation-mlogloss:1.72643
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[220]	train-mlogloss:1.69896	validation-mlogloss:1.72328
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[550]	train-mlogloss:1.43890	validation-mlogloss:1.50337
[551]	train-mlogloss:1.43850	validation-mlogloss:1.50313
[552]	train-mlogloss:1.43816	validation-mlogloss:1.50284
[553]	train-mlogloss:1.43769	validation-mlogloss:1.50248
[554]	train-mlogloss:1.43732	validation-mlogloss:1.50234
[555]	train-mlogloss:1.43694	validation-mlogloss:1.50210
[556]	train-mlogloss:1.43657	validation-mlogloss:1.50182
[557]	train-mlogloss:1.43624	validation-mlogloss:1.50152
[558]	train-mlogloss:1.43584	validation-mlogloss:1.50125
[559]	train-mlogloss:1.43545	validation-mlogloss:1.50085
[560]	train-mlogloss:1.43512	validation-mlogloss:1.50070
[561]	train-mlogloss:1.43480	validation-mlogloss:1.50059
[562]	train-mlogloss:1.43440	validation-mlogloss:1.50038
[563]	train-mlogloss:1.43403	validation-mlogloss:1.50023
[564]	train-mlogloss:1.43368	validation-mlogloss:1.49998
[565]	train-mlogloss:1.43328	validation-mlogloss:1.49972
[566]	train-mlogloss:1.43286	validation-mlogloss:1.49953
[567]	train-mlogloss:1.43247	validation-mlogloss:1.49935
[568]	train-mlogloss:1.43210	validation-mlogloss:1.49917
[569]	train-mlogloss:1.43179	validation-mlogloss:1.49901
[570]	train-mlogloss:1.43143	validation-mlogloss:1.49882
[571]	train-mlogloss:1.43108	validation-mlogloss:1.49859
[572]	train-mlogloss:1.43073	validation-mlogloss:1.49831
[573]	train-mlogloss:1.43040	validation-mlogloss:1.49808
[574]	train-mlogloss:1.43003	validation-mlogloss:1.49785
[575]	train-mlogloss:1.42962	validation-mlogloss:1.49764
[576]	train-mlogloss:1.42927	validation-mlogloss:1.49744
[577]	train-mlogloss:1.42894	validation-mlogloss:1.49719
[578]	train-mlogloss:1.42852	validation-mlogloss:1.49691
[579]	train-mlogloss:1.42824	validation-mlogloss:1.49669
[580]	train-mlogloss:1.42787	validation-mlogloss:1.49658
[581]	train-mlogloss:1.42751	validation-mlogloss:1.49646
[582]	train-mlogloss:1.42714	validation-mlogloss:1.49623
[583]	train-mlogloss:1.42679	validation-mlogloss:1.49598
[584]	train-mlogloss:1.42640	validation-mlogloss:1.49574
[585]	train-mlogloss:1.42603	validation-mlogloss:1.49542
[586]	train-mlogloss:1.42570	validation-mlogloss:1.49518
[587]	train-mlogloss:1.42534	validation-mlogloss:1.49486
[588]	train-mlogloss:1.42502	validation-mlogloss:1.49466
[589]	train-mlogloss:1.42467	validation-mlogloss:1.49452
[590]	train-mlogloss:1.42431	validation-mlogloss:1.49430
[591]	train-mlogloss:1.42396	validation-mlogloss:1.49412
[592]	train-mlogloss:1.42361	validation-mlogloss:1.49379
[593]	train-mlogloss:1.42328	validation-mlogloss:1.49354
[594]	train-mlogloss:1.42293	validation-mlogloss:1.49334
[595]	train-mlogloss:1.42262	validation-mlogloss:1.49311
[596]	train-mlogloss:1.42232	validation-mlogloss:1.49285
[597]	train-mlogloss:1.42196	validation-mlogloss:1.49269
[598]	train-mlogloss:1.42166	validation-mlogloss:1.49254
[599]	train-mlogloss:1.42137	validation-mlogloss:1.49231
[600]	train-mlogloss:1.42112	validation-mlogloss:1.49212
[601]	train-mlogloss:1.42082	validation-mlogloss:1.49184
[602]	train-mlogloss:1.42054	validation-mlogloss:1.49162
[603]	train-mlogloss:1.42025	validation-mlogloss:1.49141
[604]	train-mlogloss:1.41996	validation-mlogloss:1.49119
[605]	train-mlogloss:1.41961	validation-mlogloss:1.49104
[606]	train-mlogloss:1.41923	validation-mlogloss:1.49096
[607]	train-mlogloss:1.41885	validation-mlogloss:1.49087
[608]	train-mlogloss:1.41853	validation-mlogloss:1.49070
[609]	train-mlogloss:1.41828	validation-mlogloss:1.49051
[610]	train-mlogloss:1.41798	validation-mlogloss:1.49031
[611]	train-mlogloss:1.41768	validation-mlogloss:1.49017
[612]	train-mlogloss:1.41735	validation-mlogloss:1.48994
[613]	train-mlogloss:1.41703	validation-mlogloss:1.48981
[614]	train-mlogloss:1.41671	validation-mlogloss:1.48960
[615]	train-mlogloss:1.41641	validation-mlogloss:1.48943
[616]	train-mlogloss:1.41603	validation-mlogloss:1.48933
[617]	train-mlogloss:1.41572	validation-mlogloss:1.48921
[618]	train-mlogloss:1.41544	validation-mlogloss:1.48902
[619]	train-mlogloss:1.41512	validation-mlogloss:1.48892
[620]	train-mlogloss:1.41486	validation-mlogloss:1.48872
[621]	train-mlogloss:1.41462	validation-mlogloss:1.48855
[622]	train-mlogloss:1.41429	validation-mlogloss:1.48834
[623]	train-mlogloss:1.41397	validation-mlogloss:1.48808
[624]	train-mlogloss:1.41364	validation-mlogloss:1.48789
[625]	train-mlogloss:1.41329	validation-mlogloss:1.48764
[626]	train-mlogloss:1.41300	validation-mlogloss:1.48748
[627]	train-mlogloss:1.41275	validation-mlogloss:1.48733
[628]	train-mlogloss:1.41248	validation-mlogloss:1.48719
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.91 [%]
Global accuracy score (test) = 43.73 [%]
Global F1 score (train) = 55.73 [%]
Global F1 score (test) = 42.74 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.22      0.22       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.38      0.33       184
       CAMINAR USUAL SPEED       0.25      0.14      0.18       184
            CAMINAR ZIGZAG       0.29      0.27      0.28       184
          DE PIE BARRIENDO       0.49      0.52      0.51       184
   DE PIE DOBLANDO TOALLAS       0.43      0.41      0.42       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.56       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.28      0.23      0.25       184
         SENTADO USANDO PC       0.16      0.07      0.09       184
      SENTADO VIENDO LA TV       0.39      0.36      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.54      0.73      0.62       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 19: 43.73 [%]
F1-score capturado en la ejecución 19: 42.74 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.98 [%]
Global accuracy score (test) = 43.77 [%]
Global F1 score (train) = 55.81 [%]
Global F1 score (test) = 42.76 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.24      0.24      0.24       184
 CAMINAR CON MÓVIL O LIBRO       0.33      0.41      0.36       184
       CAMINAR USUAL SPEED       0.26      0.14      0.18       184
            CAMINAR ZIGZAG       0.30      0.31      0.30       184
          DE PIE BARRIENDO       0.45      0.48      0.47       184
   DE PIE DOBLANDO TOALLAS       0.42      0.41      0.42       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.81      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.59      0.71       184
           SENTADO LEYENDO       0.29      0.25      0.27       184
         SENTADO USANDO PC       0.11      0.04      0.06       184
      SENTADO VIENDO LA TV       0.39      0.35      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.57      0.73      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 20: 43.77 [%]
F1-score capturado en la ejecución 20: 42.76 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.91 [%]
Global accuracy score (test) = 43.55 [%]
Global F1 score (train) = 55.76 [%]
Global F1 score (test) = 42.58 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.21      0.20       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.39      0.34       184
       CAMINAR USUAL SPEED       0.26      0.14      0.18       184
            CAMINAR ZIGZAG       0.29      0.26      0.27       184
          DE PIE BARRIENDO       0.47      0.49      0.48       184
   DE PIE DOBLANDO TOALLAS       0.46      0.46      0.46       184
    DE PIE MOVIENDO LIBROS       0.40      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.27      0.22      0.24       184
         SENTADO USANDO PC       0.16      0.07      0.09       184
      SENTADO VIENDO LA TV       0.37      0.34      0.35       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 21: 43.55 [%]
F1-score capturado en la ejecución 21: 42.58 [%]

=== EJECUCIÓN 22 ===

--- TRAIN (ejecución 22) ---

--- TEST (ejecución 22) ---
2025-11-07 10:28:34.551969: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:28:34.563295: 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 00:00:1762507714.576403 2388176 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507714.580435 2388176 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507714.590386 2388176 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507714.590403 2388176 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507714.590414 2388176 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507714.590415 2388176 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:28:34.593551: 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:2.69745	validation-mlogloss:2.69741
[1]	train-mlogloss:2.68729	validation-mlogloss:2.68712
[2]	train-mlogloss:2.67695	validation-mlogloss:2.67652
[3]	train-mlogloss:2.66659	validation-mlogloss:2.66610
[4]	train-mlogloss:2.65619	validation-mlogloss:2.65575
[5]	train-mlogloss:2.64613	validation-mlogloss:2.64547
[6]	train-mlogloss:2.63570	validation-mlogloss:2.63497
[7]	train-mlogloss:2.62566	validation-mlogloss:2.62509
[8]	train-mlogloss:2.61538	validation-mlogloss:2.61476
[9]	train-mlogloss:2.60569	validation-mlogloss:2.60488
[10]	train-mlogloss:2.59551	validation-mlogloss:2.59462
[11]	train-mlogloss:2.58567	validation-mlogloss:2.58476
[12]	train-mlogloss:2.57620	validation-mlogloss:2.57522
[13]	train-mlogloss:2.56653	validation-mlogloss:2.56552
[14]	train-mlogloss:2.55724	validation-mlogloss:2.55588
[15]	train-mlogloss:2.54777	validation-mlogloss:2.54650
[16]	train-mlogloss:2.53821	validation-mlogloss:2.53706
[17]	train-mlogloss:2.52879	validation-mlogloss:2.52774
[18]	train-mlogloss:2.51944	validation-mlogloss:2.51849
[19]	train-mlogloss:2.51039	validation-mlogloss:2.50927
[20]	train-mlogloss:2.50149	validation-mlogloss:2.50039
[21]	train-mlogloss:2.49270	validation-mlogloss:2.49155
[22]	train-mlogloss:2.48355	validation-mlogloss:2.48230
[23]	train-mlogloss:2.47490	validation-mlogloss:2.47379
[24]	train-mlogloss:2.46591	validation-mlogloss:2.46486
[25]	train-mlogloss:2.45773	validation-mlogloss:2.45639
[26]	train-mlogloss:2.44987	validation-mlogloss:2.44832
[27]	train-mlogloss:2.44160	validation-mlogloss:2.44019
[28]	train-mlogloss:2.43359	validation-mlogloss:2.43200
[29]	train-mlogloss:2.42539	validation-mlogloss:2.42382
[30]	train-mlogloss:2.41730	validation-mlogloss:2.41567
[31]	train-mlogloss:2.40950	validation-mlogloss:2.40764
[32]	train-mlogloss:2.40134	validation-mlogloss:2.39965
[33]	train-mlogloss:2.39359	validation-mlogloss:2.39178
[34]	train-mlogloss:2.38591	validation-mlogloss:2.38393
[35]	train-mlogloss:2.37856	validation-mlogloss:2.37680
[36]	train-mlogloss:2.37070	validation-mlogloss:2.36885
[37]	train-mlogloss:2.36322	validation-mlogloss:2.36133
[38]	train-mlogloss:2.35604	validation-mlogloss:2.35423
[39]	train-mlogloss:2.34866	validation-mlogloss:2.34687
[40]	train-mlogloss:2.34147	validation-mlogloss:2.33988
[41]	train-mlogloss:2.33441	validation-mlogloss:2.33311
[42]	train-mlogloss:2.32719	validation-mlogloss:2.32613
[43]	train-mlogloss:2.32029	validation-mlogloss:2.31917
[44]	train-mlogloss:2.31323	validation-mlogloss:2.31214
[45]	train-mlogloss:2.30630	validation-mlogloss:2.30512
[46]	train-mlogloss:2.29940	validation-mlogloss:2.29827
[47]	train-mlogloss:2.29257	validation-mlogloss:2.29164
[48]	train-mlogloss:2.28606	validation-mlogloss:2.28502
[49]	train-mlogloss:2.27943	validation-mlogloss:2.27840
[50]	train-mlogloss:2.27276	validation-mlogloss:2.27193
[51]	train-mlogloss:2.26609	validation-mlogloss:2.26531
[52]	train-mlogloss:2.25991	validation-mlogloss:2.25913
[53]	train-mlogloss:2.25341	validation-mlogloss:2.25266
[54]	train-mlogloss:2.24718	validation-mlogloss:2.24644
[55]	train-mlogloss:2.24107	validation-mlogloss:2.24023
[56]	train-mlogloss:2.23490	validation-mlogloss:2.23418
[57]	train-mlogloss:2.22883	validation-mlogloss:2.22818
[58]	train-mlogloss:2.22281	validation-mlogloss:2.22230
[59]	train-mlogloss:2.21675	validation-mlogloss:2.21657
[60]	train-mlogloss:2.21068	validation-mlogloss:2.21079
[61]	train-mlogloss:2.20494	validation-mlogloss:2.20530
[62]	train-mlogloss:2.19902	validation-mlogloss:2.19948
[63]	train-mlogloss:2.19326	validation-mlogloss:2.19364
[64]	train-mlogloss:2.18748	validation-mlogloss:2.18785
[65]	train-mlogloss:2.18178	validation-mlogloss:2.18222
[66]	train-mlogloss:2.17633	validation-mlogloss:2.17680
[67]	train-mlogloss:2.17070	validation-mlogloss:2.17144
[68]	train-mlogloss:2.16508	validation-mlogloss:2.16602
[69]	train-mlogloss:2.15962	validation-mlogloss:2.16087
[70]	train-mlogloss:2.15423	validation-mlogloss:2.15568
[71]	train-mlogloss:2.14878	validation-mlogloss:2.15046
[72]	train-mlogloss:2.14366	validation-mlogloss:2.14520
[73]	train-mlogloss:2.13852	validation-mlogloss:2.14011
[74]	train-mlogloss:2.13327	validation-mlogloss:2.13497
[75]	train-mlogloss:2.12810	validation-mlogloss:2.13003
[76]	train-mlogloss:2.12322	validation-mlogloss:2.12555
[77]	train-mlogloss:2.11820	validation-mlogloss:2.12064
[78]	train-mlogloss:2.11326	validation-mlogloss:2.11581
[79]	train-mlogloss:2.10824	validation-mlogloss:2.11085
[80]	train-mlogloss:2.10334	validation-mlogloss:2.10594
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[474]	train-mlogloss:1.47181	validation-mlogloss:1.52640
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[476]	train-mlogloss:1.47082	validation-mlogloss:1.52556
[477]	train-mlogloss:1.47034	validation-mlogloss:1.52526
[478]	train-mlogloss:1.46983	validation-mlogloss:1.52486
[479]	train-mlogloss:1.46938	validation-mlogloss:1.52446
[480]	train-mlogloss:1.46884	validation-mlogloss:1.52422
[481]	train-mlogloss:1.46837	validation-mlogloss:1.52393
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[491]	train-mlogloss:1.46364	validation-mlogloss:1.52060
[492]	train-mlogloss:1.46312	validation-mlogloss:1.52019
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[494]	train-mlogloss:1.46224	validation-mlogloss:1.51946
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[496]	train-mlogloss:1.46129	validation-mlogloss:1.51869
[497]	train-mlogloss:1.46080	validation-mlogloss:1.51836
[498]	train-mlogloss:1.46037	validation-mlogloss:1.51804
[499]	train-mlogloss:1.45990	validation-mlogloss:1.51772
[500]	train-mlogloss:1.45951	validation-mlogloss:1.51743
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[557]	train-mlogloss:1.43632	validation-mlogloss:1.50182
[558]	train-mlogloss:1.43590	validation-mlogloss:1.50153
[559]	train-mlogloss:1.43552	validation-mlogloss:1.50127
[560]	train-mlogloss:1.43510	validation-mlogloss:1.50100
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[564]	train-mlogloss:1.43356	validation-mlogloss:1.49988
[565]	train-mlogloss:1.43318	validation-mlogloss:1.49966
[566]	train-mlogloss:1.43284	validation-mlogloss:1.49938
[567]	train-mlogloss:1.43251	validation-mlogloss:1.49913
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[576]	train-mlogloss:1.42911	validation-mlogloss:1.49719
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[579]	train-mlogloss:1.42800	validation-mlogloss:1.49661
[580]	train-mlogloss:1.42765	validation-mlogloss:1.49640
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[582]	train-mlogloss:1.42702	validation-mlogloss:1.49589
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[584]	train-mlogloss:1.42636	validation-mlogloss:1.49544
[585]	train-mlogloss:1.42598	validation-mlogloss:1.49512
[586]	train-mlogloss:1.42561	validation-mlogloss:1.49486
[587]	train-mlogloss:1.42524	validation-mlogloss:1.49452
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[592]	train-mlogloss:1.42360	validation-mlogloss:1.49342
[593]	train-mlogloss:1.42328	validation-mlogloss:1.49330
[594]	train-mlogloss:1.42291	validation-mlogloss:1.49313
[595]	train-mlogloss:1.42260	validation-mlogloss:1.49295
[596]	train-mlogloss:1.42226	validation-mlogloss:1.49266
[597]	train-mlogloss:1.42199	validation-mlogloss:1.49247
[598]	train-mlogloss:1.42169	validation-mlogloss:1.49223
[599]	train-mlogloss:1.42139	validation-mlogloss:1.49208
[600]	train-mlogloss:1.42107	validation-mlogloss:1.49189
[601]	train-mlogloss:1.42069	validation-mlogloss:1.49170
[602]	train-mlogloss:1.42035	validation-mlogloss:1.49156
[603]	train-mlogloss:1.42001	validation-mlogloss:1.49130
[604]	train-mlogloss:1.41963	validation-mlogloss:1.49099
[605]	train-mlogloss:1.41924	validation-mlogloss:1.49068
[606]	train-mlogloss:1.41889	validation-mlogloss:1.49046
[607]	train-mlogloss:1.41860	validation-mlogloss:1.49035
[608]	train-mlogloss:1.41830	validation-mlogloss:1.49011
[609]	train-mlogloss:1.41800	validation-mlogloss:1.48999
[610]	train-mlogloss:1.41767	validation-mlogloss:1.48987
[611]	train-mlogloss:1.41730	validation-mlogloss:1.48970
[612]	train-mlogloss:1.41700	validation-mlogloss:1.48942
[613]	train-mlogloss:1.41669	validation-mlogloss:1.48923
[614]	train-mlogloss:1.41640	validation-mlogloss:1.48910
[615]	train-mlogloss:1.41609	validation-mlogloss:1.48894
[616]	train-mlogloss:1.41573	validation-mlogloss:1.48875
[617]	train-mlogloss:1.41541	validation-mlogloss:1.48859
[618]	train-mlogloss:1.41513	validation-mlogloss:1.48841
[619]	train-mlogloss:1.41473	validation-mlogloss:1.48805
[620]	train-mlogloss:1.41449	validation-mlogloss:1.48793
[621]	train-mlogloss:1.41421	validation-mlogloss:1.48776
[622]	train-mlogloss:1.41389	validation-mlogloss:1.48761
[623]	train-mlogloss:1.41350	validation-mlogloss:1.48741
[624]	train-mlogloss:1.41319	validation-mlogloss:1.48722
[625]	train-mlogloss:1.41292	validation-mlogloss:1.48708
[626]	train-mlogloss:1.41258	validation-mlogloss:1.48683
[627]	train-mlogloss:1.41226	validation-mlogloss:1.48665
[628]	train-mlogloss:1.41193	validation-mlogloss:1.48647
Saved model to disk.
2025-11-07 10:29:00.009797: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:29:00.021266: 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 00:00:1762507740.034331 2388596 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507740.038395 2388596 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507740.048386 2388596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507740.048406 2388596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507740.048408 2388596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507740.048410 2388596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:29:00.051634: 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:2.69734	validation-mlogloss:2.69710
[1]	train-mlogloss:2.68680	validation-mlogloss:2.68669
[2]	train-mlogloss:2.67655	validation-mlogloss:2.67645
[3]	train-mlogloss:2.66610	validation-mlogloss:2.66578
[4]	train-mlogloss:2.65605	validation-mlogloss:2.65551
[5]	train-mlogloss:2.64585	validation-mlogloss:2.64559
[6]	train-mlogloss:2.63600	validation-mlogloss:2.63566
[7]	train-mlogloss:2.62575	validation-mlogloss:2.62545
[8]	train-mlogloss:2.61620	validation-mlogloss:2.61600
[9]	train-mlogloss:2.60656	validation-mlogloss:2.60620
[10]	train-mlogloss:2.59649	validation-mlogloss:2.59615
[11]	train-mlogloss:2.58677	validation-mlogloss:2.58670
[12]	train-mlogloss:2.57682	validation-mlogloss:2.57659
[13]	train-mlogloss:2.56733	validation-mlogloss:2.56714
[14]	train-mlogloss:2.55813	validation-mlogloss:2.55800
[15]	train-mlogloss:2.54852	validation-mlogloss:2.54823
[16]	train-mlogloss:2.53911	validation-mlogloss:2.53893
[17]	train-mlogloss:2.52980	validation-mlogloss:2.52982
[18]	train-mlogloss:2.52068	validation-mlogloss:2.52050
[19]	train-mlogloss:2.51192	validation-mlogloss:2.51162
[20]	train-mlogloss:2.50321	validation-mlogloss:2.50270
[21]	train-mlogloss:2.49426	validation-mlogloss:2.49359
[22]	train-mlogloss:2.48531	validation-mlogloss:2.48459
[23]	train-mlogloss:2.47696	validation-mlogloss:2.47597
[24]	train-mlogloss:2.46850	validation-mlogloss:2.46730
[25]	train-mlogloss:2.45960	validation-mlogloss:2.45837
[26]	train-mlogloss:2.45120	validation-mlogloss:2.44982
[27]	train-mlogloss:2.44315	validation-mlogloss:2.44169
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[628]	train-mlogloss:1.41166	validation-mlogloss:1.48801
Saved model to disk.
2025-11-07 10:29:25.711695: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:29:25.723317: 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 00:00:1762507765.736854 2389042 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507765.741030 2389042 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507765.751322 2389042 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507765.751346 2389042 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507765.751349 2389042 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507765.751351 2389042 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:29:25.754640: 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:2.69682	validation-mlogloss:2.69678
[1]	train-mlogloss:2.68584	validation-mlogloss:2.68558
[2]	train-mlogloss:2.67559	validation-mlogloss:2.67544
[3]	train-mlogloss:2.66539	validation-mlogloss:2.66519
[4]	train-mlogloss:2.65575	validation-mlogloss:2.65562
[5]	train-mlogloss:2.64520	validation-mlogloss:2.64511
[6]	train-mlogloss:2.63519	validation-mlogloss:2.63507
[7]	train-mlogloss:2.62473	validation-mlogloss:2.62452
[8]	train-mlogloss:2.61460	validation-mlogloss:2.61426
[9]	train-mlogloss:2.60468	validation-mlogloss:2.60446
[10]	train-mlogloss:2.59523	validation-mlogloss:2.59505
[11]	train-mlogloss:2.58591	validation-mlogloss:2.58564
[12]	train-mlogloss:2.57596	validation-mlogloss:2.57557
[13]	train-mlogloss:2.56584	validation-mlogloss:2.56521
[14]	train-mlogloss:2.55642	validation-mlogloss:2.55596
[15]	train-mlogloss:2.54771	validation-mlogloss:2.54701
[16]	train-mlogloss:2.53882	validation-mlogloss:2.53806
[17]	train-mlogloss:2.52967	validation-mlogloss:2.52887
[18]	train-mlogloss:2.52069	validation-mlogloss:2.51991
[19]	train-mlogloss:2.51184	validation-mlogloss:2.51110
[20]	train-mlogloss:2.50265	validation-mlogloss:2.50186
[21]	train-mlogloss:2.49435	validation-mlogloss:2.49347
[22]	train-mlogloss:2.48525	validation-mlogloss:2.48465
[23]	train-mlogloss:2.47633	validation-mlogloss:2.47558
[24]	train-mlogloss:2.46752	validation-mlogloss:2.46686
[25]	train-mlogloss:2.45887	validation-mlogloss:2.45804
[26]	train-mlogloss:2.45040	validation-mlogloss:2.44964
[27]	train-mlogloss:2.44205	validation-mlogloss:2.44110
[28]	train-mlogloss:2.43346	validation-mlogloss:2.43258
[29]	train-mlogloss:2.42566	validation-mlogloss:2.42450
[30]	train-mlogloss:2.41757	validation-mlogloss:2.41616
[31]	train-mlogloss:2.40974	validation-mlogloss:2.40836
[32]	train-mlogloss:2.40197	validation-mlogloss:2.40068
[33]	train-mlogloss:2.39432	validation-mlogloss:2.39288
[34]	train-mlogloss:2.38655	validation-mlogloss:2.38502
[35]	train-mlogloss:2.37883	validation-mlogloss:2.37738
[36]	train-mlogloss:2.37132	validation-mlogloss:2.37004
[37]	train-mlogloss:2.36385	validation-mlogloss:2.36238
[38]	train-mlogloss:2.35670	validation-mlogloss:2.35490
[39]	train-mlogloss:2.34914	validation-mlogloss:2.34743
[40]	train-mlogloss:2.34223	validation-mlogloss:2.34047
[41]	train-mlogloss:2.33502	validation-mlogloss:2.33342
[42]	train-mlogloss:2.32820	validation-mlogloss:2.32646
[43]	train-mlogloss:2.32163	validation-mlogloss:2.31979
[44]	train-mlogloss:2.31488	validation-mlogloss:2.31291
[45]	train-mlogloss:2.30809	validation-mlogloss:2.30612
[46]	train-mlogloss:2.30143	validation-mlogloss:2.29952
[47]	train-mlogloss:2.29441	validation-mlogloss:2.29256
[48]	train-mlogloss:2.28800	validation-mlogloss:2.28637
[49]	train-mlogloss:2.28096	validation-mlogloss:2.27941
[50]	train-mlogloss:2.27443	validation-mlogloss:2.27288
[51]	train-mlogloss:2.26801	validation-mlogloss:2.26645
[52]	train-mlogloss:2.26159	validation-mlogloss:2.25992
[53]	train-mlogloss:2.25564	validation-mlogloss:2.25427
[54]	train-mlogloss:2.24957	validation-mlogloss:2.24819
[55]	train-mlogloss:2.24312	validation-mlogloss:2.24198
[56]	train-mlogloss:2.23696	validation-mlogloss:2.23585
[57]	train-mlogloss:2.23059	validation-mlogloss:2.22951
[58]	train-mlogloss:2.22458	validation-mlogloss:2.22369
[59]	train-mlogloss:2.21830	validation-mlogloss:2.21784
[60]	train-mlogloss:2.21233	validation-mlogloss:2.21201
[61]	train-mlogloss:2.20640	validation-mlogloss:2.20625
[62]	train-mlogloss:2.20049	validation-mlogloss:2.20054
[63]	train-mlogloss:2.19452	validation-mlogloss:2.19462
[64]	train-mlogloss:2.18889	validation-mlogloss:2.18904
[65]	train-mlogloss:2.18340	validation-mlogloss:2.18376
[66]	train-mlogloss:2.17771	validation-mlogloss:2.17837
[67]	train-mlogloss:2.17189	validation-mlogloss:2.17283
[68]	train-mlogloss:2.16642	validation-mlogloss:2.16722
[69]	train-mlogloss:2.16100	validation-mlogloss:2.16194
[70]	train-mlogloss:2.15579	validation-mlogloss:2.15700
[71]	train-mlogloss:2.15052	validation-mlogloss:2.15186
[72]	train-mlogloss:2.14513	validation-mlogloss:2.14645
[73]	train-mlogloss:2.13977	validation-mlogloss:2.14114
[74]	train-mlogloss:2.13463	validation-mlogloss:2.13618
[75]	train-mlogloss:2.12954	validation-mlogloss:2.13127
[76]	train-mlogloss:2.12433	validation-mlogloss:2.12621
[77]	train-mlogloss:2.11924	validation-mlogloss:2.12146
[78]	train-mlogloss:2.11421	validation-mlogloss:2.11652
[79]	train-mlogloss:2.10931	validation-mlogloss:2.11157
[80]	train-mlogloss:2.10415	validation-mlogloss:2.10649
[81]	train-mlogloss:2.09930	validation-mlogloss:2.10179
[82]	train-mlogloss:2.09425	validation-mlogloss:2.09697
[83]	train-mlogloss:2.08953	validation-mlogloss:2.09233
[84]	train-mlogloss:2.08472	validation-mlogloss:2.08790
[85]	train-mlogloss:2.08001	validation-mlogloss:2.08326
[86]	train-mlogloss:2.07531	validation-mlogloss:2.07858
[87]	train-mlogloss:2.07110	validation-mlogloss:2.07459
[88]	train-mlogloss:2.06666	validation-mlogloss:2.07028
[89]	train-mlogloss:2.06218	validation-mlogloss:2.06592
[90]	train-mlogloss:2.05761	validation-mlogloss:2.06147
[91]	train-mlogloss:2.05322	validation-mlogloss:2.05730
[92]	train-mlogloss:2.04901	validation-mlogloss:2.05304
[93]	train-mlogloss:2.04458	validation-mlogloss:2.04879
[94]	train-mlogloss:2.04041	validation-mlogloss:2.04459
[95]	train-mlogloss:2.03619	validation-mlogloss:2.04081
[96]	train-mlogloss:2.03187	validation-mlogloss:2.03665
[97]	train-mlogloss:2.02784	validation-mlogloss:2.03286
[98]	train-mlogloss:2.02361	validation-mlogloss:2.02873
[99]	train-mlogloss:2.01967	validation-mlogloss:2.02509
[100]	train-mlogloss:2.01572	validation-mlogloss:2.02135
[101]	train-mlogloss:2.01171	validation-mlogloss:2.01746
[102]	train-mlogloss:2.00777	validation-mlogloss:2.01344
[103]	train-mlogloss:2.00370	validation-mlogloss:2.00974
[104]	train-mlogloss:1.99981	validation-mlogloss:2.00607
[105]	train-mlogloss:1.99596	validation-mlogloss:2.00233
[106]	train-mlogloss:1.99230	validation-mlogloss:1.99878
[107]	train-mlogloss:1.98862	validation-mlogloss:1.99502
[108]	train-mlogloss:1.98490	validation-mlogloss:1.99147
[109]	train-mlogloss:1.98099	validation-mlogloss:1.98759
[110]	train-mlogloss:1.97724	validation-mlogloss:1.98388
[111]	train-mlogloss:1.97365	validation-mlogloss:1.98034
[112]	train-mlogloss:1.97005	validation-mlogloss:1.97705
[113]	train-mlogloss:1.96654	validation-mlogloss:1.97358
[114]	train-mlogloss:1.96299	validation-mlogloss:1.97046
[115]	train-mlogloss:1.95940	validation-mlogloss:1.96717
[116]	train-mlogloss:1.95609	validation-mlogloss:1.96397
[117]	train-mlogloss:1.95260	validation-mlogloss:1.96059
[118]	train-mlogloss:1.94907	validation-mlogloss:1.95727
[119]	train-mlogloss:1.94561	validation-mlogloss:1.95403
[120]	train-mlogloss:1.94237	validation-mlogloss:1.95100
[121]	train-mlogloss:1.93888	validation-mlogloss:1.94778
[122]	train-mlogloss:1.93541	validation-mlogloss:1.94460
[123]	train-mlogloss:1.93197	validation-mlogloss:1.94115
[124]	train-mlogloss:1.92866	validation-mlogloss:1.93799
[125]	train-mlogloss:1.92539	validation-mlogloss:1.93489
[126]	train-mlogloss:1.92205	validation-mlogloss:1.93176
[127]	train-mlogloss:1.91862	validation-mlogloss:1.92873
[128]	train-mlogloss:1.91533	validation-mlogloss:1.92569
[129]	train-mlogloss:1.91201	validation-mlogloss:1.92245
[130]	train-mlogloss:1.90893	validation-mlogloss:1.91971
[131]	train-mlogloss:1.90561	validation-mlogloss:1.91659
[132]	train-mlogloss:1.90247	validation-mlogloss:1.91367
[133]	train-mlogloss:1.89940	validation-mlogloss:1.91060
[134]	train-mlogloss:1.89615	validation-mlogloss:1.90760
[135]	train-mlogloss:1.89313	validation-mlogloss:1.90482
[136]	train-mlogloss:1.89022	validation-mlogloss:1.90210
[137]	train-mlogloss:1.88721	validation-mlogloss:1.89938
[138]	train-mlogloss:1.88424	validation-mlogloss:1.89665
[139]	train-mlogloss:1.88126	validation-mlogloss:1.89397
[140]	train-mlogloss:1.87855	validation-mlogloss:1.89133
[141]	train-mlogloss:1.87567	validation-mlogloss:1.88858
[142]	train-mlogloss:1.87282	validation-mlogloss:1.88587
[143]	train-mlogloss:1.86993	validation-mlogloss:1.88321
[144]	train-mlogloss:1.86696	validation-mlogloss:1.88047
[145]	train-mlogloss:1.86432	validation-mlogloss:1.87803
[146]	train-mlogloss:1.86140	validation-mlogloss:1.87535
[147]	train-mlogloss:1.85865	validation-mlogloss:1.87266
[148]	train-mlogloss:1.85599	validation-mlogloss:1.87032
[149]	train-mlogloss:1.85320	validation-mlogloss:1.86774
[150]	train-mlogloss:1.85037	validation-mlogloss:1.86505
[151]	train-mlogloss:1.84766	validation-mlogloss:1.86241
[152]	train-mlogloss:1.84484	validation-mlogloss:1.85980
[153]	train-mlogloss:1.84221	validation-mlogloss:1.85742
[154]	train-mlogloss:1.83958	validation-mlogloss:1.85472
[155]	train-mlogloss:1.83692	validation-mlogloss:1.85234
[156]	train-mlogloss:1.83419	validation-mlogloss:1.84984
[157]	train-mlogloss:1.83165	validation-mlogloss:1.84734
[158]	train-mlogloss:1.82907	validation-mlogloss:1.84486
[159]	train-mlogloss:1.82644	validation-mlogloss:1.84239
[160]	train-mlogloss:1.82383	validation-mlogloss:1.83990
[161]	train-mlogloss:1.82120	validation-mlogloss:1.83730
[162]	train-mlogloss:1.81865	validation-mlogloss:1.83496
[163]	train-mlogloss:1.81621	validation-mlogloss:1.83257
[164]	train-mlogloss:1.81365	validation-mlogloss:1.83025
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[170]	train-mlogloss:1.79911	validation-mlogloss:1.81641
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[189]	train-mlogloss:1.75778	validation-mlogloss:1.77760
[190]	train-mlogloss:1.75577	validation-mlogloss:1.77578
[191]	train-mlogloss:1.75382	validation-mlogloss:1.77390
[192]	train-mlogloss:1.75177	validation-mlogloss:1.77196
[193]	train-mlogloss:1.74964	validation-mlogloss:1.77008
[194]	train-mlogloss:1.74751	validation-mlogloss:1.76814
[195]	train-mlogloss:1.74568	validation-mlogloss:1.76629
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[199]	train-mlogloss:1.73797	validation-mlogloss:1.75932
[200]	train-mlogloss:1.73602	validation-mlogloss:1.75753
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[578]	train-mlogloss:1.42864	validation-mlogloss:1.49836
[579]	train-mlogloss:1.42828	validation-mlogloss:1.49815
[580]	train-mlogloss:1.42795	validation-mlogloss:1.49800
[581]	train-mlogloss:1.42761	validation-mlogloss:1.49776
[582]	train-mlogloss:1.42724	validation-mlogloss:1.49743
[583]	train-mlogloss:1.42694	validation-mlogloss:1.49734
[584]	train-mlogloss:1.42659	validation-mlogloss:1.49715
[585]	train-mlogloss:1.42624	validation-mlogloss:1.49692
[586]	train-mlogloss:1.42592	validation-mlogloss:1.49671
[587]	train-mlogloss:1.42553	validation-mlogloss:1.49647
[588]	train-mlogloss:1.42521	validation-mlogloss:1.49625
[589]	train-mlogloss:1.42482	validation-mlogloss:1.49600
[590]	train-mlogloss:1.42443	validation-mlogloss:1.49579
[591]	train-mlogloss:1.42415	validation-mlogloss:1.49555
[592]	train-mlogloss:1.42384	validation-mlogloss:1.49539
[593]	train-mlogloss:1.42348	validation-mlogloss:1.49511
[594]	train-mlogloss:1.42317	validation-mlogloss:1.49480
[595]	train-mlogloss:1.42286	validation-mlogloss:1.49468
[596]	train-mlogloss:1.42249	validation-mlogloss:1.49443
[597]	train-mlogloss:1.42216	validation-mlogloss:1.49429
[598]	train-mlogloss:1.42186	validation-mlogloss:1.49415
[599]	train-mlogloss:1.42150	validation-mlogloss:1.49391
[600]	train-mlogloss:1.42110	validation-mlogloss:1.49369
[601]	train-mlogloss:1.42077	validation-mlogloss:1.49351
[602]	train-mlogloss:1.42043	validation-mlogloss:1.49337
[603]	train-mlogloss:1.42013	validation-mlogloss:1.49320
[604]	train-mlogloss:1.41977	validation-mlogloss:1.49302
[605]	train-mlogloss:1.41939	validation-mlogloss:1.49271
[606]	train-mlogloss:1.41917	validation-mlogloss:1.49260
[607]	train-mlogloss:1.41878	validation-mlogloss:1.49247
[608]	train-mlogloss:1.41840	validation-mlogloss:1.49221
[609]	train-mlogloss:1.41812	validation-mlogloss:1.49210
[610]	train-mlogloss:1.41781	validation-mlogloss:1.49185
[611]	train-mlogloss:1.41750	validation-mlogloss:1.49163
[612]	train-mlogloss:1.41717	validation-mlogloss:1.49145
[613]	train-mlogloss:1.41682	validation-mlogloss:1.49129
[614]	train-mlogloss:1.41651	validation-mlogloss:1.49107
[615]	train-mlogloss:1.41622	validation-mlogloss:1.49090
[616]	train-mlogloss:1.41589	validation-mlogloss:1.49065
[617]	train-mlogloss:1.41557	validation-mlogloss:1.49042
[618]	train-mlogloss:1.41521	validation-mlogloss:1.49026
[619]	train-mlogloss:1.41492	validation-mlogloss:1.49013
[620]	train-mlogloss:1.41459	validation-mlogloss:1.48989
[621]	train-mlogloss:1.41432	validation-mlogloss:1.48972
[622]	train-mlogloss:1.41401	validation-mlogloss:1.48953
[623]	train-mlogloss:1.41373	validation-mlogloss:1.48943
[624]	train-mlogloss:1.41344	validation-mlogloss:1.48908
[625]	train-mlogloss:1.41319	validation-mlogloss:1.48893
[626]	train-mlogloss:1.41286	validation-mlogloss:1.48866
[627]	train-mlogloss:1.41258	validation-mlogloss:1.48855
[628]	train-mlogloss:1.41232	validation-mlogloss:1.48837
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.93 [%]
Global accuracy score (test) = 43.59 [%]
Global F1 score (train) = 55.73 [%]
Global F1 score (test) = 42.59 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.17      0.18       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.40      0.34       184
       CAMINAR USUAL SPEED       0.24      0.14      0.17       184
            CAMINAR ZIGZAG       0.28      0.29      0.28       184
          DE PIE BARRIENDO       0.47      0.51      0.49       184
   DE PIE DOBLANDO TOALLAS       0.44      0.41      0.42       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.81      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.28      0.24      0.26       184
         SENTADO USANDO PC       0.19      0.07      0.10       184
      SENTADO VIENDO LA TV       0.40      0.37      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.72      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 22: 43.59 [%]
F1-score capturado en la ejecución 22: 42.59 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.93 [%]
Global accuracy score (test) = 43.41 [%]
Global F1 score (train) = 55.75 [%]
Global F1 score (test) = 42.45 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.21      0.22       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.40      0.34       184
       CAMINAR USUAL SPEED       0.25      0.14      0.18       184
            CAMINAR ZIGZAG       0.27      0.28      0.28       184
          DE PIE BARRIENDO       0.44      0.44      0.44       184
   DE PIE DOBLANDO TOALLAS       0.41      0.41      0.41       184
    DE PIE MOVIENDO LIBROS       0.40      0.40      0.40       184
          DE PIE USANDO PC       0.53      0.65      0.59       184
        FASE REPOSO CON K5       0.41      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.92      0.60      0.73       184
           SENTADO LEYENDO       0.28      0.24      0.26       184
         SENTADO USANDO PC       0.18      0.07      0.10       184
      SENTADO VIENDO LA TV       0.40      0.37      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.55      0.72      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.43      0.42      2737


Accuracy capturado en la ejecución 23: 43.41 [%]
F1-score capturado en la ejecución 23: 42.45 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.09 [%]
Global accuracy score (test) = 44.06 [%]
Global F1 score (train) = 55.95 [%]
Global F1 score (test) = 43.11 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.20      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.32      0.41      0.36       184
       CAMINAR USUAL SPEED       0.23      0.13      0.17       184
            CAMINAR ZIGZAG       0.27      0.30      0.28       184
          DE PIE BARRIENDO       0.47      0.48      0.48       184
   DE PIE DOBLANDO TOALLAS       0.45      0.46      0.45       184
    DE PIE MOVIENDO LIBROS       0.41      0.39      0.40       184
          DE PIE USANDO PC       0.54      0.65      0.59       184
        FASE REPOSO CON K5       0.41      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.92      0.60      0.72       184
           SENTADO LEYENDO       0.28      0.24      0.26       184
         SENTADO USANDO PC       0.19      0.07      0.10       184
      SENTADO VIENDO LA TV       0.39      0.36      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.57      0.73      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.44      0.44      0.43      2737


Accuracy capturado en la ejecución 24: 44.06 [%]
F1-score capturado en la ejecución 24: 43.11 [%]

=== EJECUCIÓN 25 ===

--- TRAIN (ejecución 25) ---

--- TEST (ejecución 25) ---
2025-11-07 10:29:51.780190: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:29:51.794243: 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 00:00:1762507791.810303 2389460 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507791.814399 2389460 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507791.824436 2389460 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507791.824459 2389460 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507791.824461 2389460 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507791.824462 2389460 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:29:51.827608: 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:2.69799	validation-mlogloss:2.69780
[1]	train-mlogloss:2.68773	validation-mlogloss:2.68752
[2]	train-mlogloss:2.67726	validation-mlogloss:2.67695
[3]	train-mlogloss:2.66637	validation-mlogloss:2.66608
[4]	train-mlogloss:2.65590	validation-mlogloss:2.65558
[5]	train-mlogloss:2.64599	validation-mlogloss:2.64572
[6]	train-mlogloss:2.63619	validation-mlogloss:2.63575
[7]	train-mlogloss:2.62571	validation-mlogloss:2.62526
[8]	train-mlogloss:2.61560	validation-mlogloss:2.61517
[9]	train-mlogloss:2.60594	validation-mlogloss:2.60545
[10]	train-mlogloss:2.59596	validation-mlogloss:2.59554
[11]	train-mlogloss:2.58596	validation-mlogloss:2.58538
[12]	train-mlogloss:2.57651	validation-mlogloss:2.57560
[13]	train-mlogloss:2.56685	validation-mlogloss:2.56599
[14]	train-mlogloss:2.55728	validation-mlogloss:2.55610
[15]	train-mlogloss:2.54734	validation-mlogloss:2.54615
[16]	train-mlogloss:2.53776	validation-mlogloss:2.53675
[17]	train-mlogloss:2.52821	validation-mlogloss:2.52712
[18]	train-mlogloss:2.51898	validation-mlogloss:2.51787
[19]	train-mlogloss:2.50974	validation-mlogloss:2.50881
[20]	train-mlogloss:2.50107	validation-mlogloss:2.50002
[21]	train-mlogloss:2.49197	validation-mlogloss:2.49084
[22]	train-mlogloss:2.48362	validation-mlogloss:2.48241
[23]	train-mlogloss:2.47571	validation-mlogloss:2.47458
[24]	train-mlogloss:2.46719	validation-mlogloss:2.46608
[25]	train-mlogloss:2.45850	validation-mlogloss:2.45762
[26]	train-mlogloss:2.45041	validation-mlogloss:2.44963
[27]	train-mlogloss:2.44193	validation-mlogloss:2.44110
[28]	train-mlogloss:2.43381	validation-mlogloss:2.43273
[29]	train-mlogloss:2.42543	validation-mlogloss:2.42447
[30]	train-mlogloss:2.41746	validation-mlogloss:2.41633
[31]	train-mlogloss:2.40912	validation-mlogloss:2.40820
[32]	train-mlogloss:2.40113	validation-mlogloss:2.40028
[33]	train-mlogloss:2.39308	validation-mlogloss:2.39237
[34]	train-mlogloss:2.38509	validation-mlogloss:2.38437
[35]	train-mlogloss:2.37766	validation-mlogloss:2.37695
[36]	train-mlogloss:2.37038	validation-mlogloss:2.36948
[37]	train-mlogloss:2.36299	validation-mlogloss:2.36210
[38]	train-mlogloss:2.35547	validation-mlogloss:2.35467
[39]	train-mlogloss:2.34824	validation-mlogloss:2.34721
[40]	train-mlogloss:2.34083	validation-mlogloss:2.33980
[41]	train-mlogloss:2.33381	validation-mlogloss:2.33254
[42]	train-mlogloss:2.32670	validation-mlogloss:2.32555
[43]	train-mlogloss:2.31969	validation-mlogloss:2.31840
[44]	train-mlogloss:2.31315	validation-mlogloss:2.31183
[45]	train-mlogloss:2.30624	validation-mlogloss:2.30501
[46]	train-mlogloss:2.29948	validation-mlogloss:2.29824
[47]	train-mlogloss:2.29271	validation-mlogloss:2.29152
[48]	train-mlogloss:2.28586	validation-mlogloss:2.28494
[49]	train-mlogloss:2.27947	validation-mlogloss:2.27852
[50]	train-mlogloss:2.27299	validation-mlogloss:2.27219
[51]	train-mlogloss:2.26670	validation-mlogloss:2.26594
[52]	train-mlogloss:2.26019	validation-mlogloss:2.25967
[53]	train-mlogloss:2.25377	validation-mlogloss:2.25313
[54]	train-mlogloss:2.24750	validation-mlogloss:2.24693
[55]	train-mlogloss:2.24099	validation-mlogloss:2.24055
[56]	train-mlogloss:2.23483	validation-mlogloss:2.23458
[57]	train-mlogloss:2.22887	validation-mlogloss:2.22853
[58]	train-mlogloss:2.22312	validation-mlogloss:2.22314
[59]	train-mlogloss:2.21701	validation-mlogloss:2.21707
[60]	train-mlogloss:2.21120	validation-mlogloss:2.21150
[61]	train-mlogloss:2.20541	validation-mlogloss:2.20589
[62]	train-mlogloss:2.19967	validation-mlogloss:2.20033
[63]	train-mlogloss:2.19389	validation-mlogloss:2.19492
[64]	train-mlogloss:2.18826	validation-mlogloss:2.18929
[65]	train-mlogloss:2.18300	validation-mlogloss:2.18431
[66]	train-mlogloss:2.17722	validation-mlogloss:2.17859
[67]	train-mlogloss:2.17158	validation-mlogloss:2.17311
[68]	train-mlogloss:2.16605	validation-mlogloss:2.16767
[69]	train-mlogloss:2.16064	validation-mlogloss:2.16229
[70]	train-mlogloss:2.15514	validation-mlogloss:2.15695
[71]	train-mlogloss:2.14986	validation-mlogloss:2.15162
[72]	train-mlogloss:2.14448	validation-mlogloss:2.14643
[73]	train-mlogloss:2.13924	validation-mlogloss:2.14154
[74]	train-mlogloss:2.13392	validation-mlogloss:2.13636
[75]	train-mlogloss:2.12872	validation-mlogloss:2.13118
[76]	train-mlogloss:2.12343	validation-mlogloss:2.12611
[77]	train-mlogloss:2.11838	validation-mlogloss:2.12113
[78]	train-mlogloss:2.11333	validation-mlogloss:2.11643
[79]	train-mlogloss:2.10847	validation-mlogloss:2.11169
[80]	train-mlogloss:2.10352	validation-mlogloss:2.10676
[81]	train-mlogloss:2.09873	validation-mlogloss:2.10212
[82]	train-mlogloss:2.09369	validation-mlogloss:2.09735
[83]	train-mlogloss:2.08916	validation-mlogloss:2.09286
[84]	train-mlogloss:2.08439	validation-mlogloss:2.08790
[85]	train-mlogloss:2.07988	validation-mlogloss:2.08364
[86]	train-mlogloss:2.07502	validation-mlogloss:2.07869
[87]	train-mlogloss:2.07053	validation-mlogloss:2.07424
[88]	train-mlogloss:2.06599	validation-mlogloss:2.06982
[89]	train-mlogloss:2.06159	validation-mlogloss:2.06557
[90]	train-mlogloss:2.05713	validation-mlogloss:2.06122
[91]	train-mlogloss:2.05286	validation-mlogloss:2.05742
[92]	train-mlogloss:2.04843	validation-mlogloss:2.05329
[93]	train-mlogloss:2.04421	validation-mlogloss:2.04921
[94]	train-mlogloss:2.04014	validation-mlogloss:2.04531
[95]	train-mlogloss:2.03584	validation-mlogloss:2.04119
[96]	train-mlogloss:2.03125	validation-mlogloss:2.03691
[97]	train-mlogloss:2.02703	validation-mlogloss:2.03313
[98]	train-mlogloss:2.02292	validation-mlogloss:2.02925
[99]	train-mlogloss:2.01884	validation-mlogloss:2.02538
[100]	train-mlogloss:2.01495	validation-mlogloss:2.02169
[101]	train-mlogloss:2.01089	validation-mlogloss:2.01800
[102]	train-mlogloss:2.00701	validation-mlogloss:2.01414
[103]	train-mlogloss:2.00301	validation-mlogloss:2.01047
[104]	train-mlogloss:1.99894	validation-mlogloss:2.00662
[105]	train-mlogloss:1.99504	validation-mlogloss:2.00278
[106]	train-mlogloss:1.99102	validation-mlogloss:1.99892
[107]	train-mlogloss:1.98707	validation-mlogloss:1.99502
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[472]	train-mlogloss:1.47200	validation-mlogloss:1.52703
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[477]	train-mlogloss:1.46945	validation-mlogloss:1.52527
[478]	train-mlogloss:1.46898	validation-mlogloss:1.52492
[479]	train-mlogloss:1.46847	validation-mlogloss:1.52450
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[559]	train-mlogloss:1.43503	validation-mlogloss:1.50152
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[564]	train-mlogloss:1.43313	validation-mlogloss:1.50030
[565]	train-mlogloss:1.43273	validation-mlogloss:1.50010
[566]	train-mlogloss:1.43237	validation-mlogloss:1.49995
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[588]	train-mlogloss:1.42499	validation-mlogloss:1.49506
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[592]	train-mlogloss:1.42364	validation-mlogloss:1.49431
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[594]	train-mlogloss:1.42301	validation-mlogloss:1.49395
[595]	train-mlogloss:1.42270	validation-mlogloss:1.49365
[596]	train-mlogloss:1.42239	validation-mlogloss:1.49343
[597]	train-mlogloss:1.42201	validation-mlogloss:1.49322
[598]	train-mlogloss:1.42168	validation-mlogloss:1.49304
[599]	train-mlogloss:1.42139	validation-mlogloss:1.49285
[600]	train-mlogloss:1.42103	validation-mlogloss:1.49259
[601]	train-mlogloss:1.42070	validation-mlogloss:1.49246
[602]	train-mlogloss:1.42037	validation-mlogloss:1.49226
[603]	train-mlogloss:1.41998	validation-mlogloss:1.49210
[604]	train-mlogloss:1.41961	validation-mlogloss:1.49194
[605]	train-mlogloss:1.41924	validation-mlogloss:1.49182
[606]	train-mlogloss:1.41893	validation-mlogloss:1.49165
[607]	train-mlogloss:1.41861	validation-mlogloss:1.49147
[608]	train-mlogloss:1.41832	validation-mlogloss:1.49128
[609]	train-mlogloss:1.41797	validation-mlogloss:1.49099
[610]	train-mlogloss:1.41768	validation-mlogloss:1.49079
[611]	train-mlogloss:1.41737	validation-mlogloss:1.49064
[612]	train-mlogloss:1.41699	validation-mlogloss:1.49031
[613]	train-mlogloss:1.41667	validation-mlogloss:1.49012
[614]	train-mlogloss:1.41634	validation-mlogloss:1.48985
[615]	train-mlogloss:1.41602	validation-mlogloss:1.48962
[616]	train-mlogloss:1.41573	validation-mlogloss:1.48942
[617]	train-mlogloss:1.41538	validation-mlogloss:1.48917
[618]	train-mlogloss:1.41508	validation-mlogloss:1.48898
[619]	train-mlogloss:1.41475	validation-mlogloss:1.48878
[620]	train-mlogloss:1.41447	validation-mlogloss:1.48864
[621]	train-mlogloss:1.41411	validation-mlogloss:1.48851
[622]	train-mlogloss:1.41378	validation-mlogloss:1.48828
[623]	train-mlogloss:1.41352	validation-mlogloss:1.48806
[624]	train-mlogloss:1.41320	validation-mlogloss:1.48783
[625]	train-mlogloss:1.41298	validation-mlogloss:1.48772
[626]	train-mlogloss:1.41265	validation-mlogloss:1.48754
[627]	train-mlogloss:1.41235	validation-mlogloss:1.48750
[628]	train-mlogloss:1.41201	validation-mlogloss:1.48730
Saved model to disk.
2025-11-07 10:30:17.620859: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:30:17.632793: 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 00:00:1762507817.646646 2389887 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507817.650683 2389887 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507817.660416 2389887 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507817.660436 2389887 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507817.660438 2389887 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507817.660440 2389887 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:30:17.663630: 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:2.69697	validation-mlogloss:2.69710
[1]	train-mlogloss:2.68615	validation-mlogloss:2.68641
[2]	train-mlogloss:2.67537	validation-mlogloss:2.67570
[3]	train-mlogloss:2.66457	validation-mlogloss:2.66489
[4]	train-mlogloss:2.65392	validation-mlogloss:2.65407
[5]	train-mlogloss:2.64369	validation-mlogloss:2.64365
[6]	train-mlogloss:2.63333	validation-mlogloss:2.63311
[7]	train-mlogloss:2.62396	validation-mlogloss:2.62362
[8]	train-mlogloss:2.61405	validation-mlogloss:2.61343
[9]	train-mlogloss:2.60446	validation-mlogloss:2.60375
[10]	train-mlogloss:2.59449	validation-mlogloss:2.59373
[11]	train-mlogloss:2.58507	validation-mlogloss:2.58415
[12]	train-mlogloss:2.57561	validation-mlogloss:2.57466
[13]	train-mlogloss:2.56615	validation-mlogloss:2.56516
[14]	train-mlogloss:2.55697	validation-mlogloss:2.55606
[15]	train-mlogloss:2.54794	validation-mlogloss:2.54693
[16]	train-mlogloss:2.53897	validation-mlogloss:2.53788
[17]	train-mlogloss:2.52950	validation-mlogloss:2.52857
[18]	train-mlogloss:2.52041	validation-mlogloss:2.51938
[19]	train-mlogloss:2.51164	validation-mlogloss:2.51048
[20]	train-mlogloss:2.50252	validation-mlogloss:2.50154
[21]	train-mlogloss:2.49356	validation-mlogloss:2.49267
[22]	train-mlogloss:2.48462	validation-mlogloss:2.48389
[23]	train-mlogloss:2.47591	validation-mlogloss:2.47509
[24]	train-mlogloss:2.46699	validation-mlogloss:2.46592
[25]	train-mlogloss:2.45856	validation-mlogloss:2.45769
[26]	train-mlogloss:2.45002	validation-mlogloss:2.44922
[27]	train-mlogloss:2.44137	validation-mlogloss:2.44065
[28]	train-mlogloss:2.43337	validation-mlogloss:2.43271
[29]	train-mlogloss:2.42485	validation-mlogloss:2.42423
[30]	train-mlogloss:2.41699	validation-mlogloss:2.41644
[31]	train-mlogloss:2.40874	validation-mlogloss:2.40828
[32]	train-mlogloss:2.40102	validation-mlogloss:2.40048
[33]	train-mlogloss:2.39310	validation-mlogloss:2.39241
[34]	train-mlogloss:2.38565	validation-mlogloss:2.38493
[35]	train-mlogloss:2.37787	validation-mlogloss:2.37706
[36]	train-mlogloss:2.37022	validation-mlogloss:2.36962
[37]	train-mlogloss:2.36253	validation-mlogloss:2.36195
[38]	train-mlogloss:2.35538	validation-mlogloss:2.35478
[39]	train-mlogloss:2.34846	validation-mlogloss:2.34773
[40]	train-mlogloss:2.34157	validation-mlogloss:2.34081
[41]	train-mlogloss:2.33423	validation-mlogloss:2.33317
[42]	train-mlogloss:2.32716	validation-mlogloss:2.32601
[43]	train-mlogloss:2.32012	validation-mlogloss:2.31886
[44]	train-mlogloss:2.31338	validation-mlogloss:2.31224
[45]	train-mlogloss:2.30659	validation-mlogloss:2.30545
[46]	train-mlogloss:2.29950	validation-mlogloss:2.29847
[47]	train-mlogloss:2.29306	validation-mlogloss:2.29200
[48]	train-mlogloss:2.28661	validation-mlogloss:2.28572
[49]	train-mlogloss:2.28016	validation-mlogloss:2.27912
[50]	train-mlogloss:2.27386	validation-mlogloss:2.27268
[51]	train-mlogloss:2.26734	validation-mlogloss:2.26621
[52]	train-mlogloss:2.26114	validation-mlogloss:2.26009
[53]	train-mlogloss:2.25462	validation-mlogloss:2.25362
[54]	train-mlogloss:2.24847	validation-mlogloss:2.24746
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[613]	train-mlogloss:1.41700	validation-mlogloss:1.49012
[614]	train-mlogloss:1.41667	validation-mlogloss:1.48989
[615]	train-mlogloss:1.41642	validation-mlogloss:1.48978
[616]	train-mlogloss:1.41606	validation-mlogloss:1.48964
[617]	train-mlogloss:1.41573	validation-mlogloss:1.48946
[618]	train-mlogloss:1.41542	validation-mlogloss:1.48935
[619]	train-mlogloss:1.41523	validation-mlogloss:1.48924
[620]	train-mlogloss:1.41491	validation-mlogloss:1.48901
[621]	train-mlogloss:1.41462	validation-mlogloss:1.48871
[622]	train-mlogloss:1.41431	validation-mlogloss:1.48853
[623]	train-mlogloss:1.41401	validation-mlogloss:1.48839
[624]	train-mlogloss:1.41376	validation-mlogloss:1.48823
[625]	train-mlogloss:1.41344	validation-mlogloss:1.48807
[626]	train-mlogloss:1.41303	validation-mlogloss:1.48789
[627]	train-mlogloss:1.41274	validation-mlogloss:1.48773
[628]	train-mlogloss:1.41242	validation-mlogloss:1.48758
Saved model to disk.
2025-11-07 10:30:43.233728: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:30:43.244923: 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 00:00:1762507843.258356 2390297 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507843.262351 2390297 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507843.272531 2390297 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507843.272551 2390297 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507843.272553 2390297 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507843.272555 2390297 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:30:43.275678: 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:2.69730	validation-mlogloss:2.69700
[1]	train-mlogloss:2.68669	validation-mlogloss:2.68614
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[603]	train-mlogloss:1.41946	validation-mlogloss:1.49100
[604]	train-mlogloss:1.41911	validation-mlogloss:1.49079
[605]	train-mlogloss:1.41879	validation-mlogloss:1.49057
[606]	train-mlogloss:1.41846	validation-mlogloss:1.49033
[607]	train-mlogloss:1.41818	validation-mlogloss:1.49021
[608]	train-mlogloss:1.41781	validation-mlogloss:1.48991
[609]	train-mlogloss:1.41750	validation-mlogloss:1.48967
[610]	train-mlogloss:1.41717	validation-mlogloss:1.48950
[611]	train-mlogloss:1.41687	validation-mlogloss:1.48929
[612]	train-mlogloss:1.41657	validation-mlogloss:1.48906
[613]	train-mlogloss:1.41625	validation-mlogloss:1.48886
[614]	train-mlogloss:1.41596	validation-mlogloss:1.48859
[615]	train-mlogloss:1.41565	validation-mlogloss:1.48842
[616]	train-mlogloss:1.41531	validation-mlogloss:1.48822
[617]	train-mlogloss:1.41495	validation-mlogloss:1.48802
[618]	train-mlogloss:1.41465	validation-mlogloss:1.48797
[619]	train-mlogloss:1.41438	validation-mlogloss:1.48784
[620]	train-mlogloss:1.41407	validation-mlogloss:1.48762
[621]	train-mlogloss:1.41372	validation-mlogloss:1.48748
[622]	train-mlogloss:1.41344	validation-mlogloss:1.48735
[623]	train-mlogloss:1.41312	validation-mlogloss:1.48722
[624]	train-mlogloss:1.41285	validation-mlogloss:1.48704
[625]	train-mlogloss:1.41255	validation-mlogloss:1.48685
[626]	train-mlogloss:1.41226	validation-mlogloss:1.48667
[627]	train-mlogloss:1.41201	validation-mlogloss:1.48652
[628]	train-mlogloss:1.41170	validation-mlogloss:1.48640
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.0 [%]
Global accuracy score (test) = 43.15 [%]
Global F1 score (train) = 55.83 [%]
Global F1 score (test) = 42.12 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.23      0.23       184
 CAMINAR CON MÓVIL O LIBRO       0.29      0.38      0.33       184
       CAMINAR USUAL SPEED       0.23      0.12      0.16       184
            CAMINAR ZIGZAG       0.30      0.28      0.29       184
          DE PIE BARRIENDO       0.44      0.46      0.45       184
   DE PIE DOBLANDO TOALLAS       0.42      0.42      0.42       184
    DE PIE MOVIENDO LIBROS       0.38      0.39      0.38       184
          DE PIE USANDO PC       0.54      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.81      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.59      0.71       184
           SENTADO LEYENDO       0.28      0.24      0.26       184
         SENTADO USANDO PC       0.12      0.04      0.06       184
      SENTADO VIENDO LA TV       0.39      0.35      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.43      0.42      2737
              weighted avg       0.42      0.43      0.42      2737


Accuracy capturado en la ejecución 25: 43.15 [%]
F1-score capturado en la ejecución 25: 42.12 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.92 [%]
Global accuracy score (test) = 43.59 [%]
Global F1 score (train) = 55.72 [%]
Global F1 score (test) = 42.56 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.22      0.22       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.40      0.35       184
       CAMINAR USUAL SPEED       0.26      0.13      0.17       184
            CAMINAR ZIGZAG       0.28      0.28      0.28       184
          DE PIE BARRIENDO       0.46      0.49      0.48       184
   DE PIE DOBLANDO TOALLAS       0.45      0.41      0.43       184
    DE PIE MOVIENDO LIBROS       0.37      0.39      0.38       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.26      0.20      0.22       184
         SENTADO USANDO PC       0.16      0.07      0.09       184
      SENTADO VIENDO LA TV       0.39      0.39      0.39       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.43      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 26: 43.59 [%]
F1-score capturado en la ejecución 26: 42.56 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.93 [%]
Global accuracy score (test) = 43.41 [%]
Global F1 score (train) = 55.79 [%]
Global F1 score (test) = 42.26 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.20      0.18      0.19       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.40      0.35       184
       CAMINAR USUAL SPEED       0.27      0.14      0.18       184
            CAMINAR ZIGZAG       0.31      0.32      0.31       184
          DE PIE BARRIENDO       0.47      0.48      0.48       184
   DE PIE DOBLANDO TOALLAS       0.43      0.43      0.43       184
    DE PIE MOVIENDO LIBROS       0.40      0.38      0.39       184
          DE PIE USANDO PC       0.52      0.65      0.58       184
        FASE REPOSO CON K5       0.41      0.81      0.54       184
INCREMENTAL CICLOERGOMETRO       0.90      0.59      0.71       184
           SENTADO LEYENDO       0.25      0.21      0.23       184
         SENTADO USANDO PC       0.12      0.04      0.06       184
      SENTADO VIENDO LA TV       0.38      0.36      0.37       184
   SUBIR Y BAJAR ESCALERAS       0.54      0.73      0.62       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.43      2737
                 macro avg       0.43      0.44      0.42      2737
              weighted avg       0.43      0.43      0.42      2737


Accuracy capturado en la ejecución 27: 43.41 [%]
F1-score capturado en la ejecución 27: 42.26 [%]

=== EJECUCIÓN 28 ===

--- TRAIN (ejecución 28) ---

--- TEST (ejecución 28) ---
2025-11-07 10:31:08.758078: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:31:08.769445: 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 00:00:1762507868.782594 2390708 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507868.786726 2390708 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507868.796568 2390708 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507868.796587 2390708 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507868.796589 2390708 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507868.796590 2390708 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:31:08.799774: 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:2.69759	validation-mlogloss:2.69769
[1]	train-mlogloss:2.68652	validation-mlogloss:2.68671
[2]	train-mlogloss:2.67550	validation-mlogloss:2.67579
[3]	train-mlogloss:2.66479	validation-mlogloss:2.66523
[4]	train-mlogloss:2.65461	validation-mlogloss:2.65505
[5]	train-mlogloss:2.64428	validation-mlogloss:2.64469
[6]	train-mlogloss:2.63390	validation-mlogloss:2.63422
[7]	train-mlogloss:2.62427	validation-mlogloss:2.62448
[8]	train-mlogloss:2.61486	validation-mlogloss:2.61502
[9]	train-mlogloss:2.60469	validation-mlogloss:2.60494
[10]	train-mlogloss:2.59499	validation-mlogloss:2.59503
[11]	train-mlogloss:2.58539	validation-mlogloss:2.58543
[12]	train-mlogloss:2.57546	validation-mlogloss:2.57553
[13]	train-mlogloss:2.56607	validation-mlogloss:2.56616
[14]	train-mlogloss:2.55711	validation-mlogloss:2.55692
[15]	train-mlogloss:2.54768	validation-mlogloss:2.54735
[16]	train-mlogloss:2.53856	validation-mlogloss:2.53813
[17]	train-mlogloss:2.52966	validation-mlogloss:2.52905
[18]	train-mlogloss:2.52059	validation-mlogloss:2.51971
[19]	train-mlogloss:2.51174	validation-mlogloss:2.51071
[20]	train-mlogloss:2.50243	validation-mlogloss:2.50122
[21]	train-mlogloss:2.49317	validation-mlogloss:2.49210
[22]	train-mlogloss:2.48424	validation-mlogloss:2.48314
[23]	train-mlogloss:2.47563	validation-mlogloss:2.47455
[24]	train-mlogloss:2.46701	validation-mlogloss:2.46596
[25]	train-mlogloss:2.45848	validation-mlogloss:2.45729
[26]	train-mlogloss:2.45040	validation-mlogloss:2.44903
[27]	train-mlogloss:2.44187	validation-mlogloss:2.44036
[28]	train-mlogloss:2.43348	validation-mlogloss:2.43200
[29]	train-mlogloss:2.42506	validation-mlogloss:2.42360
[30]	train-mlogloss:2.41673	validation-mlogloss:2.41552
[31]	train-mlogloss:2.40841	validation-mlogloss:2.40719
[32]	train-mlogloss:2.40049	validation-mlogloss:2.39931
[33]	train-mlogloss:2.39268	validation-mlogloss:2.39177
[34]	train-mlogloss:2.38464	validation-mlogloss:2.38378
[35]	train-mlogloss:2.37679	validation-mlogloss:2.37615
[36]	train-mlogloss:2.36902	validation-mlogloss:2.36842
[37]	train-mlogloss:2.36140	validation-mlogloss:2.36088
[38]	train-mlogloss:2.35415	validation-mlogloss:2.35357
[39]	train-mlogloss:2.34676	validation-mlogloss:2.34586
[40]	train-mlogloss:2.33941	validation-mlogloss:2.33854
[41]	train-mlogloss:2.33240	validation-mlogloss:2.33174
[42]	train-mlogloss:2.32517	validation-mlogloss:2.32446
[43]	train-mlogloss:2.31814	validation-mlogloss:2.31742
[44]	train-mlogloss:2.31104	validation-mlogloss:2.31034
[45]	train-mlogloss:2.30435	validation-mlogloss:2.30383
[46]	train-mlogloss:2.29745	validation-mlogloss:2.29708
[47]	train-mlogloss:2.29049	validation-mlogloss:2.29011
[48]	train-mlogloss:2.28382	validation-mlogloss:2.28316
[49]	train-mlogloss:2.27735	validation-mlogloss:2.27675
[50]	train-mlogloss:2.27067	validation-mlogloss:2.27005
[51]	train-mlogloss:2.26431	validation-mlogloss:2.26372
[52]	train-mlogloss:2.25796	validation-mlogloss:2.25748
[53]	train-mlogloss:2.25157	validation-mlogloss:2.25112
[54]	train-mlogloss:2.24537	validation-mlogloss:2.24503
[55]	train-mlogloss:2.23936	validation-mlogloss:2.23903
[56]	train-mlogloss:2.23304	validation-mlogloss:2.23289
[57]	train-mlogloss:2.22687	validation-mlogloss:2.22713
[58]	train-mlogloss:2.22085	validation-mlogloss:2.22115
[59]	train-mlogloss:2.21491	validation-mlogloss:2.21542
[60]	train-mlogloss:2.20912	validation-mlogloss:2.20978
[61]	train-mlogloss:2.20328	validation-mlogloss:2.20398
[62]	train-mlogloss:2.19750	validation-mlogloss:2.19823
[63]	train-mlogloss:2.19200	validation-mlogloss:2.19296
[64]	train-mlogloss:2.18620	validation-mlogloss:2.18755
[65]	train-mlogloss:2.18033	validation-mlogloss:2.18179
[66]	train-mlogloss:2.17470	validation-mlogloss:2.17608
[67]	train-mlogloss:2.16905	validation-mlogloss:2.17088
[68]	train-mlogloss:2.16366	validation-mlogloss:2.16555
[69]	train-mlogloss:2.15809	validation-mlogloss:2.16020
[70]	train-mlogloss:2.15254	validation-mlogloss:2.15493
[71]	train-mlogloss:2.14715	validation-mlogloss:2.14961
[72]	train-mlogloss:2.14180	validation-mlogloss:2.14457
[73]	train-mlogloss:2.13659	validation-mlogloss:2.13927
[74]	train-mlogloss:2.13143	validation-mlogloss:2.13409
[75]	train-mlogloss:2.12646	validation-mlogloss:2.12930
[76]	train-mlogloss:2.12149	validation-mlogloss:2.12436
[77]	train-mlogloss:2.11651	validation-mlogloss:2.11961
[78]	train-mlogloss:2.11151	validation-mlogloss:2.11473
[79]	train-mlogloss:2.10647	validation-mlogloss:2.10992
[80]	train-mlogloss:2.10165	validation-mlogloss:2.10530
[81]	train-mlogloss:2.09709	validation-mlogloss:2.10087
[82]	train-mlogloss:2.09250	validation-mlogloss:2.09661
[83]	train-mlogloss:2.08780	validation-mlogloss:2.09237
[84]	train-mlogloss:2.08305	validation-mlogloss:2.08792
[85]	train-mlogloss:2.07834	validation-mlogloss:2.08327
[86]	train-mlogloss:2.07383	validation-mlogloss:2.07862
[87]	train-mlogloss:2.06918	validation-mlogloss:2.07416
[88]	train-mlogloss:2.06496	validation-mlogloss:2.07007
[89]	train-mlogloss:2.06042	validation-mlogloss:2.06567
[90]	train-mlogloss:2.05603	validation-mlogloss:2.06152
[91]	train-mlogloss:2.05178	validation-mlogloss:2.05746
[92]	train-mlogloss:2.04740	validation-mlogloss:2.05325
[93]	train-mlogloss:2.04303	validation-mlogloss:2.04897
[94]	train-mlogloss:2.03881	validation-mlogloss:2.04472
[95]	train-mlogloss:2.03466	validation-mlogloss:2.04091
[96]	train-mlogloss:2.03042	validation-mlogloss:2.03677
[97]	train-mlogloss:2.02621	validation-mlogloss:2.03299
[98]	train-mlogloss:2.02216	validation-mlogloss:2.02917
[99]	train-mlogloss:2.01823	validation-mlogloss:2.02546
[100]	train-mlogloss:2.01417	validation-mlogloss:2.02169
[101]	train-mlogloss:2.01027	validation-mlogloss:2.01777
[102]	train-mlogloss:2.00633	validation-mlogloss:2.01400
[103]	train-mlogloss:2.00241	validation-mlogloss:2.01027
[104]	train-mlogloss:1.99855	validation-mlogloss:2.00656
[105]	train-mlogloss:1.99479	validation-mlogloss:2.00294
[106]	train-mlogloss:1.99092	validation-mlogloss:1.99917
[107]	train-mlogloss:1.98705	validation-mlogloss:1.99564
[108]	train-mlogloss:1.98338	validation-mlogloss:1.99201
[109]	train-mlogloss:1.97962	validation-mlogloss:1.98824
[110]	train-mlogloss:1.97585	validation-mlogloss:1.98466
[111]	train-mlogloss:1.97218	validation-mlogloss:1.98101
[112]	train-mlogloss:1.96857	validation-mlogloss:1.97755
[113]	train-mlogloss:1.96497	validation-mlogloss:1.97413
[114]	train-mlogloss:1.96152	validation-mlogloss:1.97063
[115]	train-mlogloss:1.95795	validation-mlogloss:1.96721
[116]	train-mlogloss:1.95443	validation-mlogloss:1.96408
[117]	train-mlogloss:1.95087	validation-mlogloss:1.96057
[118]	train-mlogloss:1.94742	validation-mlogloss:1.95720
[119]	train-mlogloss:1.94385	validation-mlogloss:1.95379
[120]	train-mlogloss:1.94047	validation-mlogloss:1.95057
[121]	train-mlogloss:1.93704	validation-mlogloss:1.94734
[122]	train-mlogloss:1.93362	validation-mlogloss:1.94425
[123]	train-mlogloss:1.93034	validation-mlogloss:1.94106
[124]	train-mlogloss:1.92690	validation-mlogloss:1.93786
[125]	train-mlogloss:1.92370	validation-mlogloss:1.93476
[126]	train-mlogloss:1.92059	validation-mlogloss:1.93161
[127]	train-mlogloss:1.91735	validation-mlogloss:1.92852
[128]	train-mlogloss:1.91422	validation-mlogloss:1.92564
[129]	train-mlogloss:1.91105	validation-mlogloss:1.92267
[130]	train-mlogloss:1.90786	validation-mlogloss:1.91956
[131]	train-mlogloss:1.90469	validation-mlogloss:1.91678
[132]	train-mlogloss:1.90153	validation-mlogloss:1.91372
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[579]	train-mlogloss:1.42825	validation-mlogloss:1.49703
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[597]	train-mlogloss:1.42211	validation-mlogloss:1.49379
[598]	train-mlogloss:1.42180	validation-mlogloss:1.49362
[599]	train-mlogloss:1.42151	validation-mlogloss:1.49349
[600]	train-mlogloss:1.42121	validation-mlogloss:1.49341
[601]	train-mlogloss:1.42089	validation-mlogloss:1.49334
[602]	train-mlogloss:1.42054	validation-mlogloss:1.49316
[603]	train-mlogloss:1.42022	validation-mlogloss:1.49295
[604]	train-mlogloss:1.41990	validation-mlogloss:1.49274
[605]	train-mlogloss:1.41958	validation-mlogloss:1.49260
[606]	train-mlogloss:1.41920	validation-mlogloss:1.49241
[607]	train-mlogloss:1.41889	validation-mlogloss:1.49226
[608]	train-mlogloss:1.41857	validation-mlogloss:1.49202
[609]	train-mlogloss:1.41823	validation-mlogloss:1.49199
[610]	train-mlogloss:1.41787	validation-mlogloss:1.49171
[611]	train-mlogloss:1.41752	validation-mlogloss:1.49150
[612]	train-mlogloss:1.41716	validation-mlogloss:1.49132
[613]	train-mlogloss:1.41680	validation-mlogloss:1.49114
[614]	train-mlogloss:1.41650	validation-mlogloss:1.49094
[615]	train-mlogloss:1.41620	validation-mlogloss:1.49077
[616]	train-mlogloss:1.41587	validation-mlogloss:1.49061
[617]	train-mlogloss:1.41556	validation-mlogloss:1.49051
[618]	train-mlogloss:1.41531	validation-mlogloss:1.49036
[619]	train-mlogloss:1.41504	validation-mlogloss:1.49025
[620]	train-mlogloss:1.41478	validation-mlogloss:1.49016
[621]	train-mlogloss:1.41448	validation-mlogloss:1.48998
[622]	train-mlogloss:1.41418	validation-mlogloss:1.48975
[623]	train-mlogloss:1.41383	validation-mlogloss:1.48949
[624]	train-mlogloss:1.41359	validation-mlogloss:1.48934
[625]	train-mlogloss:1.41328	validation-mlogloss:1.48918
[626]	train-mlogloss:1.41297	validation-mlogloss:1.48897
[627]	train-mlogloss:1.41262	validation-mlogloss:1.48871
[628]	train-mlogloss:1.41239	validation-mlogloss:1.48852
Saved model to disk.
2025-11-07 10:31:34.255786: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-11-07 10:31:34.267068: 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 00:00:1762507894.280617 2391127 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1762507894.284623 2391127 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1762507894.294722 2391127 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507894.294741 2391127 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507894.294744 2391127 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1762507894.294745 2391127 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-11-07 10:31:34.297718: 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:2.69726	validation-mlogloss:2.69704
[1]	train-mlogloss:2.68667	validation-mlogloss:2.68634
[2]	train-mlogloss:2.67586	validation-mlogloss:2.67532
[3]	train-mlogloss:2.66512	validation-mlogloss:2.66458
[4]	train-mlogloss:2.65521	validation-mlogloss:2.65486
[5]	train-mlogloss:2.64474	validation-mlogloss:2.64428
[6]	train-mlogloss:2.63470	validation-mlogloss:2.63437
[7]	train-mlogloss:2.62490	validation-mlogloss:2.62433
[8]	train-mlogloss:2.61465	validation-mlogloss:2.61411
[9]	train-mlogloss:2.60433	validation-mlogloss:2.60360
[10]	train-mlogloss:2.59416	validation-mlogloss:2.59342
[11]	train-mlogloss:2.58442	validation-mlogloss:2.58371
[12]	train-mlogloss:2.57457	validation-mlogloss:2.57392
[13]	train-mlogloss:2.56483	validation-mlogloss:2.56405
[14]	train-mlogloss:2.55554	validation-mlogloss:2.55476
[15]	train-mlogloss:2.54614	validation-mlogloss:2.54550
[16]	train-mlogloss:2.53706	validation-mlogloss:2.53642
[17]	train-mlogloss:2.52829	validation-mlogloss:2.52762
[18]	train-mlogloss:2.51867	validation-mlogloss:2.51781
[19]	train-mlogloss:2.50919	validation-mlogloss:2.50833
[20]	train-mlogloss:2.50021	validation-mlogloss:2.49930
[21]	train-mlogloss:2.49093	validation-mlogloss:2.49012
[22]	train-mlogloss:2.48244	validation-mlogloss:2.48157
[23]	train-mlogloss:2.47378	validation-mlogloss:2.47294
[24]	train-mlogloss:2.46533	validation-mlogloss:2.46423
[25]	train-mlogloss:2.45647	validation-mlogloss:2.45543
[26]	train-mlogloss:2.44796	validation-mlogloss:2.44689
[27]	train-mlogloss:2.44018	validation-mlogloss:2.43900
[28]	train-mlogloss:2.43185	validation-mlogloss:2.43062
[29]	train-mlogloss:2.42350	validation-mlogloss:2.42218
[30]	train-mlogloss:2.41521	validation-mlogloss:2.41382
[31]	train-mlogloss:2.40774	validation-mlogloss:2.40621
[32]	train-mlogloss:2.39958	validation-mlogloss:2.39809
[33]	train-mlogloss:2.39195	validation-mlogloss:2.39047
[34]	train-mlogloss:2.38425	validation-mlogloss:2.38281
[35]	train-mlogloss:2.37659	validation-mlogloss:2.37504
[36]	train-mlogloss:2.36901	validation-mlogloss:2.36751
[37]	train-mlogloss:2.36132	validation-mlogloss:2.35982
[38]	train-mlogloss:2.35414	validation-mlogloss:2.35292
[39]	train-mlogloss:2.34693	validation-mlogloss:2.34567
[40]	train-mlogloss:2.33995	validation-mlogloss:2.33872
[41]	train-mlogloss:2.33258	validation-mlogloss:2.33132
[42]	train-mlogloss:2.32550	validation-mlogloss:2.32414
[43]	train-mlogloss:2.31841	validation-mlogloss:2.31719
[44]	train-mlogloss:2.31169	validation-mlogloss:2.31036
[45]	train-mlogloss:2.30481	validation-mlogloss:2.30365
[46]	train-mlogloss:2.29788	validation-mlogloss:2.29705
[47]	train-mlogloss:2.29111	validation-mlogloss:2.29015
[48]	train-mlogloss:2.28420	validation-mlogloss:2.28343
[49]	train-mlogloss:2.27785	validation-mlogloss:2.27716
[50]	train-mlogloss:2.27136	validation-mlogloss:2.27077
[51]	train-mlogloss:2.26476	validation-mlogloss:2.26435
[52]	train-mlogloss:2.25842	validation-mlogloss:2.25797
[53]	train-mlogloss:2.25196	validation-mlogloss:2.25173
[54]	train-mlogloss:2.24566	validation-mlogloss:2.24553
[55]	train-mlogloss:2.23973	validation-mlogloss:2.23939
[56]	train-mlogloss:2.23371	validation-mlogloss:2.23332
[57]	train-mlogloss:2.22762	validation-mlogloss:2.22735
[58]	train-mlogloss:2.22153	validation-mlogloss:2.22149
[59]	train-mlogloss:2.21556	validation-mlogloss:2.21557
[60]	train-mlogloss:2.20950	validation-mlogloss:2.20951
[61]	train-mlogloss:2.20364	validation-mlogloss:2.20383
[62]	train-mlogloss:2.19787	validation-mlogloss:2.19842
[63]	train-mlogloss:2.19208	validation-mlogloss:2.19281
[64]	train-mlogloss:2.18642	validation-mlogloss:2.18741
[65]	train-mlogloss:2.18070	validation-mlogloss:2.18180
[66]	train-mlogloss:2.17504	validation-mlogloss:2.17636
[67]	train-mlogloss:2.16943	validation-mlogloss:2.17083
[68]	train-mlogloss:2.16413	validation-mlogloss:2.16556
[69]	train-mlogloss:2.15858	validation-mlogloss:2.16011
[70]	train-mlogloss:2.15320	validation-mlogloss:2.15507
[71]	train-mlogloss:2.14781	validation-mlogloss:2.15012
[72]	train-mlogloss:2.14276	validation-mlogloss:2.14501
[73]	train-mlogloss:2.13762	validation-mlogloss:2.13997
[74]	train-mlogloss:2.13269	validation-mlogloss:2.13512
[75]	train-mlogloss:2.12762	validation-mlogloss:2.13000
[76]	train-mlogloss:2.12262	validation-mlogloss:2.12523
[77]	train-mlogloss:2.11756	validation-mlogloss:2.12018
[78]	train-mlogloss:2.11272	validation-mlogloss:2.11540
[79]	train-mlogloss:2.10782	validation-mlogloss:2.11053
[80]	train-mlogloss:2.10295	validation-mlogloss:2.10583
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[382]	train-mlogloss:1.52387	validation-mlogloss:1.56882
[383]	train-mlogloss:1.52318	validation-mlogloss:1.56833
[384]	train-mlogloss:1.52248	validation-mlogloss:1.56778
[385]	train-mlogloss:1.52177	validation-mlogloss:1.56728
[386]	train-mlogloss:1.52110	validation-mlogloss:1.56685
[387]	train-mlogloss:1.52040	validation-mlogloss:1.56622
[388]	train-mlogloss:1.51973	validation-mlogloss:1.56554
[389]	train-mlogloss:1.51907	validation-mlogloss:1.56502
[390]	train-mlogloss:1.51845	validation-mlogloss:1.56440
[391]	train-mlogloss:1.51785	validation-mlogloss:1.56374
[392]	train-mlogloss:1.51719	validation-mlogloss:1.56317
[393]	train-mlogloss:1.51660	validation-mlogloss:1.56261
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[395]	train-mlogloss:1.51535	validation-mlogloss:1.56155
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[401]	train-mlogloss:1.51142	validation-mlogloss:1.55866
[402]	train-mlogloss:1.51075	validation-mlogloss:1.55813
[403]	train-mlogloss:1.51011	validation-mlogloss:1.55763
[404]	train-mlogloss:1.50952	validation-mlogloss:1.55716
[405]	train-mlogloss:1.50891	validation-mlogloss:1.55656
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[407]	train-mlogloss:1.50769	validation-mlogloss:1.55533
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[469]	train-mlogloss:1.47354	validation-mlogloss:1.52913
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[471]	train-mlogloss:1.47255	validation-mlogloss:1.52844
[472]	train-mlogloss:1.47209	validation-mlogloss:1.52815
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[474]	train-mlogloss:1.47110	validation-mlogloss:1.52747
[475]	train-mlogloss:1.47055	validation-mlogloss:1.52713
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[477]	train-mlogloss:1.46962	validation-mlogloss:1.52640
[478]	train-mlogloss:1.46915	validation-mlogloss:1.52610
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[483]	train-mlogloss:1.46677	validation-mlogloss:1.52438
[484]	train-mlogloss:1.46630	validation-mlogloss:1.52404
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[489]	train-mlogloss:1.46384	validation-mlogloss:1.52213
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[492]	train-mlogloss:1.46252	validation-mlogloss:1.52124
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[501]	train-mlogloss:1.45847	validation-mlogloss:1.51822
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[504]	train-mlogloss:1.45719	validation-mlogloss:1.51726
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[506]	train-mlogloss:1.45627	validation-mlogloss:1.51661
[507]	train-mlogloss:1.45583	validation-mlogloss:1.51638
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[510]	train-mlogloss:1.45451	validation-mlogloss:1.51544
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[513]	train-mlogloss:1.45321	validation-mlogloss:1.51439
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[516]	train-mlogloss:1.45183	validation-mlogloss:1.51355
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[519]	train-mlogloss:1.45063	validation-mlogloss:1.51274
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[522]	train-mlogloss:1.44935	validation-mlogloss:1.51197
[523]	train-mlogloss:1.44896	validation-mlogloss:1.51169
[524]	train-mlogloss:1.44854	validation-mlogloss:1.51136
[525]	train-mlogloss:1.44812	validation-mlogloss:1.51107
[526]	train-mlogloss:1.44771	validation-mlogloss:1.51074
[527]	train-mlogloss:1.44726	validation-mlogloss:1.51050
[528]	train-mlogloss:1.44685	validation-mlogloss:1.51023
[529]	train-mlogloss:1.44644	validation-mlogloss:1.50992
[530]	train-mlogloss:1.44602	validation-mlogloss:1.50962
[531]	train-mlogloss:1.44561	validation-mlogloss:1.50938
[532]	train-mlogloss:1.44522	validation-mlogloss:1.50910
[533]	train-mlogloss:1.44486	validation-mlogloss:1.50880
[534]	train-mlogloss:1.44447	validation-mlogloss:1.50847
[535]	train-mlogloss:1.44411	validation-mlogloss:1.50820
[536]	train-mlogloss:1.44373	validation-mlogloss:1.50790
[537]	train-mlogloss:1.44333	validation-mlogloss:1.50756
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[539]	train-mlogloss:1.44259	validation-mlogloss:1.50707
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[545]	train-mlogloss:1.44022	validation-mlogloss:1.50544
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[548]	train-mlogloss:1.43909	validation-mlogloss:1.50459
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[551]	train-mlogloss:1.43795	validation-mlogloss:1.50369
[552]	train-mlogloss:1.43757	validation-mlogloss:1.50344
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[554]	train-mlogloss:1.43682	validation-mlogloss:1.50310
[555]	train-mlogloss:1.43648	validation-mlogloss:1.50293
[556]	train-mlogloss:1.43613	validation-mlogloss:1.50271
[557]	train-mlogloss:1.43576	validation-mlogloss:1.50250
[558]	train-mlogloss:1.43537	validation-mlogloss:1.50226
[559]	train-mlogloss:1.43503	validation-mlogloss:1.50207
[560]	train-mlogloss:1.43466	validation-mlogloss:1.50183
[561]	train-mlogloss:1.43427	validation-mlogloss:1.50150
[562]	train-mlogloss:1.43393	validation-mlogloss:1.50131
[563]	train-mlogloss:1.43357	validation-mlogloss:1.50117
[564]	train-mlogloss:1.43322	validation-mlogloss:1.50096
[565]	train-mlogloss:1.43288	validation-mlogloss:1.50064
[566]	train-mlogloss:1.43255	validation-mlogloss:1.50037
[567]	train-mlogloss:1.43226	validation-mlogloss:1.50016
[568]	train-mlogloss:1.43189	validation-mlogloss:1.49997
[569]	train-mlogloss:1.43151	validation-mlogloss:1.49977
[570]	train-mlogloss:1.43116	validation-mlogloss:1.49956
[571]	train-mlogloss:1.43082	validation-mlogloss:1.49936
[572]	train-mlogloss:1.43037	validation-mlogloss:1.49918
[573]	train-mlogloss:1.43000	validation-mlogloss:1.49893
[574]	train-mlogloss:1.42961	validation-mlogloss:1.49871
[575]	train-mlogloss:1.42928	validation-mlogloss:1.49855
[576]	train-mlogloss:1.42889	validation-mlogloss:1.49831
[577]	train-mlogloss:1.42855	validation-mlogloss:1.49799
[578]	train-mlogloss:1.42821	validation-mlogloss:1.49768
[579]	train-mlogloss:1.42789	validation-mlogloss:1.49748
[580]	train-mlogloss:1.42755	validation-mlogloss:1.49727
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[589]	train-mlogloss:1.42442	validation-mlogloss:1.49548
[590]	train-mlogloss:1.42405	validation-mlogloss:1.49532
[591]	train-mlogloss:1.42373	validation-mlogloss:1.49507
[592]	train-mlogloss:1.42341	validation-mlogloss:1.49483
[593]	train-mlogloss:1.42312	validation-mlogloss:1.49458
[594]	train-mlogloss:1.42282	validation-mlogloss:1.49431
[595]	train-mlogloss:1.42247	validation-mlogloss:1.49426
[596]	train-mlogloss:1.42207	validation-mlogloss:1.49405
[597]	train-mlogloss:1.42169	validation-mlogloss:1.49376
[598]	train-mlogloss:1.42136	validation-mlogloss:1.49355
[599]	train-mlogloss:1.42102	validation-mlogloss:1.49333
[600]	train-mlogloss:1.42073	validation-mlogloss:1.49319
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[602]	train-mlogloss:1.42013	validation-mlogloss:1.49278
[603]	train-mlogloss:1.41985	validation-mlogloss:1.49261
[604]	train-mlogloss:1.41954	validation-mlogloss:1.49242
[605]	train-mlogloss:1.41924	validation-mlogloss:1.49215
[606]	train-mlogloss:1.41890	validation-mlogloss:1.49199
[607]	train-mlogloss:1.41856	validation-mlogloss:1.49178
[608]	train-mlogloss:1.41828	validation-mlogloss:1.49159
[609]	train-mlogloss:1.41797	validation-mlogloss:1.49130
[610]	train-mlogloss:1.41768	validation-mlogloss:1.49112
[611]	train-mlogloss:1.41735	validation-mlogloss:1.49098
[612]	train-mlogloss:1.41699	validation-mlogloss:1.49097
[613]	train-mlogloss:1.41668	validation-mlogloss:1.49077
[614]	train-mlogloss:1.41641	validation-mlogloss:1.49059
[615]	train-mlogloss:1.41610	validation-mlogloss:1.49035
[616]	train-mlogloss:1.41583	validation-mlogloss:1.49018
[617]	train-mlogloss:1.41553	validation-mlogloss:1.49001
[618]	train-mlogloss:1.41520	validation-mlogloss:1.48973
[619]	train-mlogloss:1.41491	validation-mlogloss:1.48964
[620]	train-mlogloss:1.41463	validation-mlogloss:1.48948
[621]	train-mlogloss:1.41432	validation-mlogloss:1.48931
[622]	train-mlogloss:1.41403	validation-mlogloss:1.48907
[623]	train-mlogloss:1.41375	validation-mlogloss:1.48896
[624]	train-mlogloss:1.41346	validation-mlogloss:1.48882
[625]	train-mlogloss:1.41315	validation-mlogloss:1.48861
[626]	train-mlogloss:1.41289	validation-mlogloss:1.48848
[627]	train-mlogloss:1.41260	validation-mlogloss:1.48843
[628]	train-mlogloss:1.41233	validation-mlogloss:1.48825
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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.05 [%]
Global accuracy score (test) = 43.66 [%]
Global F1 score (train) = 55.91 [%]
Global F1 score (test) = 42.73 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.21      0.22      0.22       184
 CAMINAR CON MÓVIL O LIBRO       0.30      0.38      0.33       184
       CAMINAR USUAL SPEED       0.25      0.14      0.18       184
            CAMINAR ZIGZAG       0.31      0.30      0.31       184
          DE PIE BARRIENDO       0.46      0.48      0.47       184
   DE PIE DOBLANDO TOALLAS       0.44      0.42      0.43       184
    DE PIE MOVIENDO LIBROS       0.38      0.39      0.38       184
          DE PIE USANDO PC       0.53      0.65      0.59       184
        FASE REPOSO CON K5       0.42      0.82      0.56       184
INCREMENTAL CICLOERGOMETRO       0.90      0.60      0.72       184
           SENTADO LEYENDO       0.28      0.24      0.26       184
         SENTADO USANDO PC       0.18      0.07      0.10       184
      SENTADO VIENDO LA TV       0.38      0.34      0.36       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.73      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 28: 43.66 [%]
F1-score capturado en la ejecución 28: 42.73 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 55.86 [%]
Global accuracy score (test) = 44.5 [%]
Global F1 score (train) = 55.7 [%]
Global F1 score (test) = 43.48 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.23      0.24      0.24       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.41      0.36       184
       CAMINAR USUAL SPEED       0.27      0.13      0.18       184
            CAMINAR ZIGZAG       0.30      0.28      0.29       184
          DE PIE BARRIENDO       0.48      0.51      0.49       184
   DE PIE DOBLANDO TOALLAS       0.46      0.45      0.45       184
    DE PIE MOVIENDO LIBROS       0.39      0.39      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.43      0.82      0.56       184
INCREMENTAL CICLOERGOMETRO       0.92      0.60      0.73       184
           SENTADO LEYENDO       0.29      0.24      0.26       184
         SENTADO USANDO PC       0.17      0.07      0.09       184
      SENTADO VIENDO LA TV       0.40      0.37      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.74      0.64       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.45      2737
                 macro avg       0.44      0.45      0.43      2737
              weighted avg       0.44      0.45      0.43      2737


Accuracy capturado en la ejecución 29: 44.5 [%]
F1-score capturado en la ejecución 29: 43.48 [%]

=== 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.
(2737, 42)
(18331, 42)
[[ 2.]
 [ 2.]
 [ 2.]
 ...
 [13.]
 [13.]
 [13.]]
(2737, 1)
[ 3 13 13 ... 13 13  1]
(2737,)
-------------------------------------------------

Global accuracy score (train) = 56.07 [%]
Global accuracy score (test) = 43.7 [%]
Global F1 score (train) = 55.9 [%]
Global F1 score (test) = 42.72 [%]
                            precision    recall  f1-score   support

     CAMINAR CON LA COMPRA       0.22      0.20      0.21       184
 CAMINAR CON MÓVIL O LIBRO       0.31      0.41      0.35       184
       CAMINAR USUAL SPEED       0.25      0.13      0.17       184
            CAMINAR ZIGZAG       0.28      0.30      0.29       184
          DE PIE BARRIENDO       0.46      0.51      0.49       184
   DE PIE DOBLANDO TOALLAS       0.44      0.42      0.43       184
    DE PIE MOVIENDO LIBROS       0.40      0.38      0.39       184
          DE PIE USANDO PC       0.53      0.65      0.58       184
        FASE REPOSO CON K5       0.42      0.82      0.55       184
INCREMENTAL CICLOERGOMETRO       0.92      0.59      0.72       184
           SENTADO LEYENDO       0.26      0.22      0.24       184
         SENTADO USANDO PC       0.18      0.07      0.10       184
      SENTADO VIENDO LA TV       0.39      0.37      0.38       184
   SUBIR Y BAJAR ESCALERAS       0.56      0.71      0.63       184
                    TROTAR       0.94      0.82      0.88       161

                  accuracy                           0.44      2737
                 macro avg       0.44      0.44      0.43      2737
              weighted avg       0.43      0.44      0.42      2737


Accuracy capturado en la ejecución 30: 43.7 [%]
F1-score capturado en la ejecución 30: 42.72 [%]

=== RESUMEN FINAL ===
Accuracies: [43.66, 44.32, 43.77, 43.55, 44.36, 43.73, 43.55, 43.66, 43.15, 43.33, 43.44, 43.77, 43.84, 43.77, 43.33, 43.84, 43.44, 43.62, 43.73, 43.77, 43.55, 43.59, 43.41, 44.06, 43.15, 43.59, 43.41, 43.66, 44.5, 43.7]
F1-scores: [42.74, 43.35, 42.72, 42.47, 43.37, 42.79, 42.59, 42.58, 42.21, 42.33, 42.46, 42.88, 42.87, 42.82, 42.31, 42.78, 42.46, 42.62, 42.74, 42.76, 42.58, 42.59, 42.45, 43.11, 42.12, 42.56, 42.26, 42.73, 43.48, 42.72]
Accuracy mean: 43.6750 | std: 0.3118
F1 mean: 42.6817 | std: 0.3217

Resultados guardados en /mnt/nvme1n2/git/uniovi-simur-wearablepermed-data/output/Paper_results/cases_dataset_C/case_C_XGB_acc_17_classes/metrics_test.npz
